Mapping the Distribution of Grasslands Grazed by ...Figure 2 Map of southeast China showing the...

Mapping flood recessional grasslands grazed by overwintering geese: an application of

multi-temporal remote sensing

Si Yali March 2006

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Mapping flood recessional grasslands grazed by overwintering geese: an application of

multi-temporal remote sensing

by

Si Yali

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation (Natural Resource Management)

Degree Assessment Board Chairman: Prof. Dr. Ir. Alfred de Gier (ITC) External examiner: Associate Prof. Dr. Liu Xuehua (Tsinghua University) Primary supervisor: Associate Prof. Dr. Jan de Leeuw (ITC) Secondary supervisor: Prof. Dr. Li Lin (Wuhan University) Internal examiner: Dr. Michael Weir (ITC) Internal examiner: Prof. Dr. Liu Yaolin (Wuhan University)

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

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Disclaimer

This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

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To my parents …

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Acknowledgements

It is such a special and hard phase in my life for studying both in the Netherlands and China. My life becomes so enjoyable and shining because so many people support, encourage and care about me a lot. For those times I’ve missed saying “Thank you”, I want to thank you now for being. I wish to express my everlasting gratitude to my parents first. I even have no opportunity to start my study without your physical and mental support. Thank you for being always stand by my side and enduring years of neglect during the process of my study. I want to give my sincere thanks to my schools: International Institute for Geo-Information Science and Earth Observation (ITC) and Wuhan University. Thank you for providing such an excellent environment for me to carry out my research. My deepest appreciation goes to my primary supervisor Dr. Jan de Leeuw. Thank you for the sincerity and seriousness in science you inspire. Thank you for the lessons in both study and life that I have learned from you. Thank you for being such a beautiful example of an earnest, patient and caring supervisor. Thank you for your pages of comments, advices and suggestions every time. You make me fall in love with science and wish to continue my study career. Many thanks go to my second supervisor Prof. Dr Li Lin. Thank you for your persistent concern and support to my research and me. I enjoyed each discussion and appointment with you. To my supervisors, I know I’ve said it before, but it is worthy of repetition, I want to thank you for everything you do for me. It is my first experience to study far from my own country, struggling both in my life and my research. I appreciate that Prof. Dr. Liu Yanfang and Dr. Ai Tinghua came by and supervised me during the proposal period. I want to thank Dr. Michael Weir and Dr. David Rossiter for their precious and critical advices. I feel so lucky to make so many kind-hearted friends in ITC. Mr. Chudamani Joshi, I am so touched that you even came to my cluster to assist me when I met problems in my research. Mr. Pravesh Debba, thank you for always helping me dealing with my statistic problems. Ms. Nicky Knox, I really appreciate your nice and concern for sending me useful literatures. Mr. Kim Jh, I would never forget you stayed up for a whole night accompanying with me for discussing my research. Words are never enough to express my thankfulness to people who care about me a lot during my research. I feel so honored to acquire so many valuable advices and suggestions about geese study from Prof. Dr. Herbert Prins and Ms. Heuermann Nicol in Wageningen University. Thank you for understanding me as a beginner in geese study and providing me self-giving support. I learned a lot from Mr. Lei Gang (WWF-China) about geese. Thank you for being so friendly and generous and providing me geese observation data. I would always remember those days I spent in Poyang Lake National Nature Reserve for my field data collection. I want to thank Mr. Ji Weitao for arranging the whole logistic things. My most sincere appreciation goes to the staff in Dahu Lake protecting station: Mr. Zhou Feilong, Mr.Yi Wusheng and Mr. Ai Bing. Thank you for accompanying with me walk more than 10 kilometers per day in the floodplain and never care about the risk of schistosomiasis and bird flue. I want to express my deepest

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sensation to people who work in the remote area under tough conditions enthusiasm for environmental and wildlife protection. I want to express my appreciation to Prof. Liu Renlin in Jiangxi Agricultural University. Thank you for assisting me in identifying grass species in Poyang Lake. Many thanks go to Prof. Liu Liangmin and Mr. Yu Fan in Wuhan University MODIS receiving station, Ms. Chen Wenbo, Ms. Lin Xin and Mr. Wang Zhengxing in Institute of Geographical Science and Natural Resource Research, CAS. Thank you for being so passional to assist me in the satellite imagery collection. The most special thankfulness goes to PhD candidates Mr. Wang Tiejun, Mr. Wu Guofeng and Mr. Hu Yong. Thank you for all of the thoughtful things you do to let me move on in my research. Thank you for standing out to assist me every time I need, not out of obligation, but out of the goodness of your heart and in the spirit of care and concern for my success. Your goodness burned in my mind and I will cherish it forever.

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Table of Contents

1. Introduction .......................................................................................................................1

1.1. Background .......................................................................................................................1 1.2. Research Hypothesis .........................................................................................................3 1.3. General Objective..............................................................................................................4 1.4. Research Questions ...........................................................................................................4 1.5. Organization of the Thesis.................................................................................................4 1.6. Research Flow Chart .........................................................................................................5

2. Materials and Methods .....................................................................................................6

2.1. Study Area.........................................................................................................................6 2.1.1. Location .....................................................................................................................6 2.1.2. Climate and Hydrology..............................................................................................7

2.2. Research Materials ............................................................................................................8 2.2.1. Field Data...................................................................................................................8 2.2.2. Satellite Image ...........................................................................................................8 2.2.3. Accessorial Data ........................................................................................................9

2.3. Research Methods ...........................................................................................................10 2.3.1. Field Data Collection ...............................................................................................10 2.3.2. Accessorial Data Pre-processing..............................................................................10 2.3.3. Field Data Analysis (TM) ........................................................................................11 2.3.4. TM Imagery Pre-processing and Analysing ............................................................11 2.3.5. MODIS Imagery Pre-processing and Analysing .....................................................12 2.3.6. Classification Methods.............................................................................................15

3. Results...............................................................................................................................17

3.1. Local Scale Grasslands Mapping using TM....................................................................17 3.1.1. Statistic Analysis......................................................................................................17 3.1.2. Unsupervised Classification.....................................................................................18 3.1.3. Training and Validating Samples Design ................................................................19 3.1.4. Maximum Likelihood Classification .......................................................................19 3.1.5. Accuracy Assessment ..............................................................................................20 3.1.6. Dominant Plant Species Analysis ............................................................................21 3.1.7. Summary..................................................................................................................22

3.2. Regional Scale Grasslands Mapping using MODIS........................................................23 3.2.1. Principle Components Analysis...............................................................................23 3.2.2. Perform Unsupervised Classification based on NDVI-PCA and EVI-PCA............24 3.2.3. Time-series Spectral Indices Analysis .....................................................................27 3.2.4. Hybrid Hierarchical Classification ..........................................................................29 3.2.5. Validating Samples Preparation...............................................................................30 3.2.6. Accuracy Assessment ..............................................................................................30

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3.2.7. Summary..................................................................................................................32 3.3. Relationship Analysis between Grasslands and Geese....................................................33

3.3.1. Relationship Analysis among Temperature, Grasses and Geese Lingering time ....34 3.3.2. Relationship Analysis between the grass heights and geese....................................35 3.3.3. Statistic Analysis between Geese Distribution and Grasslands Distribution...........36 3.3.4. Summary..................................................................................................................38

4. Discussion and Conclusions ............................................................................................39

4.1. Discussion .......................................................................................................................39 4.1.1. MODIS and Flood Recessional grasslands Detection .............................................39 4.1.2. Photosynthetic Pathways of Early and Late Up-greening Grasses: C3 or C4?........40 4.1.3. Geese and Flood Recessional Grasslands ................................................................41

4.2. Conclusions .....................................................................................................................42 References................................................................................................................................43

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List of Figures

Figure 1 Overall Framework of the research....................................................................................5

Figure 2 Map of southeast China showing the Yangtze river Basin (Top), map of the regional

study area derived from MODIS imagery showing the middle and lower part of the Yangtze

with its associated lakes: Poyang and Dongting Lake (Bottom left) and Landsat TM false

colour composite of the local study area showing the north western part of Poyang lake

(bottom right) ...........................................................................................................................6

Figure 3 Graphs showing environmental conditions in Poyang Lake in 2004. Top left: mean

monthly water level (m); Top right: Mean monthly temperature (ºC); Low left: mean

monthly moisture (%); Low right: mean monthly precipitation (mm).....................................7

Figure 4 Map of Poyang lake Nature Reserve showing elevation above sea level (m) and

distributions of the field sample sites, for the local (Landsat TM) and regional (MODIS)

study .........................................................................................................................................8

Figure 5 Flow chart of spectral enhancement on TM image..........................................................12

Figure 6 Flow chart of the hybrid hierarchical classification.........................................................16

Figure 7 Relation between elevation (DEM) and the cover (proportion of area covered) of late

and early up-greening grassland species ................................................................................17

Figure 8 Distribution of flood recessional grasslands map in Poyang Lake Nature Reserve

predicted using the Landsat TM image and an unsupervised classification...........................19

Figure 9 Distribution of flood recessional grasslands map in Poyang Lake Nature Reserve

predicted using the Landsat TM image and a maximum likelihood classifier .......................20

Figure 10 Number of pixels per land cover class according to the unsupervised and maximum

likelihood classifier ................................................................................................................22

Figure 11 Distribution of flood recessional grasslands along Yangtze based on NDVI-PCA.......25

Figure 12 Distribution of flood recessional grasslands along Yangtze based on EVI-PCA ..........25

Figure 13 Distribution of flood recessional grasslands in Poyang Lake based on NDVI-PCA .....26

Figure 14 Distribution of flood recessional grasslands in Poyang Lake based on EVI-PCA ........26

Figure 15 Time series derived from MODIS 2004 data of NDVI, EVI and LSWI for late

up-greening grasslands (LUG), early up-greening grasslands (EUG), as well as irrigated

agricultural ecosystems (Paddy rice) and other terrestrial vegetation (herbs)........................27

Figure 16 Time series derived from MODIS 2004 data of NDVI, EVI, and LSWI for permanent

water ecosystems....................................................................................................................28

Figure 17 Distribution of flood recessional grasslands along the Yangtze according to the hybrid

hierarchical classification .......................................................................................................29

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Figure 18 Distribution of flood recessional grasslands in Poyang Lake Nature Reserve according

the hybrid hierarchical classification......................................................................................30

Figure 19 Graph summarizing the accuracy of different methods at the regional scale ................32

Figure 20. Distribution and migratory flyways of the Lesser white-fronted goose (Anser

erythropus) (UNEP, 2005) .....................................................................................................33

Figure 21 Map showing mean monthly temperature in January. The red circle indicates the

regional study area..................................................................................................................34

Figure 22 Relationship among temperature, late up-greening grasslands and geese lingering time

................................................................................................................................................34

Figure 23 Box plots showing variation in sward height in three grassland communities in Poyang

Lake Nature Reserve ..............................................................................................................35

Figure 24 Observed distribution of greater white-fronted goose in relation to the distribution of

early and late up-greening grasslands according the hybrid hierarchical classification in

Poyang Lake Nature Reserve .................................................................................................36

Figure 25 Distribution of geese recorded by WWF (2004) in relation to the distribution of

grasslands according the hybrid hierarchical classification ...................................................37

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List of Tables

Table 1 Statistics describing various regression models between late up-greening grasslands

distribution and elevation .......................................................................................................18

Table 2 Statistics describing various regression models between early up-greening grasslands

distribution and elevation .......................................................................................................18

Table 3 Land covers remerging of UC based on spectral enhanced TM image.............................18

Table 4 Training and validating samples for maximum likelihood classification..........................19

Table 5 Error matrix for four land cover classes based on unsupervised classification.................20

Table 6 Accuracy assessment for the data presented in table 5......................................................20

Table 7 Error matrix for four land cover classes based on maximum likelihood classification ....21

Table 8 Accuracy assessment for the data presented in table 7......................................................21

Table 9 Matrix showing the eigenstructure of eight principal components against eight MODIS

NDVI images..........................................................................................................................23

Table 10 Matrix showing the eigenstructure of eight principal components against eight MODIS

EVI images .............................................................................................................................23

Table 11 Land cover merging of unsupervised classification based on NDVI-PCA .....................24

Table 12 Land cover merging of unsupervised classification based on EVI-PCA ........................24

Table 13 Error matrix for four land cover classes based on NDVI-PCA.......................................30

Table 14 Accuracy assessment for the data presented in table 13..................................................31

Table 15 Error matrix for four land cover classes based on EVI-PCA ..........................................31


Table 17 Error matrix for four land cover classes based on hybrid hierarchical classification......31


Table 19 Distribution of greater white-fronted geese related to land cover in Poyang Lake NR ..36

Table 20 Probability ratio of the geese presence in each land cover..............................................37

Table 21 Probability ratio of the greater white-fronted goose presence in each land cover ..........38

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Abbreviations and Acronyms

TM Thematic Mapper

MODIS Moderate Resolution Imaging Spectroradiometer

DEM Digital Elevation Model

PCA Principle Components Analysis

TCT Tasselled Cap Transformation

UC Unsupervised Classification

MLC Maximnm Likelihood Classification

HHC Hybird Hierarchical Classification

GCP Ground control point

AVHRR Advanced Very High Resolution Radiometer

NOAA National Oceanic and Atmospheric Administration

LUG Late Up-greening Grasslands

EUG Early Up-greening Grasslands

NDVI Normalized Difference Vegetation Index

EVI Enhanced Vegetation Index

LSWI Land Surface Water Index

SBI Soil Brightness Index

GVI Greenness Vegetation Index

PP Probability presence

PA Probability absence

PPR Probability presence ratio

WWF World Wide Fund For Nature

NR Nature Reserve

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Abstract

Significant numbers of geese migrate in East Asia as far south as the Yangtze seeking to forage on young and high quality grasses. This resource is difficult to find in autumn and winter in areas along the Yangtze, where lack of rainfall in autumn and winter causes the vegetation to die off. Late up-greening grass develops in flood recessional grasslands along the Yangtze in autumn. Geese overwinter in this biotope, attracted by this young and high quality forage in cool seasons. Recently more insight became available on the distribution of these geese species, followed winter bird counts along the whole of the Yangtze organized by WWF-China in January 2004 and January 2005. Information of flood recessional grasslands is needed for further resource management and geese protection. Two kinds of grasslands with distinct phenology produce in the flood recessional grasslands. Late up-greening grasslands (Carex spp.) distribute in the lower part of the floodplain are dominant plant species compared to early up-greening grasslands (Miscanthus sacchriflorus Benth and Cynodon daxtylon). In this study we explored the possibility using time series of nine MODIS images of 2004 to localize the distribution of late and early up-greening flood recessional grasslands along the Yangtze River. Two approaches based on three spectral indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) derived from MODIS images were executed in detecting these grasslands. We firstly carried out unsupervised classification based on principle components of time series vegetation indices (NDVI_PCA and EVI_PCA); then performed hybrid hierarchical classification based on comparison of NDVI, EVI and LSWI patterns among different land cover types. These late and early up-greening grasslands maps were assessed, by using the grasslands map derived from Landsat TM image (NOV. 2004). Excellent results were obtained from all these methods though hybrid hierarchical classifier showed the best result and unsupervised classifier based on EVI_PCA performed better than that of NDVI_PCA. The distribution of geese recorded by geese droppings, field spotting and WWF winter bird counts matched perfectly with the flood recessional grasslands map. Geese especially greater white-fronted geese preferentially utilized late up-greening grasslands because these grasslands produced young high quality grass and proper sward heights for them. Keywords: Flood recessional grasslands; Late up-greening grasslands; Early up-greening grasslands; Spectral indices; PCA; MODIS; Phenology; Geese

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MAPPING THE DISTRIBUTION OF GRASSLANDS GRAZED BY OVERWINTERING GEESE USING MULTI–TEMPORAL REMOTE SENSING

1. Introduction

1.1. Background

Geese migrate from Siberia through the north of China to winter in the middle and lower Yangtze basin in search of suitable grazing lands (Andreev, 1997; Lei, 2000; Markkola, 2000; Syroechkovski, 2000; UNEP, 2005). The wetlands in the middle and lower Yangtze basin are important for over wintering waterfowl (Scott, 1989). There are five goose species wintering along the Yangtze River. They are swan goose (Anser cygnoide), bean goose (Anser fabalis), greater white-fronted goose (Anser albifrons), lesser white-fronted goose (Anser erythropus) and grey-leg goose (Anser anser). According to the water bird survey of the middle and lower Yangtze River basin in late January and early February 2004, the population of these geese was 60886, 79758, 25241, 16937 and 890 respectively (Mark, Chen, Cao, & Lei, 2004). Swan goose and lesser white-fronted goose are globally endangered species. Significant proportions of the global populations overwinter along the Yangtze (BirdLife, 2003; Mark et al., 2004). The other three species are not threatened but protected at national (greater white-fronted goose, second-grade state protection wildlife) or local level (grey-leg goose and bean goose, locally protected wildlife). Why do geese migrate to the south? In Western Europe geese overwinter in the lowlands at the southern shores of the North Sea, at latitudes above 50ºN. It is thought that geese migrate just as far south as where they find grasslands that continue growth during winter. The growth of grasses stops below 4ºC and geese in Europe overwinter close to this thermocline. The Yangtze is located at 30ºN or below and East Asian geese thus overwinter more than 20ºN further to the south. We hypothesize that geese presumably migrate further to the south because of the more continental climate in East Asia, in search grasslands that continue growing actively over winter. What type of forage do geese select? Geese are herbivores foraging on the leaves and rhizomes of grasses and sedges (J. Zhang & Lu, 1999). They prefer easily digestible plant tissue low in fiber and high in nitrogen and/or carbohydrate content (Owens, 1997; Juliet A Vickery & Gill, 1999). Such high quality is common to young tissue, but quality rapidly deteriorates when grasses grow taller and older. It also has frequently been observed that geese select vegetation of specific height (Allport, 1989; Juliet A Vickery & Gill, 1999; J.A. Vickery, Sutherland, O.Brien, Watkinson, & Yallop1, 1997). Shorter vegetation is avoided while the foraging efficiency goes down with reduced bite size, while tall vegetation is avoided because of relatively poor quality (R. Riddington, M. Hassall, & S. J. Lane, 1997; Summers & Critchley, 1990). Geese arrive along the Yangtze in autumn. Where along the Yangtze would geese find such actively growing vegetation with the described properties? It is surely not in terrestrial plant communities. These start their growth with the onset of the rains in May. The rains stop by September and temperatures drop in October, leading to senescence of most of the terrestrial vegetation in autumn. An

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exception is formed by tree species most of which are evergreen. This resource is however not edible to geese. Where then do geese forage from autumn to early spring? The answer to this question is related to the seasonal pattern of the hydrology of the Yangtze. Water levels are high in summer due to the monsoon rains and decrease in autumn. During the flood period, extensive areas in the floodplains and lakes along the Yangtze River are flooded. In contrast, large areas in the floodplains become exposed in autumn upon recession of the floods. Grasslands develop in these areas in autumn and wintertime. It is this particular flood recessional ecosystem, which provides the actively growing high quality tissue sought for by the geese. Various authors who described these grasslands (Cai, Ma, Zhu, & Yang, 1997; Wu & Ji, 2002) noted that there is a gradient in the vegetation from high to lower elevations. The duration of the flooding and emergence of the vegetation will depend on elevation. While vegetation at higher elevations may remain emergent and continue to grow over summer it may submerge and die off at lower elevations. Consequently, these grasslands will have a phenology that changes with elevation. Where grasslands at higher elevation may remain green for the whole summer, these at lower elations might die off during summer and become flooded by open water for a shorter or longer period. When dying off due to flooding, those grasslands located at higher elevations will green up earlier than those at lower elevation. Therefore, during the wintertime, late up-greening grasses usually produce young leaves. Because of this, we hypothesize that the flood recessional grasslands are dominated by late up-greening species, which possibly could be more attractive to the geese. Multi-temporal remote sensing offers the possibility to monitor phenological patterns of vegetation and remote sensing derived phonological information has been used to classify and map plant communities differing in phenology (Davidson & Csillag, 2003; L.L. Tieszen, Reed, Bliss, Wylie, & DeJong, 1997). We thus suggest that it would be possible to differentiate grassland communities based on their phenology, and for instance distinguish late up-greening grasslands (LUG) from early up-greening grasslands (EUG). The surveys reported by WWF were not very explicit in their description of the geese foraging habitat. Also, the exact location of the grasslands grazed by overwintering geese remains poorly explored. WWF thus called for mapping the habitat of these geese species. The middle and lower stretches of the Yangtze has been intensively mapped for economical, political, social and ecological purposes. However, no attempt has so far been made to localize the grasslands attracting the overwintering geese. We suggest that multi-temporal satellite imagery might allow distinguishing the phenology of the vegetation and enabling to detect the flood recessional grasslands, which we presume attract geese to the basin of the Yangtze. Optical satellite remote sensing provides a viable means to successively monitor grasslands temporally and spatially. Time trajectory indices and vegetation indices derived from The National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery (1 km spatial resolution) had been used to explore different phenological grasslands (Davidson & Csillag, 2003; L.L. Tieszen et al., 1997). However, these

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researches only referred to the terrestrial grasslands. The grasslands in the flood recessional ecosystem were seldom explored. The Moderate Resolution Imaging Spectroradiometer (MODIS) is a high signal-to-noise instrument designed to satisfy a diverse set of oceanographic, terrestrial, and atmospheric science observational requirements. The advantages of MODIS sensor include: (1) High spectral resolution: 36 spectral bands cover the visible and infrared spectrum, from 0.415 to 14.235μm, Narrower bandwidth; (2) High spatial resolution (for regional study): 2 bands with 250m, 5 bands with 500m and 29 bands with 1000m; (3) High radiometric resolution: 12bit, compared with 6bit (e.g. MSS), 8bit (e.g. TM, SPOT), 10bit (e.g. AVHRR); (4) Daily receiving. MODIS had been successfully utilized to predict drought, detect farmland and monitor vegetation phenology (Liu, 2004; Toshihiro Sakamoto. et al., 2005; Xiangming Xiao et al., 2005; X. Zhang et al., 2003). Landsat TM imagery has relatively high spatial resolution (30m). It was extensively used in vegetation indices analysis, farmland detection, vegetation monitoring and vegetation biomass calculation (Dengsheng Lu, Emilio Morana, & Mateus Batistella, 2003; Eric C. Brown de Colstoun et al., 2003; Wagtendonk & Root, 2003; X. Xiao et al., 2002a; Y.Oguro., Y.Suga., S.Takeuchi., H.Ogawa., & K.Tsuchiya., 2003; Zheng et al., 2004). However, one scene of TM image can only cover local areas with 16-day repeat cycle; and it is difficult to acquire cloud free and time-series images. In this research, TM imagery was used to analyze grasslands characters and validate MOIDS imagery. In this thesis, we aim to map flood recessional grasslands along the middle and lower Yangtze River. We firstly investigated the character of flood recessional grasslands in Poyang Lake NR by Landsat TM imagery. We then studied the potential of the MODIS imagery to distinguish the late and early up-greening flood recessional grasslands, and compared the accuracy of two image-processing techniques, an unsupervised classification based on time-series vegetation indices and a hybrid hierarchical classification, by using grasslands derived in TM image. Finally we analyzed the relationship between the spatial distribution of geese and the flood recessional grasslands.

1.2. Research Hypothesis

The date of up greening of grassland changes along the elevation gradient. Late up-greening

grasslands dominate lower elevation flood recessional grasslands. Phenological patterns derived from multi-temporal MODIS imagery allows to establish the date

of up greening and discrimination of late and early up-greening grasslands well. The arrival of geese from their breeding grounds coincides with the green time of late up-greening

grasslands. Geese selectively utilize and forage on late up-greening flood recessional grasslands.

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1.3. General Objective

Study the potential of MODIS imagery in mapping the up greening of flood recessional grasslands and demonstrate that geese preferentially use late up-greening grasslands.

1.4. Research Questions

What is the relationship between elevation and the date of grasslands up greening? Were these grasslands dominant by late up-greening grasses? Could the multi-temporal vegetation indices derived from MODIS imagery be used to map the

distribution of flood recessional grasslands? Could the phenology of grasses derived from MODIS imagery be used to predict the distribution

of the flood recessional grasslands? What is the relationship among the temperature, grasses growth and geese lingering time? What is the relationship between geese distribution and grasses heights? How does geese distribution related to the flood recessional grasslands distribution?

1.5. Organization of the Thesis

The thesis is organized in the following four chapters. The first chapter introduced the scientific background of this study. It explained why we carry out this research and places the research question in a wider scientific perspective. It includes research hypothesis, objective and questions. The second chapter describes the methods and materials. It included a description of the study area, the data collection, pre-processing and analysing methods. The principle components analysis and time-series spectral indices analysis were introduced in this part. The classification methods used in this research were also briefly described here. They are unsupervised classification (UC), maximum likelihood classification (MLC) and hybrid hierarchical classification (HHC). The research results were listed in the third chapter. It showed the maps of the flood recessional grasslands distribution in both local study area (Poyang Lake NR) and regional study area (the middle and lower Yangtze Basin). The areas of late and early up-greening grasses in Poyang Lake NR illustrated the dominant grass in the flood recessional grasslands. The geese distribution map related grasslands map showed the relationship between the geese and the flood recessional grasslands. The fourth chapter discussed the results and drew conclusions. The topics in the conclusions section went back to the research questions introduced in the first chapter.

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1.6. Research Flow Chart

TM Image

Enhanced TM

Field Data

Statistic Analysis Spectral Enhancement

UC

MLC

Grasslands Maps

Spectral Indices Data

Field Data

HHC

Index Layers PCA

UC

Grasslands Maps

Accuracy Assessme

MODIS Final Grasslands Map

Geese Data

Statistic Analysis

Geese Grazing Map

Validating

Indices Calculation

MODIS Images

DEM Image

TM Final Grasslands Map

Grass Heights

Figure 1 Overall Framework of the research

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2. Materials and Methods

2.1. Study Area

2.1.1. Location

The research was executed in both regional and local level. The regional study area (for MODIS) is located in the middle and lower reaches of the Yangtze River floodplain including Poyang and Dongting Lake. It is located in southeast of China (28° 12′- 31° 28′ N, 112° 18′- 117° 10′ E). The local study area (for TM) is within the Poyang Lake NR (29° 05′ - 29° 18′ N, 115° 53′- 116° 10′ E).

Figure 2 Map of southeast China showing the Yangtze river Basin (Top), map of the regional study area derived from MODIS imagery showing the middle and lower part of the Yangtze with its associated lakes: Poyang and Dongting Lake (Bottom left) and Landsat TM false colour composite of the local study area showing the north western part of Poyang lake (bottom right)

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2.1.2. Climate and Hydrology

Temperatures vary greatly according to latitude and monsoon activities. During the wintertime in China, an isotherm of zero degrees traverses the Huaihe River - Qinling Mountain - southeast Qinghai - Tibet Plateau. There are five temperature zones and a plateau-climate zone in China: Cool temperate zone, Middle temperate zone, Warm temperate zone, Subtropical zone, Tropical zone and plateau-climate zone. The middle and lower reaches of the Yangtze River is located in the North subtropical zone. The climate in subtropical zone is warm and humid with plentiful rainfall.

Figure 3 Graphs showing environmental conditions in Poyang Lake in 2004. Top left: mean monthly water level (m); Top right: Mean monthly temperature (ºC); Low left: mean monthly moisture (%); Low right: mean monthly precipitation (mm) From the meteorological data collected from Poyang Lake NR, we obtained some detail information about the temperature, moisture and precipitation in 2004 in Poyang Lake NR. The temperature in Poyang Lake NR was cool in winter and warm in summer. The maximum and minimum temperatures were 29ºC in July and 5ºC in January. It provided a condition for vegetation to grow all the year round. The moisture curve was in a year-round high level with tiny fluctuations. The moisture percentages were wandering around 70%. The relatively high precipitations in 2004 occurred in May (12mm) and August (6.5mm). There was no precipitation in January and October, and the precipitations in other months were around 2.5mm. From the water level graph in figure 3, we can see that the lakes in Poyang Lake NR experienced a seasonal flooding. The water level was low in winter and early spring;

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it rose sharply in the late spring and peaked in the summer; then it started to fall in autumn and retained a low level in the whole wintertime.

2.2. Research Materials

2.2.1. Field Data

¦

Figure 4 Map of Poyang lake Nature Reserve showing elevation above sea level (m) and distributions of the field sample sites, for the local (Landsat TM) and regional (MODIS) study The distributions of the field samples were showed in figure 4. The dark blue points were collected for TM classification; where the light blue points were collected for MODIS classification. The numbers of the sample were 208 and 52 respectively. The collection methods would be described later.

2.2.2. Satellite Image

Landsat 5 TM Image on 29th November in 2004 was selected for this research. The quality of the image was high because it was acquired in an unclouded day. The image included 7 bands with the resolution of 30*30m (except band 6). The spectral range includes visible blue (0.45-0.52μm), visible green (0.52-0.60μm), visible red (0.63-0.69μm), near infrared (0.76-.090μm), short wave infrared (0.55-0.75μm), thermal infrared (10.40-12.50μm) and short wave infrared (2.08-2.35μm) (Campbell, 1996; Inforterra). The reason why we chose this data was because large area of flood recessional grasslands showed up in the late winter. The end of November was also coincident with the field survey time. The image was resampled to 20*20m for further analysis.

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The MODIS imagery was obtained from MODIS receiving centre in Wuhan University and Institute of Geographical Science and Natural Resource Research, CAS. 8 daily MODIS Images were available for this research. The dates were: 2005-03-06, 2005-04-21, 2004-07-23, 2004-08-08, 2004-09-16, 2004-10-04, 2004-11-07 and 2004-12-05. The MODIS sensor has 36 spectral bands. We selected 7 bands designed for terrestrial analysis in this research. They are visible red (0.620-0.670μm), near infrared 1 (0.841-0.876μm), visible blue (0.459-0.479μm), visible green (0.545-0.565μm), near infrared 2 (1.230-1.250μm), short wave infrared 1 (1.628-1.652μm) and short wave infrared 2 (2.105-2.155μm) (Liu, 2004). The image resolution is 250*250m (band 1, 2) and 500*500m (band 3-7). The bands 3-7 were resampled to 250*250m for further analysis. The digital number was also transformed into reflectance value for spectral indices calculation. The reasons why we chose this imagery are: First, finer spatial resolution (250m compared with 500m and 1000m); Second, shorter temporal resolution (daily compared with 8-days and 10-days).

2.2.3. Accessorial Data

Eight kinds of accessorial data were used in my research. 1) DEM: The digital elevation model was made in 1998. The vertical accuracy is 0.1m. And the

position accuracy is 20*20 m. The DEM covers the whole Poyang Lake NR (includes part of the big Poyang Lake). It was used to analyse the filed data and assist image classification in TM imagery.

2) Topographic map of the Poyang Lake NR (1:10000): The topographic map was made in 1997. It was used to do geometric correction in TM imagery.

3) Environmental data: The daily temperature and water level data in 2004 were acquired from the Administration of Poyang Lake NR. They were used to provide background information and analyse the relationship between the grasslands and environmental indicators.

4) Botanical data was acquired from local experts in Poyang Lake NR and Jiangxi Agriculture University. They were used to identify the grasses species in the field.

5) Vegetation map of the Yangtze River (1:5000, 000): the vegetation map used to assist MODIS classification was gained from the book <Atlas of the Yangtze River Basin> (Lin, Li, Yang, Wei, & Hong, 1999).

6) Wetland vegetation map and the typical wetland sedimentary physiognomy ichnography of the Poyang Lake NR: they were all obtained from the book <Three gorges, wetland along the Yangtze River and pickled land in estuary > (Cai et al., 1997). These two maps were used to assist TM classification.

7) Geese observation data: the data was acquired from World Wide Fund for Nature (WWF). The waterfowl observation activity was organized in Jan. - Feb. 2004. 14 teams conducted the survey. It covered six provinces included Anhui, Hubei, Hunan, Jiangsu, Jiangxi and Shanghai. The observation sites coordinates, bird species and population were recorded during the observation. This data was used to analyse the relationship between the grasses and geese.

8) Greater white-fronted geese sites in Poyang Lake NR: The data was acquired from the Dahu Lake preserving station. 10 geese sites were gained from the sketch map drew by staff in birds survey in Nov.2005. These samples were used as field data together with geese dropping samples to analyse the relationship between the grasses and geese.

9


2.3. Research Methods

2.3.1. Field Data Collection

The dominant early and late up-greening grasses survey was taken before the field data collection. The results were gained based on Botanical data (Cai et al., 1997; Watson & Dallwitz., 1992) and pre-knowledge about the study area (Cai et al., 1997; Wu & Ji, 2002). Three Carex species (cyperaceae) are the dominant late up-greening grasses in the flood recessional grasslands: they are Carex cinerascens, Carex argyi and Carex unisexualis. There are also two dominant early up-greening species growing in there: Miscanthus sacchriflorus Benth and Cynodon daxtylon. Before go to the fieldwork, we carried out sample design based on the TM image data. The grasslands were obtained by visual interpretation using bands 4, 3 and 2 false colour composites based on TM image 2004-11-29. Seven sample lines were designed around six lakes: Bang Lake, Sha Lake, Dahu Lake, Zhushi Lake, Changhu Lake and Xuzhou Lake. From the bank to the centre of the lake, according to the change of the elevation and water depth, the vegetation types show an obviously ringed belt distribution. The sample lines were designed vertical to the ringed belts. Besides the sample lines, randomly samples were also collected to assist further image classification. The cover and heights of the dominant grasses was recorded within the 20*20m plots. The cover was obtained by visual interpretation. We divided one plot to 4 parts. The final cover of one plot was calculated by the average of the four parts. The average sward heights of each grass species were also recorded. 208 samples were collected in the fieldwork. The 250*250m late and early up-greening grass plots were collected randomly in the Poyang Lake NR. Because these samples were collected for further quantitative spectral analysis, we chose higher purity late and early up-greening grass plots in the field. When the cover of the late or early up-greening grasses in a plot exceeded 80 percent, it was recorded as late up-greening grass or early up-greening sample. 26 late up-greening grass and 26 early up-greening grass samples were collected during the fieldwork. Different goose species has different preferential foods. Swan Goose always grazes in the shallow water (J. Zhang & Lu, 1999). Greater White-fronted Goose prefer for grazing in the grasslands (J. Zhang & Lu, 1999). In this research, we intended to record the dropping sites of greater white-fronted goose. The shape of the dropping is cylindrical, 5.8cm in length and 0.9cm in diameter. The colour is grassy-green which is related to their food (J. Zhang & Lu, 1999). The difference between the night dropping and daily grazing dropping is the former contains more white materials than the latter (G. lei pers. Comm.). We only recorded the grassy-green droppings with little white materials inside in the field. 5 samples were collected randomly in Poyang Lake NR.

2.3.2. Accessorial Data Pre-processing

We firstly prepared the validating samples (except grasslands samples) for TM classification. Water and others samples were generated from the typical wetland sedimentary physiognomy ichnography of the Poyang Lake NR (Cai et al., 1997). 46 water samples and 52 others samples (mountains and rice

10


paddy) were acquired for final accuracy assessment. We then prepared the land cover samples for MODIS classification. Water, paddy rice and other herb samples were digitized from the vegetation map of the Yangtze River (Lin et al., 1999). 35 water samples, 42 paddy rice samples and 29 other herb samples were obtained for further time-series spectral indices analysis. The geese observation data acquired from WWF were manual input data in Excel work sheets. We cleaned up the data and digitized them into point map. In order to generate the geese sites from the observation sites, we need to be familiar with the methods used in the observation. Varieties of methods were used to approach the wetland and the water birds during the survey. Usually the survey team drove as close as possible to the target area and proceeded on foot (Mark et al., 2004). People then recorded the coordinates of the observation sites and counted the birds by telescope. Considered about the range of the telescope, we generated 400-meter buffers around the observation sites. We assumed that the real geese sites were located within this area. The polygon map was used for final statistic analysis between the geese and grasslands.

2.3.3. Field Data Analysis (TM)

The flood recessional grasslands showed up due to the dynamic of the hydrology. The area with higher elevation would show up earlier than the lower parts. In the field survey, we also found that early up-greening grasslands always appeared in the higher area. The late up-greening grasslands appeared more in lower part. Therefore, elevation was an important environmental indicator for predicting early and late up-greening grasslands. Statistic analysis was taken to detect the relationship between the early and late up-greening grass cover and elevation. For further analysis, we sorted samples into two groups according to the grass cover. We assume a sample to be late or early up-greening grass samples if the cover of the vegetation type exceeds 50%. 110 late up-greening grass samples and 98 early up-greening grass samples were obtained finally. Late and early up-greening samples were sort to two groups randomly in a proportion of 7:3 (training samples: validating samples) for further supervised classification.

2.3.4. TM Imagery Pre-processing and Analysing

Image rectification is a process of converting a raw image into a specified map projection (Sabins, 1997). Six ground control points (GCPs) acquired from the topographic map of Poyang Lake NR (see section 2.2.2) were used to correct the TM image. The projection of the image was Gauss Kruger. The final total Root Mean Square Error was 0.2518. The image was resampled by using the Nearest Neighbour method to create the corrected image. The thermal band (6) was rejected from the image layers because it was not particularly used for classification. Spectral enhancements were used to modify the pixel values of the TM image. The raw TM image had redundancy information and was not so interpretable (Jesse, 1999). The enhancement methods used here were principle components analysis (PCA) and tasselled cap transformation (TCT). PCA can compress the information in a multi-spectral data. It allows the redundant data to be compacted into fewer bands and the dimensionality of the data is reduced. The PCA components are independent and more interpretable than the original data (Faust, 1989; Jensen, 1996). The first component (PC1)

11


contains most of the variation of the brightness value of the original bands(Jensen, 1996). Consider about the high correlation of the three visible bands (1, 2, and 3) and two middle-infrared bands (5 and 7) (Jensen, 1996), we apply two PCA on these two band-stack layers. Then we used two first components instead of band 1, 2, 3, 5 and 7 for the further classification. The near infrared band (4) is uncorrelated with other bands. It was retained for further analysis.

PCA TCT

PC1

TM B1 2 3

TM B5 7

TM B1-6

PC1 SBI GVI

Data merge

PCA

Final Enhanced TM Image

TM B4NIR

Figure 5 Flow chart of spectral enhancement on TM image

The Tasselled Cap Transformation uses mathematical algorithm to transform the original n multi-spectral bands into a new n-dimensional space. It provides a method to optimize data viewing for vegetation studies. The first two layers define the vegetation information content, producing most of the information (95-98%) (Crist, Laurin., & R.C. Cicone., 1986; Crist & R.J. Kauth., 1986; Jensen, 1996). The first layer is called soil brightness index (SBI), which defines the principle variation in soil reflectance. The second layer named greenness vegetation index (GVI), it is a contrast between the near infrared and visible bands, and strongly related to the amount of green vegetation. We executed TCT on the six TM bands and left SBI and GVI. Finally, we merged the PC1 (bands 1, 2 and3), PC1 (band 5 and 7), SBI, GVI and TM band 4 by layer stack.

2.3.5. MODIS Imagery Pre-processing and Analysing

The MODIS imagery was obtained after coarse geometric rectification. The projection was Lambert Conformal Conic. In order to decrease the relative error among the multi-temporal images and the field data, we tested the images by three well-distributed checkpoints (e.g. the corner of the hill) from

12


the field. The image 2004-08-08 fitted the checkpoints well. It was chosen to be a base image to do the geometric rectification. Seventeen ground control points selected from the image (2004-08-08) were used to modify the other 7 images. The Nearest Neighbour method was used to resample the other images. The final total Root Mean Square Error (RMSE) were 13.7461 (2004-07-23), 15.6937 (2004-0916), 15.3164 (2004-10-04), 18.9605 (2004-11-07), 19.4340 (2004-12-05), 11.6562 (2005-03-06) and 10.6525 (2005-04-21). The radiometric value changes because of the sensor noise and the atmospheric influence. In order to decrease the influence from these factors, we executed the histogram minimum value exclusion method to correct the value. The pre-requirement of using this method is the image should contain extremely low reflectance value objects (the shade of the mountain or the deep water). The reflectance value of these objectives approaches to zero. Every pixel in each band should subtract the minimum value of this band. Then the brightness range of the image improved and the quality of the image increased. In this research, the permanent water met the pre-requirement because its reflectance value is nearly zero. The histogram minimum value exclusion method was selected to do the radiometric correction (Mei, Peng, Qin, & Liu, 2003). After geometric and atmospheric corrections, we then buffered the study area and masked the cloud. The unsupervised classification was taken on MODIS image 2004-08-08. The summer image has the highest water level (figure 4). The water body was obtained by visual interpretation. The 5-km buffer was generated around the water body. 8 MODIS images were subset by the buffer for further analysis. In order to erase the cloud influence, four images (month 7, 8, 9 and11) with clouds were chosen to do the unsupervised classification. Four cloud cover maps were generated; all cloud observations were excluded from further analysis. Classification of grasslands was based on MODIS surface reflectance values from the blue (0.459-0.473μm), red (0.620-0.670μm), near infrared (0.841-0.875μm) and short wave infrared (1.628-1.625μm) bands. We calculated for each of the 8 MODIS images the Normalized Difference Vegetation Index (NDVI, formula1), the Land Surface Water Index (LWSI, formula 2) and the Enhanced Vegetation Index (EVI, formula 3) (A. Huete et al., 2002; A. R. Huete, liu, Batchily, & vanLeewen, 1997; Xiangming Xiao et al., 2005). NDVI is sensitive to green vegetation biomass and has been intensively used to classify land cover and monitor vegetation biomass and productivity (Gamon et al., 1995; Hunt, 1994; Malingreau, 1989; Sader, R.B. Waide, W.T.Lawrence, & A.T. Joyce, 1989; Vogelmann, 1988; William K. Michener & Paula F. Houhoulis, 1997). However, NDVI is also sensitive to the atmospheric conditions and soil background and saturates at closed canopy (A. Huete et al., 2002; Xiangming Xiao et al., 2005; X. Xiao et al., 2003). These problems are reduced while using EVI, which adjusts the difference in reflectance between the infrared and red band to a denominator that also considers the reflectance in the blue band. The blue band is sensitive to the atmospheric conditions and was used to do the atmospheric correction before. In order to prevent eliminating atmospheric influence twice, we selected images without atmospheric correction to calculate EVI. The availability of water is a crucial factor in flood recessional grasslands. LSWI is sensitive to leaf water and soil moisture content and was chosen together with NDVI and EVI to do the classification(Boles et al., 2004; X. Xiao et al., 2002a; Xiangming Xiao et al., 2005).

13


rednir

rednir

ρρρρ

+−

=NDVI (Formula 1)

swirnir

swirnir

ρρρρ

+−

=LSWI (Formula 2)

15.765.2 EVI

+×−×+−

×=bluerednir

rednir

ρρρρρ

(Formula 3)

Before executed unsupervised classification, we used principal component analysis (PCA) to transform the eight original index layers into a number of uncorrelated principal components. The values of indices derived from multi-temporal images were highly correlated. Such correlation leads to multi-collinearity of the independent data (indices) used in classification. The description of principle components analysis can be seen in section 2.3.4. PCA can offer significant improvement in classification accuracy (Dobson, J.R. Jensen, R.B.Lacy, & F.G.Smith, 1995b; Eastman & Fulk.M, 1993; Muchoney & B.N. Haack, 1994; William K. Michener & Paula F. Houhoulis, 1997). It was shown that one or more of the PCA components can be directly related to the change (Byrne, P.F.Crapper., & K.K. Mayo, 1980; Eastman & Fulk.M, 1993; Jensen, 1986). The flood recessional grasslands are appeared because of the dynamic of the water. There are two kinds of grasses growing in the flood recessional grasslands. They show distinct growing habits after flooding. Early up-greening grasslands (Miscanthus and Cynodon) usually grow in the higher part of the bottomlands (with the elevation more than 16m), which experience an earlier growth period and the over ground plants start to die in the early winter. While late up-greening grasslands appear in the lower part of the bottomlands, which grow fast in the late autumn and wintertime and start their dormancy in the late winter. For detecting these kinds of grasses by optical sensor, we require spectral bands or indexes that are sensitive to both water and vegetation. Some research had explored the potential of the greenness-related vegetation index (NDVI, EVI) and the water related index (LSWI) for detecting the paddy rice (Boles et al., 2004; Xiangming Xiao et al., 2005). In this paper, we wish to probe into more complicated ecosystem, the flood recessional grasslands. We firstly monitored the phenology of late and early up-greening grasslands by multi-temporal spectral indices. We then generated algorithms in identifying early and late up-greening grasslands by comparing phenological patterns of each land cover type.

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2.3.6. Classification Methods

Unsupervised classification is based on the natural groupings of pixels in image data when they are plotted in feature space. According to the specified parameters, these groups can later be merged, disregarded, manipulated, or used as the basis of a signature. Clusters are defined by analysing all or many of the pixels in the input data file. The ISODATA algorithm was executed in this research because it is iterative and not biased to the top of the data file. The ISODATA clustering method uses spectral distance as in the sequential method, but iteratively classifies the pixels, redefines the criteria for each class, and classifies again, so that the spectral distance patterns in the data gradually emerge (GIS & Mapping, 2003). ISODATA is iterative in that it repeatedly outputs a thematic raster layer and recalculates statistics. It uses minimum spectral distance to assign a cluster for each candidate pixel. The process begins with a specified number of arbitrary cluster means or the means of existing signatures, and then processes repetitively, so that those means shift to the means of the clusters in the data (GIS & Mapping, 2003). Maximum likelihood classification assumes that the statistics for each class in each band are normally distributed. It calculates the probability that a given pixel belongs to a specific class. Unless a probability threshold is selected, all pixels are classified. Each pixel is assigned to the class that has the highest probability (i.e., the maximum likelihood) (RSI, 2004). The maximum likelihood classifier is one of the most popular methods of classification in remote sensing. Before apply this method, some requirements deserve people’s attention. First, sufficient ground truth data, they help estimate the mean vector and the variance-covariance matrix of population. Second, prevent high correlation among bands and homogeneous ground truth data. Third, the distribution of the population must follow the normal distribution (JARS, 1996). In our research, we collected enough ground truth data (144 training samples). We eliminated the high correlation bands by spectral enhancements. And the population was normal distribution. The maximum likelihood classification can be applied in TM classification. Hybrid hierarchical classification is selected to classify several objectives that have distinct spectral characters. It requires enough ground truth data to determine the threshold of each objective. Other knowledge like phenology and expert knowledge is also essential for assisting hybrid hierarchical classification. In this research, late and early up-greening grasses have distinct phenological patterns. Because they grow in the flood recessional ecosystem, compared with other terrestrial vegetation, they also show different seasonal patterns. The water plays an important role in the flood recessional ecosystems. We can identify it by its unique spectral character and temporal change. The algorithms in Hybrid Hierarchical Classification were generated based on the phenological differences among variety of land cover types. The thresholds were calculated by comparing multi-temporal spectral indices of different end members. The masking layer sequence was: permanent water, Normal vegetation (paddy rice and other herb), floods lands, early up-greening grasslands and late up-greening grasslands. Figure 6 showed the flow chart of hybrid hierarchical classification.

15


Flood Lands Map

Grass Heights

Early Up-greening Grassland Mask

Late Up-greening Grassland Mask

Late up greening Map

Early up greening Map

Classes Remerge

Final Grasslands Map

Field and Accessory Data

NDVI EVI LSWI Layers

NDVI EVI LSWI Patterns

Permanent Water Mask

Normal Vegetation Mask

Flood Lands Mask

Permanent Water Map

Figure 6 Flow chart of the hybrid hierarchical classification

16


3. Results

3.1. Local Scale Grasslands Mapping using TM

3.1.1. Statistic Analysis

Figure 7 shows the cover of early and late up-greening grasses in relation to elevation. The figure shows as expected a negative relation between elevation and late up-greening grasses and a positive relation with early up-greening grasses cover. Late and early up-greening grasslands predominated below or above 14.5m respectively. The maximum elevation of the flood recessional grasslands was 17m. The area higher than 17 m was excluded from the further grasslands classification.

17.000016.000015.000014.000013.000012.0000

DEM

1.0

0.8

0.6

0.4

0.2

0.0

Cov

er_L

UG

17.000016.000015.000014.000013.000012.0000

DEM

1.0

0.8

0.6

0.4

0.2

0.0

Cov

er_E

UG

Figure 7 Relation between elevation (DEM) and the cover (proportion of area covered) of late and early up-greening grassland species

17


Table 1 Statistics describing various regression models between late up-greening grasslands distribution and elevation

Model Summary Parameter Estimates Equation R Square F df1 df2 Sig. Constant b1 b2 b3 Linear .320 97.738 1 208 .000 4.957 -.310 Cubic .325 49.786 2 207 .000 9.255 -.756 .000 .001

Table 2 Statistics describing various regression models between early up-greening grasslands distribution and elevation

Model Summary Parameter Estimates Equation R Square F df1 df2 Sig. Constant b1 b2 b3 Linear .334 104.391 1 208 .000 -3.563 .279 Cubic .336 52.275 2 207 .000 -5.554 .486 .000 .000

From table 1 and 2 show the result of regression between late and early up-greening grass covers and elevation. The P and F values show that the relationship between the grasses and elevation is significant. This is coincident with the field survey. Early up-greening grasses were dominated in the higher part and late up-greening ones were growing in the relatively lower part. However, the R square remained relatively low. The R square between the late up-greening grasses and elevation was 0.325, The R Square between the early up-greening grasses and elevation was 0.336. Because of the narrow elevation span, late and early up-greening grasses grow mixed together, especially in the community boundary area. We thus concluded that it’s not well feasible to predict the grasses distribution by digital elevation model and therefore excluded the DEM layer from the further classification.

3.1.2. Unsupervised Classification

We generated 20 classes by using unsupervised classification. According to the field knowledge and the vegetation map of the Poyang Lake (Cai et al., 1997), 20 classes were re-classed into four classes (water, late up-greening grasses, early up-greening grasses, and others). Table 3 Land covers remerging of UC based on spectral enhanced TM image

Class Land Cover Colour Cluster Numbers

1 2 3 4

Water LUP EUP Others

Blue Green Yellow Grey

2-13 16-20 14, 15 1

18


¦

Figure 8 Distribution of flood recessional grasslands map in Poyang Lake Nature Reserve predicted using the Landsat TM image and an unsupervised classification The first and fourth class (water and others) was removed from the spectral enhanced TM image. The flood recessional grasslands were retained for further Maximum Likelihood Classification.

3.1.3. Training and Validating Samples Design

Table 4 shows the training and validating sample design for maximum likelihood classification. Land cover types included water, late up-greening grasslands (LUG), early up-greening grasslands (EUG) and others. Table 4 Training and validating samples for maximum likelihood classification

Class Name Total Samples Training Samples Validating Samples

Water LUG EUG Others

46 110 98 52

---- 75 68

----

46 35 30 52

Total 306 144 163

3.1.4. Maximum Likelihood Classification

The result of MLC was remerged with the other land cover types (water and others) generated before.

19


¦

Figure 9 Distribution of flood recessional grasslands map in Poyang Lake Nature Reserve predicted using the Landsat TM image and a maximum likelihood classifier

3.1.5. Accuracy Assessment

Table 5 Error matrix for four land cover classes based on unsupervised classification Classified Data Water LUG EUG Others Row Total

Water 46 0 1 1 48

LUG 0 30 7 0 37

EUG 0 5 22 3 30

Others 0 0 0 48 48

Column Total 46 35 30 52 163

Table 6 Accuracy assessment for the data presented in table 5 Class Reference Classified Number Producers Users

Name Totals Totals Correct Accuracy Accuracy

Water 46 48 46 100.00% 95.83%

LUG 35 37 30 85.71% 81.08%

EUG 30 30 22 73.33% 73.33%

Others 52 48 48 92.31% 100.00%

Totals 163 163 146

Overall Classification Accuracy = 89.57%

Overall Kappa Statistics = 0.8591

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Table 7 Error matrix for four land cover classes based on maximum likelihood classification Classified Data Water LUG EUG Others Row Total

Water 46 0 1 2 49

LUG 0 28 1 1 30

EUG 0 7 28 1 36

Others 0 0 0 48 48

Column Total 46 35 30 52 163



Water 46 49 46 100.00% 93.88%

LUG 35 30 28 80.00% 93.33%

EUG 30 36 28 93.33% 77.78%

Others 52 48 48 92.31% 100.00%

Totals 163 163 150


Overall Kappa Statistics=0.8924

Both the unsupervised classification and maximum likelihood classification showed excellent results. The overall accuracies were 89.6% and 92.0%. The overall Kappa values of 0.86 and 0.89 pointed at an excellent classification result according to Landis and Koch (Alan. H. Fielding. & John. F. Bell., 1997; Landis & Koch, 1997) who considered Kappa value higher than 0.75 was excellent. We selected the maximum likelihood classification because of its higher accuracy to be used to validate the MODIS classification, which follows below.

3.1.6. Dominant Plant Species Analysis

Figure 10 shows the number of pixels classified as early and late up-greening grasslands. The total numbers of late and early up greening grasses pixels in unsupervised classification were 288321and 176736 respectively. And were 260520 and 204537 when using maximum likelihood classification. The late up-greening grasslands thus occupied according to unsupervised classification and maximum likelihood classification between 62 and 56 percent.

21


Figure 10 Number of pixels per land cover class according to the unsupervised and maximum likelihood classifier

3.1.7. Summary

In this sub-chapter, we firstly executed statistic analysis on the field data. Two classification methods (UC and MLC) were applied in the spatial enhanced TM image. We then analysed the dominant plant species in the flood recessional grasslands. The results are as follows: 1) The elevation range of the flood recessional grasslands in November was between 12.3 to 17 m.

The late up-greening grasses distributed in the elevations between 12.3 and 16 m. While the early up-greening grasses distributed between 12.8 to 17 m.

2) There is a significant relationship between the elevations and late & early up-greening grasses.

The R2 in linear model were 0.320 and 0.334 respectively. While the R2 in cubic model were 0.325 and 0.336 respectively.

3) Both unsupervised classification and Maximum Likelihood Classification produced excellent

results in mapping the flood recessional grasslands. The combination of unsupervised classification and Maximum Likelihood Classification obtained a higher overall accuracy (92.02%) and Kappa statistics (0.8924) compared with pure unsupervised classification. (89.57% and 0.8591).

4) The late up greening grasses were the dominant plant species in the flood recessional grasslands,

which providing 62% and 56% of the whole grasslands based on two classification methods.

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3.2. Regional Scale Grasslands Mapping using MODIS

3.2.1. Principle Components Analysis

The relation between the eight PCA axes and the NDVI of the original images is shown in table 9. The two principal components, representing 89% of the multi temporal NDVI information, were related to overall brightness. They were retained to classify the relatively stable land cover types like permanent water and terrestrial vegetation (classified as others). Principal components 3 and 8 were retained because they highlighted differences between late and early up greening grasslands. PCA 4, 5, 6 and 7 were excluded from the further analysis. Together components 1, 2, 3 and 8, accounted for almost 94% percent of the spectral information of the NDVI time series. Table 9 Matrix showing the eigenstructure of eight principal components against eight MODIS NDVI images Date/PCs 1 2 3 4 5 6 7 8

0.30 0.30 0.19 -0.67 -0.44 -0.30 0.20 03/06 04/21 0.24 0.45 0.17 -0.31 0.62 0.43 -0.23 07/23 0.40 -0.40 0.65 0.16 -0.17 0.40 0.18 08/08 0.42 -0.27 0.04 0.03 0.01 -0.37 -0.76 09/16 0.46 -0.29 -0.25 -0.03 0.51 -0.34 0.51 10/04 0.46 0.03 -0.65 0.01 -0.35 0.45 -0.06 11/06 0.25 0.46 0.04 0.55 -0.11 -0.03 0.15

-0.09 -0.02 0.10 -0.18 -0.03 0.19 -0.62

12/05 0.20 0.42 0.15 0.35 0.01 -0.33 -0.03 0.72 Eigenvalue 0.61 0.14 0.04 0.03 0.03 0.02 0.02 Cumulative% 80.17 89.34 92.61 94.84 96.62 97.92 98.99

0.02 100

Table 10 Matrix showing the eigenstructure of eight principal components against eight MODIS EVI images Date/PCs 1 2 3 4 5 6 7 8 03/06 0.20 0.23 0.06 -0.10 -0.04 -0.08 -0.94 -0.08 04/21 0.17 0.66 0.17 0.53 0.38 0.24 0.11 07/23 0.47 -0.23 0.64 -0.39 0.25 0.30 0.09 08/08 0.52 -0.20 0.20 0.52 -0.55 -0.28 0.07 09/16 0.50 -0.19 -0.48 0.00 0.57 -0.38 0.04 10/04 0.39 0.09 -0.53 -0.16 -0.33 0.62 0.03 11/06 0.13 0.34 -0.03 -0.23 -0.15 -0.03 0.21 12/05 0.14 0.51 0.06 -0.45 -0.20 -0.49 0.22

0.09 0.03 -0.02 -0.06 0.20 -0.86 0.44

Eigenvalue 0.103 0.025 0.009 0.005 0.005 0.004 0.003 Cumulative% 81.7 91.02 93.9 95.55 97.06 98.41 99.43

0.002 100

23


Table 10 shows the eigenstructure of the eight NDVI images plotted against the eight PCA axes. PCA 1 and 2 accounted for 91% of the information. PCA 6 and 8 were related to the phenological difference between late and early up-greening species. In this case, components 1, 2, 6 and 8 were retained for the unsupervised classification.

3.2.2. Perform Unsupervised Classification based on NDVI-PCA and EVI-PCA

20 classes were generated respectively by unsupervised classification. These spectral classes were visually inspected and re-classed into four classes (water, late up-greening grasslands, early up-greening grasslands, and others) based on their appearance in the imagery, knowledge of the study area and field survey in the Poyang Lake NR. The remerging of the classes can be seen in table 11 and table 12. Table 11 Land cover merging of unsupervised classification based on NDVI-PCA

Class Land Cover Colour Cluster Numbers 1 2 3 4



1-6, 10 7-9 11 12-20

Table 12 Land cover merging of unsupervised classification based on EVI-PCA

Class Land Cover Colour Cluster Numbers 1 2 3 4



1-6 7-9 10, 11 12-20

Figure 11 and 12 showed the late and early-up greening grasses in flood recessional grasslands distributed along the middle and lower reach of the Yangtze River. Figure 13 and 14 showed the late and early up-greening grasses in flood recessional grasslands in Poyang Lake Nature Reserve.

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Figure 11 Distribution of flood recessional grasslands along Yangtze based on NDVI-PCA

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Figure 12 Distribution of flood recessional grasslands along Yangtze based on EVI-PCA

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Figure 13 Distribution of flood recessional grasslands in Poyang Lake based on NDVI-PCA

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Figure 14 Distribution of flood recessional grasslands in Poyang Lake based on EVI-PCA

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3.2.3. Time-series Spectral Indices Analysis

Based on the regional study area (within 5km buffer) knowledge, besides of the flood recessional grasslands, there are 3 other kinds of dominant land cover types, permanent water, paddy rice and other herb. With the field survey and land cover samples obtained from the vegetation map (see section 2.3.2); the multi-temporal NDVI, EVI, and LSWI patterns were generated for further analysis. These patterns are shown in figures 15 and 16.

Late Up Greening Grasses Patterns

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Figure 15 Time series derived from MODIS 2004 data of NDVI, EVI and LSWI for late up-greening grasslands (LUG), early up-greening grasslands (EUG), as well as irrigated agricultural ecosystems (Paddy rice) and other terrestrial vegetation (herbs) From the upper two graphs in figure 15, we found that two NDVI and two EVI indices were all have a sharp decrease in July. It happened because of the influence of the water. During the summer time the water level was high and the grasslands were flooded. The NDVI and EVI of early up-greening grasses started to increase fleetly after flood. In contrast, the NDVI and EVI of late up-greening

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grasses were lingering around the low value for another two month (August and September). The reasons why the indices changed like this are, one side, the distribution of the early up-greening grasses was higher than late up-greening ones in the bottomland. Early up-greening grasses showed up earlier than late up-greening ones. We further analysed the relationship between the water index (LSWI) and the vegetation indices (NDVI and EVI). For late up-greening grasses graph, we found that LSWI was higher than NDVI or EVI in the month of July and September. In fact, the water level was high in August (figure 3). However, the aquatic plants grow flourishing and the content of the chlorophyll was very high, NDVI and EVI showed slightly higher than LSWI. Similar as late up-greening grasses, early up-greening grasses graph showed that LSWI was approach to or higher than NDVI and EVI in July and September. The common ground of these two grasslands was they were flooded in the summer time. The pixel was identified as flooded one when either the EVI < LSWI or NDVI < LSWI in one to a few MODIS imagery. There are several other land cover types that would also influence the implementation of the algorithms for identifying flood recessional grasslands. We first generated these land cover layers and erased them from the grasslands identification. Here, we analysed three kinds of land cover types, permanent water, paddy rice and other herbs.

Water Index Patterns

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Figure 16 Time series derived from MODIS 2004 data of NDVI, EVI, and LSWI for permanent water ecosystems The first step was to identify the permanent water. Figure 16 shows the change of index based on water pixels. By analysing the dynamics of NDVI, EVI and LSWI, we assumed that a pixel to be water when EVI < 0.1 and EVI < LSWI. The algorithm was stricter than the one used in the research of mapping paddy rice, NDVI < 0.1 and NDVI < LSWI (Xiangming Xiao et al., 2005). We generated a map with the frequency of being classified as water. We then assumed that a pixel to be the permanent water if it was classified as water with no less than 5 times within 8 MODIS imagery. The second step was to detect the normal vegetation compared with the flood recessional plants. The rice paddy was also temporal flooded. However, it was flooded in the end of July and the July image was missing. Then the dynamic of these indexes based on paddy rice were similar to those of the other herb. The NDVI and EVI indexes started to increase in spring peaked in September and decreased

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sharply in wintertime. It showed that the paddy rice and other herbs all grew well in summer and perished in winter. Because of the flooding influence, the flood recessional grasslands and normal vegetation had different prosperous time. The flood recessional grasslands had higher NDVI and EVI value in spring and lower value in late summer. The normal vegetation had higher NDVI and EVI value in late summer and lower value in spring. The pixel was identified as normal vegetation when NDVI (9) > NDVI (4) or EVI (9) > EVI (4). For each MODIS image, 8 normal vegetation maps were generated. All normal vegetation pixels were excluded from further analysis. The third step was to determine the temporally flooding pixels. We assume that the lands were flooded when the LSWI approached to or exceeded the NDVI or EVI value. The flood recessional lands were all have at least once flooded. A pixel was classified as flood recessional land when LSWI > EVI or LSWI > NDVI in either 8 MODIS image. 8 flood recessional lands layers were extract for further late and early up-greening grasslands detection. At last, we firstly executed algorithms for detecting C4 grasses: (NDVI (10) > DNVI (07) and NDVI (10) > DNVI (12)) or (EVI (10) > EVI (07) and EVI (10) > EVI (07)). After masking C4 grasslands layer, we then actualised the algorithm for exploring C3 grasses: (NDVI (10) < DNVI (11) or NDVI (10) < DNVI (12)) or (EVI (10) < EVI (11) or EVI (10) < EVI (12))

3.2.4. Hybrid Hierarchical Classification

Figure 17 showed late and early up-greening grasslands distributed along the middle and lower reach of the Yangtze River. Figure 18 showed late and early up-greening grasslands in part of the Poyang Lake.

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Figure 17 Distribution of flood recessional grasslands along the Yangtze according to the hybrid hierarchical classification

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Figure 18 Distribution of flood recessional grasslands in Poyang Lake Nature Reserve according the hybrid hierarchical classification

3.2.5. Validating Samples Preparation

The result of maximum likelihood classification based on spectral enhanced TM image was satisfying. The overall accuracy was 86.15%, and the overall Kappa statistics equalled to 0.73. In this case, we assume the maximum likelihood classification result map to be a convincing flood recessional grasslands distribution product. The late and early up-greening grass samples were generated from the maximum likelihood classification grasslands map randomly. The distance between two samples was no less than 10 pixels (300m). 108 late up-greening grasses and 59 early up-greening grasses samples were acquired finally. They were used as validating data for testing the classification accuracy of the MODIS imagery.

3.2.6. Accuracy Assessment

Table 13 Error matrix for four land cover classes based on NDVI-PCA Classified Data Water LUG EUG Other Row Total

Water 86 9 1 12 108

LUG 0 85 11 1 97

EUG 0 10 17 0 27

Others 1 4 30 39 74

Column Total 87 108 59 52 306

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Water 87 108 86 98.85% 79.63%

LUG 108 97 85 78.70% 88.54%

EUG 59 27 17 28.81% 62.96%

Others 52 74 39 75.00% 52.70%

Totals 306 306 227



Table 15 Error matrix for four land cover classes based on EVI-PCA Classified Data Water LUG EUG Other Row Total

Water 86 12 4 17 119

LUG 1 92 14 0 107

EUG 0 3 27 9 39

Others 0 1 14 26 41

Column Total 87 108 59 52 306



Water 87 119 86 98.85% 72.27%

LUG 108 107 92 85.19% 86.79%

EUG 59 39 27 45.76% 90.00%

Others 52 41 26 50.00% 63.41%

Totals 306 306 231



Table 17 Error matrix for four land cover classes based on hybrid hierarchical classification Classified Data Water LUG EUG Others Row Total

Water 79 12 0 5 96

LUG 3 83 4 6 96

EUG 0 12 39 1 52

Others 5 1 16 40 62

Column Total 87 108 59 52 306

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Water 87 96 79 90.80% 82.29%

LUG 108 96 83 76.85% 95.40%

EUG 59 52 39 66.10% 76.47%

Others 52 52 40 76.92% 64.52%

Totals 306 306 122



According to accuracy assessments, all of methods NDVI_PCA, EVI_PCA and hybrid hierarchical classification showed excellent result in classifying flood recessional grasslands. Hybrid hierarchical classification had the highest overall accuracy (78.76%) and Kappa Statistics (0.7102). Other methods, in increasing order of accuracy, were NDVI_PCA and EVI_PCA, with the overall accuracy 74.18% and 75.49%, Kappa Statistics 0.659 and 0.7102 respectively. The comparisons of the two methods can be seen in figure 19. The late and early up-greening grasslands map classified by Hybrid Hierarchical Classification was prepared for the following analysis between the grasslands distribution and geese distribution.

H H CE V I_P C AN D V I_P C A

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Figure 19 Graph summarizing the accuracy of different methods at the regional scale

3.2.7. Summary

In this sub-chapter, we compared two methods (VI_PCA and HHC) in classifying the flood recessional grasslands based on MOIDS imagery. The results were summarized as follows: 1) Unsupervised Classification based on the PCA of multi-temporal vegetation indices (NDVI and

EVI) obtained excellent results in classifying late and early up-greening grasses in the flood recessional grasslands. EVI_PCA showed a relatively better result than NDIV_PCA.

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2) The phenology of late and early up-greening grasses could be monitored by spectral indices (NDVI and EVI) derived from multi-temporal MODIS images. The early up-greening grasses grew fast in the early autumn and started to decrease in the early wintertime. The late up-greening grasses grew sharply after showed up in late autumn and drew to a steadily increase in the wintertime.

3) Hybrid Hierarchical Classification was executed by comparing the three spectral indices (NDVI,

EVI and LSWI) of different land cover types. HHC based on the phenological knowledge could classify the flood recessional grasslands well. It showed a relatively better result than the first method.

3.3. Relationship Analysis between Grasslands and Geese

The right green line in figure 20 showed the migratory flyway of the lesser white-fronted goose to the Yangtze River Basin. The blue points in the southeast China were the geese wintering sites.

Figure 20. Distribution and migratory flyways of the lesser white-fronted goose (Anser erythropus) (UNEP, 2005) Figure 21 shows the temperature in January. The area inside a red circle in southeast China is the middle and lower reaches of Yangtze. The 0℃ isotherm is right located in the north of the Yangtze basin. The average temperature of this area in January is around 6℃.It provides the proper climate for grass growing during the wintertime.

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Figure 21 Map showing mean monthly temperature in January. The red circle indicates the regional study area

3.3.1. Relationship Analysis among Temperature, Grasses and Geese Lingering time

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Figure 22 Relationship among temperature, late up-greening grasslands and geese lingering time The phenological patterns of late up-greening grasslands showed that, the grasslands experienced an active growing during the spring, autumn and wintertime. Compared with the temperature graph in figure 22, the late up-greening grasslands were a kind of cool season grasslands. They preferred to grow in the temperature between 8-20℃. The up-greening period of this cool season grasses was coincident with the geese lingering time (September to April) (Cai et al., 1997). The late up-greening grasslands growing in the cool seasons could produce higher quality young leafs for overwintering

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geese. It is likely to be one of the reasons why geese grazing in the flood recessional grasslands during the wintertime.

3.3.2. Relationship Analysis between the grass heights and geese

As we introduced before, geese prefer grasses in detail heights. Some research concluded that, both pink-footed and greater white-front geese prefer grass heights of 13-20 cm (J. A. Vickery, Sutherland, O'Brien, Watkinson, & Yallop, 1997); Norfolk bean geese also prefer longer swards of 20cm in length, then the captive birds can have a faster intake (Allport, 1989; Juliet A Vickery & Gill, 1999). However, some experimental studies indicated that Barnacle geese prefer for swards of < 10 cm in length but will graze on longer swards when preferred areas are grazed heavily (Juliet A Vickery & Gill, 1999). In fact, in the field, we found pecks in sward height of 30-45 cm (before grazed). It was also showed that the relatively high grasses could significantly increase peck rates of geese (Juliet A Vickery & Gill, 1999). The reduced intake rate occurred at height of < 4 cm in Brent geese and < 2 cm in Barnacle geese (Drent & Swierstra, 1977; R. Riddington, M. Hassall, & S.J. Lane, 1997). The relationship between the grass height (quantity) and nitrogen content (quality) was very complicated. However, there’s a negative relationship between the grass height and the nitrogen content (Summers & Critchley, 1990). Figure 23 shows the sward heights of late up-greening grasses (Carex spp.) and early up-greening grasses (Miscanthus and Cynodon). The height of Carex was between 2-84 cm according to different growing stages. Most of Carex heights were between 20-40cm. The height of Miscanthus was between 16-168 cm. Most of their heights were between 90-150 cm. The height of Cynodon was from 0.3 to 6 cm. And most of the heights were from 1 to 2 cm. According to the geese grazing preference, the early up-greening grasses were not suitable for geese to graze. They were either too high (Miscanthus) or too low (Cynodon). The younger late up-greening grasses (Carex. spp) showed perfect sward heights for geese. They can provide both relatively low (2-20 cm) and relatively high grasses (>20 cm) for overwintering geese. That would be another reason why geese foraged in the flood recessional grasslands.

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Figure 23 Box plots showing variation in sward height in three grassland communities in Poyang Lake Nature Reserve

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3.3.3. Statistic Analysis between Geese Distribution and Grasslands Distribution

The red points in Figure 24 show that the distribution of droppings of greater white-fronted goose (collected in the field) and presence sites (acquired from Dahu Lake preserving station) in Poyang Lake NR. The white-fronted geese primarily distributed in five lakes: north and west of the Xuzhou Lake, southeast of the Changhu Lake, north of the Sha Lake, most of the Dahu Lake and Zhushi Lake. We then calculated the presence ratio of the Greater White-fronted Goose in each land cover type. Table 19 reveals that 80% of the White-fronted geese were presented in the late up-greening grasslands; other land cover types, in decreasing order of the presence ratio, were early up greening grasses (0.13), water (0.07) and others (0). The results indicated that white-fronted goose preferred for utilizing the late up-greening grasslands. Table 19 Distribution of greater white-fronted geese related to land cover in Poyang Lake NR Geese / Classes Water LUG EUG Others Total Presence 1 12 2 0 15 Presence Ratio 0.07 0.80 0.13 0 1

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Figure 24 Observed distribution of greater white-fronted goose in relation to the distribution of early and late up-greening grasslands according the hybrid hierarchical classification in Poyang Lake Nature Reserve Figure 25 showed the geese observation sites in the middle and lower reaches of the Yangtze River. Most observation sites were located in south and east of the Poyang Lake, south and northeast of the Dongting Lake and lower of the Yangtze River. We calculated the geese probability presence ratio in

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different land cover within 400m buffers of the observation sites (see section 2.3.2). The result in table 20 shows that, geese were likely to present in late up-greening grasslands, the probability presence ratio was 0.81; other land covers, in decreasing sequence of the probability presence ratio, were water (0.59), others (0.48) and early up-greening grasslands (0.31). Geese were most likely to choose late up-greening grasslands as their foraging lands. Water produced a relatively higher probability presence ratio here because late up-greening grasslands usually grow along the water. Table 20 Probability ratio of the geese presence in each land cover Geese / Classes Water LUG EUG Others PP 41 57 22 34 PA 29 13 48 36 Total 70 70 70 70 PPR 0.59 0.81 0.31 0.48 PP- Probability presence; PA- Probability absence; PPR- Probability presence ratio

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Figure 25 Distribution of geese recorded by WWF (2004) in relation to the distribution of grasslands according the hybrid hierarchical classification

Considered about the greater white-fronted goose primarily utilized the grasslands (J. Zhang & Lu, 1999). We calculated the presence ratio of the greater white-fronted goose in different land cover types. The result in Table 21 shows that the probability presence ratio of greater white-fronted goose in late up-greening grasslands was 0.92. It shows a significant increase compared with using all of the geese sites. Other land covers producing 0.46 (water and others) and 0.41 (early up-greening grasslands)

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respectively. Obviously, greater white-fronted goose preferentially used the late up-greening grasslands. Table 21 Probability ratio of the greater white-fronted goose presence in each land cover Geese / Classes Water LUG EUG Others PP 11 22 10 11 PA 13 2 14 13 Total 24 24 24 24 PPR 0.46 0.92 0.41 0.46 PP- Probability presence; PA- Probability absence; PPR- Probability presence ratio

3.3.4. Summary

In this sub-chapter, we analysed the relationship between geese and grasslands. Three kinds of relative relationship were summarized as follows: 1) The geese lingering time was coincident with the late up-greening grasslands green time and cool

season time. The late up-greening grasslands activated under temperature 8-20℃ produced young leaves when geese overwintering here. Geese grazed in these grasslands because they could provide higher quality forage.

2) Late up-greening grasslands (Carex spp.) could provide proper sward heights for overwintering

geese: relatively low (2-20 cm) and relatively high (>20 cm). In the field, pecks were also found in sward heights of 30-45 cm.

3) By analysing the field data, we found 80% of the Greater White-fronted geese presented in the

late up-greening grasses. Geese were selectively utilizing late up-greening grasslands. By analysing geese data acquired from WWF, the probability presence ratio of all kinds of geese in late up-greening grasslands was 0.81, and the probability presence ratio of greater white-fronted goose in late up-greening grasslands was 0.92. Geese obviously preferred late up-greening grasslands to any other vegetation types.

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4. Discussion and Conclusions

4.1. Discussion

4.1.1. MODIS and Flood Recessional grasslands Detection

Flood recessional grasslands are a kind of demanding objectives when we try to detect them. They keep altering because of the fluctuation of water levels and were only distributed along the bank of rivers or lakes. Compared with traditional remote sensing (e.g. TM), Moderate Resolution Imaging Spectroradiometer (MODIS) imagery doesn’t have high spatial resolution. However, high temporal resolution (one day one scene), high radiometric resolution and broad coverage make MODIS imagery show overwhelming advantages in mapping dynamic ecosystems in regional scale. In this research, MOIDS not only detected the flood recessional grasslands, but also successfully mapped the distribution of two different phenological plant species in the flood recessional grasslands. It also showed the potential in monitoring the paddy rice agriculture (Xiangming Xiao et al., 2005). According to these potentials, MODIS could be extended to exploring other dynamic ecosystems, for instance, detecting vegetation change associated with extensive flooding in a forested ecosystem. It is not so feasible to collect field samples in a regional study area. Unsupervised classification requires only minimal initial input. That is why we try to detect the potential of unsupervised classification in MODIS regional study area classification. Unexpectedly, unsupervised classification based on multi-temporal vegetation index obtained excellent results in this research. Unsupervised classification should be considered as an important classifier in later regional study. Two multi-temporal vegetation indices maps were merged before executing unsupervised classification. Huete has announced that enhance vegetation index (EVI) is better than normalize difference vegetation index (NDVI) because it considers the residual atmospheric contamination and variable soil and canopy background reflectance (A. Huete et al., 2002; A. R. Huete et al., 1997). This research also verified this conclusion. EVI_PCA gained better result than NDVI_PCA in classifying late and early up-greening flood recessional grasslands. The MODIS vegetation indices were designed to provide consistent, spatial and temporal comparisons of vegetation conditions that can be used to monitor photosynthetic activities (A.Huete. et al., 2002; Justice et al., 1998; Running et al., 1994). Some research (X. Zhang et al., 2003) already showed MODIS could detect vegetation phenology well. In this research, hybrid hierarchical classifier based on phenology obtained restively best result in mapping late and early up-greening flood recessional grasslands. We explored the three indices here: NDVI, EVI and LSWI (Land Surface Water Index). However, there are two short wave infrared bands (SWIR1: 1.628-1.625 μm and SWIR2: 1.580-1.750 μm) in MODIS imagery. In this research, we only calculated the LSWI by near infrared band and the first short wave infrared band. In fact, we found the flood recessional grasslands were visually distinct from other land cover types when using combination of near infrared band and the second short wave infrared band. It should be a potential band in detecting flood recessional grasslands in the future research.

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However, more accuracy algorithms were required in the hybrid hierarchical classification. When masking the water, we assumed that a pixel to be the permanent water if it was classified as water with no less than 5 times within 8 MODIS imagery. The threshold <= 5 was obtained by comparing the masking result with the vegetation map of Yangtze River. When masking the paddy rice, we sorted the rice and other herb together and masked them as one layer. In fact, the paddy rice was also temporally flooding, and its seasonal pattern was different from that of the other herb. The phenological pattern of paddy rice would have a sharp drop in June because of the planting. This distinct character couldn’t be shown up because we missed the image of June. The phenological pattern and identified algorithm are actually different with those in this research. MODIS pre-processing technology need to be improved: First, quality of the original image: There are still some strips in the images we collected. We wish to erase this influence to get a more accurate result. Second, geometric and atmospheric corrections: The geometric and atmospheric corrections used in my research were relatively coarse, more accurate corrections needed in the future. According to the regression analysis in section 3.1.1, there is a significant relationship between elevation and late & early up-greening grasses in the flood recessional grassland. Considered about the distribution of these grasses in the field, elevation is one of the most important environmental indicators for detecting the flood recessional grasslands. However, the R2 between the elevations and grasslands were only 0.325 (late up-greening grasslands) and 0.336 (early up-greening grasslands). There are several reasons here: First, the narrow elevation span, it makes late and early up-greening grasses grow mixed together, especially in the community boundary area; Second, cattle grazing, in the field, we found cattle were grazing in the higher part of the flood recessional grasslands, overgrazing might change the dominance of plant species. Third, the quality of the DEM, though the DEM used in this research has a high vertical resolution, part of the bank elevations were generated from land cover maps. The accuracy of DEM especially on the bank is not so high. A digital elevation model could be built to predict late and early up-greening flood recessional grasslands in the future, if the elevation span is relatively wide and the accuracy of DEM is high enough.

4.1.2. Photosynthetic Pathways of Early and Late Up-greening Grasses: C3 or C4?

The photosynthetic pathway determines the ecophysiology and biogeography of grasses. Commonly, grasses execute either C3 (Calvin–Benson cycle) or C4 (Hatch–Slack) photosynthesis. They are distinguished by the distinct carbon isotope ratios (δ13C) of C3 plants (~ -27‰) and C4 plants (~ -13‰ ) (Deines, 1980; Smith & White, 2004). C3 grasses thrive in an environment with decreased light (L. L Tieszen, 1970), increased soil moisture (Barnes, Tieszen, & Ode, 1983), lower temperature (Schuster & and Monson, 1990) and higher CO2 conditions (Ehleringer & Monson, 1993) compared to C4 species (Davidson & Csillag, 2001). Different environmental requirements result in two distinct seasonal patterns of C3 and C4 species. C3 species green up in early spring and are most active under the cooler conditions of spring and fall. C4 species green up later in the growing season, and are more active under the hotter and drier summer months. According to the photosynthetic efficiency, C3 grasses frequently have higher levels of photosynthetic enzymes and protein compared with C4 grasses (Raymond.V. Barbehenn., Zhong Chen., David, & Angela. Speickard., 2004). C3 grasses commonly contain higher levels of non-structural carbohydrates, protein, and lower levels of fibre, silica and toughness than C4 grasses (Barbehenn RV & EA, 1992; Bernays EA & Hamai J, 1987;

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Wilson JR, Brown RH, & WR, 1983). C3 dominate grasslands produce better diet quality for animals, which is reflected by higher levels of faecal nitrogen (Ash, McIvor, Corfield, & Winter, 1995). Why we discuss the photosynthetic pathways of C3 and C4 species here? The phenology of C3 and C4 species were coincident with those of late and early up-greening grasses. C4 species grew fast in hotter and dryer seasons and ceased growth in the cool seasons. C3 species preferred for cooler temperature and were active during the autumn and wintertime (Davidson & Csillag, 2001, 2003). Late up-greening grasses growing in the flood recessional grasslands were green, productive in the cool seasons (autumn, winter and spring). Early up-greening grasses were active in warm seasons (late summer and early autumn) and ceased growth in winter. We have also identified that the early up-greening grasses were C4 species (Watson & Dallwitz., 1992). The photosynthetic pathways of Carex species (late up-greening grasslands) are complicated, some of them are C3 species and some are C4, some even execute both photosynthetic pathways under different environmental conditions (Bruhl;, Watson;, & Dallwitz, ; R.Z. Wang., 2004). Some literature (Tafta, 2003) also mentioned that Carex spp. (late up greening grasses) were C3 species. We have no authoritative literature to prove the C3 or C4 status of late up-greening grasses (Carex cinerascens, Carex argyi and Carex unisexualis). Some identification needs to be taken in the lab to determine the photosynthetic pathway of these three carex species. Here we assumed that late up-greening grasses were likely to be C3 species. The middle and lower reaches of Yangtze River is distributed in the sub-tropical zone where C4 species dominated (Yin & Li, 1997). Because of lower nitrogen content and old leaves (Barbehenn RV & EA, 1992; Bernays EA & Hamai J, 1987; Wilson JR et al., 1983), C4 species (early up greening grasses) do not appear to be an attractive source of food for overwintering geese. However, the situation was different in the flood recessional grasslands. We demonstrated that the dominant plant species in flood recessional grasslands were late up-greening grasses (Carex spp., see section 3.1.6.). This condition explained why geese migrated to Yangtze River and selected flood recessional grasslands as their overwintering land. C4 species (early up-greening grasses Miscanthus and Cynodon) were not the dominant plant species in the flood recessional grasslands. Late up-greening grasslands, which execute similar C3 photosynthetic pathway, produced higher quality forage for geese.

4.1.3. Geese and Flood Recessional Grasslands

A special flood recessional ecosystem attracts geese overwintering in the middle and lower Yangtze. The quality and height of grass in the flood recessional grasslands were considered here. Higher quality and moderate height grasses are more preferred by geese. More accurate information about the geese grazing habits is required in the further relationship analysis. More detailed geese observation data required in analysing the relationship between geese and flood recessional grasslands. Four kinds of accessory information recommended for the later research: first, the coordinates of geese sites: The geese observation data acquired from WWF only recorded the coordinates of observation sites. That’s why we generate a 400m buffer to calculate the probability presence ratio of geese. The geese coordinates could be generated from sketches drew by observers, and the results would be more convincing. Second, detailed activities of geese: There are six kinds of geese activities: Preening, Alert, Locomotion, Resting, Feeding and Other (J. Zhang & Lu, 1999). In order to analyse the relationship between geese and grazing lands, we need to record the coordinates

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where geese grazing. Third, geese dropping recording: Geese dropping is an important evidence for geese appearance. In this research, because of the bird flue, we only collected 5 geese dropping sites. Far more ground truth data is needed for further relationship analysis. Day droppings only appeared when geese feeding, while night droppings appeared where geese resting during the night. In this research, we identified them by the quantity of the white material in the droppings. A more accurate method for identifying the day dropping and the night dropping is needed. Fourth, heights of geese grazing grasses: measuring the grass height when geese fly away after grazing can be used to collect this kind of data. It can be used to verify the geese favourite sward heights.

4.2. Conclusions

The proposed research aims to support the following conclusions.

1) There is a significant relationship between elevation and late & early up-greening grasslands. 2) The late up-greening grasslands were the dominant plant species in the flood recessional

grasslands.

3) Multi-temporal vegetation indices derived from MODIS imagery predicted late and early up greening grasses in the flood recessional grasslands well. EVI_PCA showed a relatively better result than NDIV_PCA.

4) Late up-greening grasses growing in the flood recessional grasslands were green, productive

in autumn, winter and spring. Early up-greening grasslands were inactive in late autumn and ceased growth in winter.

5) Hybrid Hierarchical Classification (HHC) based on phenology produced highest accuracy in

classifying late and early up-greening grasses in the flood recessional grasslands by using MODIS imagery.

6) The late up-greening grasses activated under temperature 8-20℃ produced young leaves

when geese overwintering in the middle and lower Yangtze River. Late up-greening grasslands (Carex. spp) provided proper sward heights for overwintering geese. Geese were selectively utilizing late up-greening grasslands.

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