Abstract Sigificant dependence from climate andanthropogenic influences characterize ecological systemsof Kazakhstan. As result of the geographical location of therepublic and ecological situation vegetative degradationsites exist throughout the territory of Kazakhstan. Themajor process of desertification takes place in the arid andsemi-arid areas. To allocate spots of stable degradation ofvegetation, the transition zone was first identified.Productivity of vegetation in transfer zone is slightlydependent on climate conditions. Multi-year digital mapsof vegetation index were generated with NOAA satelliteimages. According to the result, the territory of the republicwas zoned by means of vegetation productivity criterion.All the arable lands in Kazakhstan are in the riskyagriculture zone. Estimation of the productivity ofagricultural lands is highly important in the context ofrisky agriculture, where natural factors, such as wind andwater erosion, can significantly change land quality in arelatively short time period. We used an integratedvegetation index to indicate land degradation measures toassess the inter-annual features in the response ofvegetation to variations in climate conditions from low-resolution satellite data for all of Kazakhstan. This analysisallowed a better understanding of the spatial and temporalvariations of land degradation in the country.
Keywords remote sensing, NOAA, land cover changes,vegetation indexes
1 Introduction
Remote sensing of the Earth is the most effective methodof monitoring the underlying surface state, especially whenit comes to the study of vast inaccessible areas, such as the
arid and semi-arid regions of Kazakhstan. The Republic ofKazakhstan occupies a large area of 2.7 M km2. Themajority of this area is located in arid and semi-arid zonesand is used for pastures. Currently, there is a desertificationproblem in these zones, which is connected to climatechanges and anthropogenic impact. Space monitoring ofthe Kazakhstan Republic has been implemented since2000 in order to register vegetation condition changes anddetect desertification zones. To create an effectivedesertification area identification method, it is necessaryto distinguish seasonal vegetation changes, which arecaused by climate condition variations, from sustainablevegetation degradation over a long period. There is a needto map the spatial and temporal changes of vegetationcover within Kazakhstan. Long-term remote sensing dataare the means for monitoring and mapping changes invegetation. A set of vegetation indices derived fromNOAA/AVHRR imagery were used to describe the state ofvegetation cover and changes of Kazakhstan.
2 Analysis methods
A special method incorporating the integral index ofvegetation was developed in order to have an objectiveassessment of vegetation dynamics. This method includesfollowing procedures (Spivak et al., 2006; Spivak et al.,2008):1) Processing of NOAA satellite images and referencing
of images in Geographic Lat/Lon Projection for the WGS-84 spheroid;2) Calculating daily values of normalized differential
vegetation index (NDVI) using two reflective channels ofthe AVHRR/NOAA radiometer;3) Constructing decade (ten days) composite values of
NDVI. The calculation of decade (note: “decade” = tendays, in this paper) composites is based on maximal valuecompositing of NDVI for each a period of ten days. Thelong-term NDVI series are used for recording and analysis
Received November 22, 2011; accepted April 16, 2012
of environmental changes, vegetation states, and tradi-tional land use (Townshend and Justice, 1986; Tucker andSellers, 1986; Ichii et al., 2002). The experiments carriedout under a USAID research grant showed that the NOAANDVI decadal maxima values of the entire territory ofKazakhstan well reflect the dynamics of vegetation duringthe growing season, and can be used to quantify theamount of green biomass (Gitelson et al., 1998; Kogan etal., 2003). NDVI values describe the combined effects ofnatural (long-term) and weather (short-term) influences onvegetation productivity.4) Calculating integral vegetation index (IVI), an
indicator of green biomass, by summing NDVI compositesfor each vegetative growing season using the followingequation:
IVI ¼X27
i¼10
NDVIi, (1)
where index “i” is the number of decades counted out fromthe beginning of the year. Analysis of long-term changes inproductivity of vegetation is most effectively performed byusing the IVI, which characterizes the total amount ofgreen biomass accumulated during the growing season ineach pixel.5) Calculating the vegetation conditions index (VCI).
The term vegetation condition index, introduced by Kogan(1995), provides a numerical approximation of the impactof weather conditions on the productivity of vegetativecommunities. The value of VCI can be used as a coefficientfor the impact of seasonal weather conditions on theamount of terrestrial biomass.6) Constructing the integral vegetation conditions index
(IVCI) for determining inter-annual variations of climateusing Eq. (2):
IVCI ¼ IVIi – IVImin
IVImax – IVImin, (2)
where IVIi is the value of the current decadal compositeNDVI in a given pixel; IVImax represents the maximumlong-term value of decadal NDVI composite for the baseperiod in the pixel; IVImin represents the minimum long-term value of decadal NDVI composite for the base periodin the pixel. These vegetation indicators were then mappedfor Kazakhstan from 2000 to 2009, using the NOAA/AVHRR data at 1km spatial.
3 Results and analysis
The distribution of IVCI for the entire period is shown inFig. 1. It is evident from preceding satellite data that 2002
Fig. 1 Dynamics of the IVCI for the territory of Kazakhstan (2000–2009)
Lev SPIVAK et al. The experience of land cover change detection by satellite data 141
was the most favorable year for vegetation growth duringthe considered period, while 2000, 2001, 2003 and 2005could be marked as seasons with moderate weatherconditions. These distributions show an increase inweather stress impacts on vegetation cover in theRepublic’s territory since 2004, with maximal deteriora-tion in 2006.Territory zoning was completed based on digital maps of
IVI for the period 2000–2008, and five zones of differentproductivity of vegetation were detected: Zone A –the highproductivity zone; Zone B–the temperate productivityzone; Zone C –the medium productivity zone; Zone D –thelow productivity zone; Zone E –the very low productivityzone (desert).The arrangement of detected zones is represented in
Fig. 2. It is shown that the latitudinal sequence of thelocation of zones with various productivities is connectedwith the typical vegetative cover and features of thegeographical location of the Republic of Kazakhstan.The changing dynamic of land degradation zones is
represented in Fig. 3. The quantitative sizes of zones withhigh and low productivities essentially depend on seasonalweather conditions. An analysis of inter-seasonaldynamics of different zones’ squares ranked the vegetationseasons according to vegetation productivity. The mostfavorable year for vegetation productivity was 2002, whilethe worst was 2006. A significant territory increase in theZone A, and a corresponding contraction of the Zone E areobserved in years with favorable weather conditions. In
those years when the vegetative cover is exposed toweather stresses, the spatial desert zone expands while thehigh productivity zone decreases. Interstitial zones experi-ence less change.The steady growth of the area of zones with low IVI
value (deserts and semi-deserts) is notable. Conducting thezoning of Kazakhstan territory according to vegetativeproductivity allowed us: a) to identify areas with sustain-able vegetation degradation caused by variations inweather conditions, and b) to assess the productivity ofabandoned land in the major grain-producing regions ofNorthern Kazakhstan.
4 Discussion
4.1 Detection of sites with low levels of vegetativeproductivity
Desertification processes lead to the degradation ofvegetation cover, expressed by a reduction of total biomassand a vegetation type change. Remote sensing data can beused for early detection of foci of desertification, as well aschanges in vegetation over large, well-recorded areas fromspace.To analyze long duration changes of vegetation
productivity occurring in every zone, it is necessary toexclude seasonal weather impacts. For this purpose, a
Fig. 2 Configuration of different productivity zones for period 2000–2009
142 Front. Earth Sci. 2012, 6(2): 140–146
method of detection of “transit zone” with minimalweather impact was implemented. The locations of “transitzones” are shown in Fig. 4. The selected area is one of themost probable locations of seasonal and stable sites ofdeterioration of vegetation. It is important to note that thearea is less than 13% of the total area of Kazakhstan. Areaswith the increased anthropogenic influence, such asSemipalatinsk nuclear site, are not taken into account.Transition zones marked by satellite data correspond to
the dry-steppe and steppe agro-climatic zones of theRepublic defined by ground data (The National Atlas of theRepublic of Kazakhstan, 2006). The ongoing degradationof vegetation cover over a long period is one of the mostsensitive indicators defined by satellite climatic andanthropogenic influences. The transitional zone is one ofthe most probable locations of vegetation state degradationinduced by natural rather than anthropogenic factors.
Application of the method of designating territory of theRepublic into zones of varying vegetative productivityaccording to annual maps of IVI reveals seasonal centerswith low levels of vegetation. Next, the localization anddynamics of these zones were studied.Identification of sites of seasonal vegetation degradation
for the entire considered time was accomplished usingmaps of vegetation indices for moderate years with similarweather conditions, as shown in Fig. 5.It should be noted that these sites are located in the lower
part of the selected transition zone, mainly in the four areaswith approximate longitude coordinates: 52°– 54°Е, 62°–64°30′Е, 65°–66°Е, 71°–73°Е. Areas with low vegetationproductivity increased, thereby decreasing total biomass inthe transition zone (Fig. 6). There is a 5-fold increase in thesquares of areas with low vegetation productivity in 2000–2009.
Fig. 3 Dynamic of change of squares of zones with different productivities between 2000 and 2009
Fig. 4 Location of transit zone on Kazakhstan territory
Lev SPIVAK et al. The experience of land cover change detection by satellite data 143
4.2 Estimation of agricultural land productivity usingsatellite vegetation indexes
Non-irrigated spring grain crops are the foundation ofagricultural production in Kazakhstan. The Ministry ofAgriculture plans to subsidize the increase of sown areas innear future. Estimation of the productivity of agriculturallands is of great importance in the context of riskyagriculture, where natural factors (wind erosion, watererosion, etc.) can significantly change the quality of theland in a relatively short time period. Therefore, there is an
emerging problem of scientific substantiation whenselecting previously abandoned lands for spring crop use.Highly efficient methods for assessing the productivity ofland that do not require long-term ground surveys may bedeveloped based on satellite data. Accordingly, thedevelopment of methods of analyzing satellite data toevaluate the fertility of agricultural lands is of particularrelevance.Preliminary classification of agricultural land includes
the construction of masks of the main types of farmland(the current crop rotation, deposits, natural grass). Maskswere created based upon the separated high-resolutionscenes (IRS/LISS, 23 m) and on monitoring data withmedium resolution (MODIS, 250 m) in the period 2005–2008. Then, the originally obtained masks were adjusted tothe resolution of NDVI maps constructed from NOAAsatellite data. More details of this technique are describedin Spivak et al. (2009). Land productivity estimations werecarried out based on the archive of each decade’s quantitiesof NOAA/AVHRR/NDVI with resolution of 1 km, whichhave been formed since 2000. This information makes itpossible to determine places with relatively high yield andareas of degraded vegetation, which may be connected tomany factors— for example, wind erosion of topsoil.Figure 7 provides an example of masks of land created for
Fig. 5 Localization of sites with low values of the normalized IVI for years of similar and moderate weather conditions
Fig. 6 Dynamics of squares of sites with low values ofnormalized IVI for years of moderate weather conditions
Fig. 7 Masks of the main classes of agricultural lands to assess their productivity obtained from remote sensing data of the Kustanairegion: (a) agricultural land; (b) abandoned land
144 Front. Earth Sci. 2012, 6(2): 140–146
various purposes in the Kustanai Province in northernKazakhstan.Zoning on the above-described 5-point scale was
conducted within the distinguished land masks fordifferent purposes. It is notable that there are areas withlow levels of productivity in fields with free crop rotation,whereas there are areas with high productivity ofvegetation in the abandoned fields.Next, maps of high and low productivity of arable and
abandoned land vegetation are created using multi-yearseries of remote sensing, in which sites are ranked byproductivity. An example of such maps is shown in Fig. 8,where sections belonging to different classes of vegetationproductivity are displayed. The received estimates can beused to justify the expansion of cultivated areas and toprioritize the abandoned lands input in the rotation.
5 Conclusions
The technology of forming long-term series of vegetationindices is complex. It is based on remote sensing data fromlow resolution AVHRR/NOAA images. Satellite dataprovides a general integral evaluation of the productivityof large areas of land without the consideration of locallandscaping features of individual sites. Zoning theterritory of the Republic of Kazakhstan in terms ofproductivity of vegetation was achieved through the useof long-term distributions of vegetation indices. Thelocation of the zone with the highest probability ofidentifying sites of stable vegetation degradation wasdetected. The transition zone selected by using the satellitedata coincides with the dry steppe zone of Kazakhstan.The productivity of un-irrigated agricultural lands
depends significantly on seasonal weather conditions.
Therefore, in order to achieve an objective estimation ofland potential it is necessary to use long-term series ofremote sensing data. By using these, it is possible to definezones with consistently high yield as well as zones ofstable degradation of vegetative cover, which are asso-ciated with desertification. Additionally, high-resolutionsatellite imagery must be used in order to improve theaccuracy of individual fields’ and sites’ estimates.
Acknowledgements The authors express their gratitude to the SpaceResearch Institute of RK for the opportunity to conduct this study whichfunded by the Program for Basic Research.
References
Ichii K, Kawabata A, Yamaguchi Y (2002). Global correlation analysis
for NDVI and climatic variables and NDVI trends 1982–1990. Int J
Remote Sens, 23(18): 3873–3878
Kogan F (1995). Droughts of the Late 1980s in the United States as
derived from NOAA Polar Orbiting Satellite Data. Bul. Amer.
Meteor. Soc., 76(5): 655–668
Gitelson A A, Kogan F, Zakarin E, Spivak L, Lebed L (1998). Using
AVHRR data for quantitative estimation of vegetation conditions:
calibration and validation. Adv Space Research, 22(5): 673–676
Kogan F, Gitelson A, Zakarin E, Spivak L, Lebed L (2003). AVHRR-
based spectral vegetation index for quantitative assessment of
vegetation state and productivity: calibration and validation. Photo-
gramm Eng Remote Sensing, 69(8): 899–906
Spivak L, Terekhov A, Vitkovskaya I, Batyrbayeva M (2009). Use of
long-term satellite data of different resolutions for complex
estimation of the state of vegetative cover of Kazakhstan territory.
Modern Problems of Remote Sensing of Earth Institute of Space
Research of the Academy of Russian Sciences, 52–59
Spivak L, Vitkovskaya I, Batyrbayeva M (2008). Analysis of inter-
Fig. 8 Definition of sites with different vegetation productivity interpreted from remote sensing data of Kustanai Province: (a) lowproductivity, cropland; (b) high productivity, abandoned land
Lev SPIVAK et al. The experience of land cover change detection by satellite data 145
seasonal variations of productivity of vegetative cover of Kazakhstan
using temporal remote sensing rows. News of National Academy of
Sciences of Kazakhstan. Phys Math Ser, (4): 29–32
Spivak L, Batyrbayeva M, Vitkovskaya I, Mamedov B, Nurberdiev M,
Orlowsky L (2006). Analysis of inter seasonal dynamic of vegetation
for Turkmenistan territory. Problems of Desert, (4): 25–29
The National Atlas of the Republic of Kazakhstan (2006). Almaty I,
Natural Conditions and Resources.
Townshend J R G, Justice C (1986). Analysis of the dynamics of African
vegetation using the normalized difference vegetation index. Int J
Remote Sens, 7(11): 1435–1447
Tucker C J, Sellers P J (1986). Satellite remote sensing of primary
production. Int J Remote Sens, 7(11): 1395–1416
AUTHOR BIOGRAPHY
Spivak Lev is Ph.D. (Software computer systems, 1984) ofComputer Center of SO Russian Academy of Sciences, Novosibirsk,Russia. He is Prof. Dr. Science (Application of computers,mathematical modeling and mathematical methods in scientificresearch, 1996), Novosibirsk State University, Russia. Spivak Levhas been working: Deputy Director of Science, Space ResearchInstitute, Ministry of education and science of Kazakhstan (2003–2006); Deputy Director General of Science, Astrophysical ResearchCentre, Ministry of Education and Science of Kazakhstan (2006–2008); Vice-President of Science, National Center of SpaceResearches and Technologies, National Space Agency Republic ofKazakhstan (2008–2009); Director Space Research Institute,National Center of Space Researches and Technologies, NationalSpace Agency Republic of Kazakhstan (from 2010 to the presenttime). The main areas of professional interest: system analysis,mathematical modelling, expert system, intelligent informationsystems; methodology of environmental space monitoring; methodsand algorithms of thematic image processing. Email: [email protected].
Vitkovskaya Irina graduated from Kazakh State University,specialty of physician-hydrodynamics in 1978. She is Ph.D.(physical and mathematical sciences, 1991). Vitkovskaya has been
working at the Space Research Institute, National Center of SpaceResearches and Technologies, National Space Agency Republic ofKazakhstan, Almaty, since 1991. Vitkovskaya Irina is majorityresearch senior at the Department of Earth from 2008 to the presenttime. The main area of professional interest is the use of long-termsatellite data in the study of changes of ecosystem in Kazakhstan(vegetation, land use, water resources, etc.), space monitoring of theecological state of regions with high anthropogenic stress (Semi-palatinsk nuclear test site, the Aral Sea). Key publications related todetection of land cover change by long-time satellite data (differentvegetation indexes). Email: [email protected].
Batyrbayeva Madina graduated from Kazakh National TechnicalUniversity (Economics and Management in Geology). She is Ph.D.(Technical science, 2007). Batyrbayeva has been working at theSpace Research Institute, National Center of Space Researches andTechnologies, National Space Agency Republic of Kazakhstan,Almaty, since 1998. She is Head of Laboratory of Space Monitoringof Long-term Changes from 2008 to the present time. Her researchinterests include study and prediction of long-term environmentalchanges, both in global and regional scales using software andtechnological applications and methods of remote sensing. Email:[email protected].
Terekhov Alexey graduated from National State Al-Farabi Uni-versity, Kazakhstan, in 1982 (Specialty of chemistry). He receivedhis Ph.D. in technical sciences (2007) from Institute of ApplyMathematics, Ministry of Education and Sciences, Kazakhstan,Almaty. Professional experience: Senior Researcher, Space ResearchInstitute, National Center of Space Researches and Technologies,National Space Agency Republic of Kazakhstan (1991–2010);Senior Researcher, Kazakh Research Institute of Ecology andClimate, Ministry of Environment of Republic of Kazakhstan.Scientific Positions and Memberships: - Scientific expert of LandProduct Validation subgroup, Committee on Earth ObservationSatellites, NASA, USA - (since 2008); - Member ISPRS Commis-sion VIII, WG VIII/6 [agriculture, ecosystems and bio-diversity] -(since 2009). His research interests include methods of remotesensing, landcover/landuse, agriculture monitoring. Email: [email protected].
