Land Cover Mapping TrainingManual Using eCognition Draft May … · 2015-05-28 · Land cover...

Training Manual

Land cover mapping using satellite dataDraft

i Land cover mapping using satellite data

Contents

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

1.1 Legend development and classification scheme .............................................. 3

1.2 Available satellite images and their spatial and spectral resolutions .................. 4

2. Fundamental concepts in satellite image classification ............... 6

2.1 Image ratios for information extraction .......................................................... 7

3. Methodology of image analysis for land cover mapping ........... 8

3.1 Image rectification ...................................................................................... 8

3.2 Image classification ..................................................................................... 9

3.3 Object-based image analysis (OBIA) ........................................................... 10

3.4 Accuracy assessment ................................................................................. 12

3.5 Land cover change analysis ....................................................................... 13

4. Hands on Exercise Using eCognition Developer ....................... 18

4.1 Getting started .......................................................................................... 18

Create a New Project .......................................................................................... 19

Subset Selection.................................................................................................. 21

Insert Thematic Layer ........................................................................................... 22

4.2 Image Objects by Segmentation ................................................................. 24

4.3 Classification of Land Cover Using Landsat ETM+ Image ............................... 25

View Settings Toolbar .......................................................................................... 27

Insert Rule for Object Creation ............................................................................. 28

Create Relational Feature ..................................................................................... 31

Land & Water Mask (LWM) ................................................................................. 32

Insert the Class/Class Hierarchy ........................................................................... 33

Insert a Classification Parent Process ..................................................................... 33

4.4 Manual Editing ......................................................................................... 60

4.5 Annex I: Information on Landsat and Indices ................................................ 66

5. References and Useful links ...................................................... 68

ii Land cover mapping using satellite data

1 Land cover mapping using satellite data

1. Introduction

The Hindu Kush-Himalaya (HKH) is the youngest and one of the most fragile mountain ecosystems in the world. The HKH region encompasses about 3500 kilometers east to west and includes eight regional member countries (RMCs) namely- Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal and Pakistan. It is the youngest, highest and one of the most fragile mountain systems of the world. It is estimated that more than 1.3 billion people are directly dependent upon the ecosystem services provided by these mountains. Most mountain communities are dependent upon subsistence agriculture and natural resources. More recently, climate change has put the Himalayan region in centre of the international attention - as one of the most vulnerable ecosystems in the world severely impacting its social and environmental security of the region. The vulnerability of the HKH region, to the effects of socio-economic development in the region together with degradation caused by the improper use of natural resources, becomes a critical element which needs to be assessed in the planning and management of sustainable mountain development in the region. The best indicator to monitor these vulnerabilities is land cover and use, and its dynamics over time.

Distribution and changes in land cover affect the ecosystem services (e.g., provision of food and fiber, sustaining biodiversity, providing recreational places etc.) of an area, induce climate changes by modifying water and energy exchanges with the atmosphere, and distort greenhouse gas balances. Thus land cover information is a key input to a wide range of interventions on issues of national to global interest, including land degradation, climate change, food security, poverty and environmental sustainability. In addition many of the climate variables that are difficult to measure at large scale can be partly inferred by interpreting the vegetation and land-surface types. Thus, land cover can serve as proxy to other important climate variables.

Land cover refers to the physical and biological cover over the surface of land, including water, vegetation, bare soil, and/or artificial structures. Land use denotes how humans use the biophysical or ecological properties of land. Land use is characterized by the arrangements, activities and inputs people undertake in a certain land cover type to produce, change or maintain it. Definition of land use in this way establishes a direct link between land cover and the actions of people in their environment. Information on land use and land cover is required in many aspects of sustainable management of land resources and policy development, as a prerequisite for monitoring and modelling land use and environmental change, and as a basis for land-use statistics at all levels (Jansen & Di Gregorio 2004).

Land cover-land use analysis provides knowledge about landscape patterns and their changes which over time gives very important insights into the ongoing natural and human processes in the ecosystem. Human activities are a major factor contributing to global change, and they are overriding natural changes to ecosystems brought on by climate variations (Figure 1). On the local level land use changes can fundamentally alter the availability of natural inputs to ecosystems (energy, water, and nutrients), generate exotic


species invasions, and accelerate natural processes of ecosystem change (Ojima et al., 1994).

Figure1: Processes and impact of land use changes (Ojima et al. 1994)

The demand of information on land cover, land use and their changes is increasing at the global, regional and national levels to support policy decisions and regulate management processes.

In contrast to survey data, aerial and satellite imagery can be used to monitor the spatial extent of changes in land cover (i.e., conversion) or land conditions. Satellite imagery offers contiguous spatial coverage, facilitates better repetition and replaces costly and slow data collection of different land cover and provides statistical information of the area /object. Remote sensing technologies have made substantial contribution in deriving land cover information and correlating to land-use statistics (Jansen &Di Gregorio 2004). Availability of satellite images with different spatial and temporal resolutions have made it possible to map land cover at different scales and carry out analyses of the changes over last three decades. Specifically, remote sensing change detection analyses can be used to identify areas of rapid change to target management efforts (Rogan et al 2002; Coppin et al 2004; Kennedy et al 2009). Repeated satellite images and/or aerial photographs are useful for both visual assessment of natural resources dynamics occurring at a particular time and space as well as quantitative evaluation of land cover changes (Tekle and Hedlund 2000). A number of deforestation and degradation studies have been conducted in tropical forests using coarse and high-resolution remote sensing data (Qamer et al 2013, Panta et al 2008; Gautam et al 2004; Jha et al 2000; Skole and Tucker 1993). The temporal evaluation of forest changes based on satellite imagery is becoming a valuable set of technique for assessing the degree of threat to ecosystem (Giriraj et al


2008; Reddy et al 2008). GIS on the other hand provides environment to analyze digital data useful for change detection, database development, and modeling of its future change and data dissemination for effective management planning. In context of Reducing Emissions from Deforestation and Degradation (REDD), Remote-sensing methods are considered to be appropriate for most developing countries to assess historical and future deforestation rates, i.e., forest area change (GOFC-GOLD 2009).

Study of land cover dynamics involves developing a multi temporal land cover database using satellite images of different dates. In this context, a uniform/comparable land cover legend is essential for meaningful comparisons between different time periods. The land cover mapping have to be useful for applications at different scales and therefore it is important to design a system which follows a uniform approach and allows aggregation at different levels of detail. Harmonization of the classification system is therefore an important step; it will not only facilitate the generation of land cover maps that are compatible with each other but also can be used consistently for change studies. Land cover mapping requires significant resources and due to the gaps in harmonized legends and methodologies, investments in past initiatives could not be properly used for change studies which are crucial for decision support for key applications. In order to make the current efforts sustainable, the capacity building of the partners in the regional member countries is an important step to utilize common approach and methodology to develop regional land cover database periodically. Such a database is crucial for assessing key drivers for land cover and land use change and decision-making.

1.1 Legend development and classification scheme

The definition of land cover is fundamental, because in many existing classifications and legends it is confused with land use. A classification describes the systematic framework with the names of the classes and the criteria used to distinguish them, and the relation between classes. Classification thus necessarily involves definition of class boundaries that should be clear, precise, possibly quantitative, and based upon objective criteria. Understanding of land use and cover is essential to understand landscape patterns and their change in past and for that harmonizes and standardizes classification is very important (Birendra et al 2010). In order to address these differences, a number of organizations and institutions are working to create general classification systems and legends for global consistency, such as terrestrial ecoregion. To address this urgent need, the Food and Agriculture Organization (FAO) has developed a system for land cover classification (Di Gregorio 2005). The International Centre for Integrated Mountain Development initiated research to harmonize land cover classification at the regional scale and address the immediate needs of the Hindu Kush Himalayan Region. Harmonization of the classification system is facilitating the generation of land cover maps that can be used consistently for studies of change. The table 1 below shows example harmonized land cover legend developed using a Land Cover Classification System (LCCS).


Table 1: legend of land cover for different levels

Level-1 Level-2 Level-3

Primary vegetated Area

Terrestrial

Cultivated Managed

Agriculture

Valley cultivation

Mixed crops

Level terrace Paddy Current fallow

Natural and Semi Natural Vegetation

Forest

Needleleaved forest

Needleleaved open forest Needleleaved closed forest

Broadleaved forest

Broadleaved closed forest Broadleaved open forest

Mixed Forest Mixed open forest

Mixed closed forest

Shrub land

Needleleaved Shrub

Needleleaved open Shrub Needleleaved closed Shrub

Broadleaved shrub

Broadleaved closed shrub Broadleaved open shrub

Mixed Shrub Mixed closed shrub

Mixed open shrub

Grass land

Needleleaved grass

Needleleaved grass

Broadleaved grass

Broadleaved grass

Primary non vegetated Area

Terrestrial

Artificial surface

Settlement Settlement Settlement Road Road Road

Bare area Bare rock Bare rock Bare rock Bare Soil Bare Soil Bare Soil

Aquatic/Regularly Flooded

Natural Waterbodies, snow and ice

River River River River bed River bed River bed

Snow/ice Snow/ice Snow/ice

1.2 Available satellite images and their spatial and spectral resolutions

Land cover maps derived from satellite images play a key role in global, regional, national and subnational land cover assessments. Land cover map is available in a range of data formats and spatial resolutions to suit different user requirements. According to the study area size and details of mapping selection suitable satellite image are important. Presently satellite imagery became widely available when affordable. The relevance of different


satellites and information which can be generated to suit different scales of land cover assessment are given in Table- 2.

Table 2: Commonly used satellite imagery with resolution and scale

Sl No

Launch Satellite/ Sensor

Band/Resolution Scale Level*

Quicklook

1 Jun 2014 WorldView-2

Very high-resolution with 8 Band (Pan and Multi)* - Panchromatic 31 cm - Multispectral 1.24 m - Short-wave infrared 3.7 m

5000 4

2 Oct 2009 WorldView-

2 Very high-resolution with 8 Band (Pan and Multi)* -Panchromatic 46 cm -Multispectral 1.85 m (red, blue, green, near-IR, red edge, coastal, yellow, near-IR2)

5000 4

3 Oct 2001 Quickbird High-resolution with 5 Band

(Pan and Multi)* - Panchromatic 61 cm - Multispectral 2.44 m

10000 3

4 Sep 2009 Cartosat-2 High-resolution

-Panchromatic 1 m 10000 3

5 Jan 2006 ALOS(PRISM,

AVNIR, PALSAR)

High-resolution -Panchromatic 2.5m -Multispectral 10m

50000 2

6 Oct 2003 IRS LISS IV

MX High medium resolution -Multi (Green, Red and NIR) 5.8 m

50000 2

7 Apr 2009 Landsat7

ETM+ Medium-resolution with 8 Band (Pan and Multi)* -Panchromatic 150m -Multispectral 30m (TR 60m)

100000 1

8 Apr 2009 Landsat5 TM Medium-resolution with 7 Band

-Multispectral 30m (TR 120m) 100000 1

* Land cover map legends at different scales


2. Fundamental concepts in satellite image classification

The basic principle behind these assessments based on passive remote sensing is exploitation of electromagnetic spectrum. Energy from the sun comes in many different wavelengths. Various features on earth’s surface respond differently to different wavelengths. In simple feature has its own "signature".

Different surface types such as water, bare ground or vegetation reflect radiation differently in various channels. The radiation reflected as a function of the wavelength is called the spectral signature of the surface.

A. Graphs of spectral signatures of water, soil and vegetation. Vegetation has a remarkably high reflection in the near infrared channel 4 and a low reflection in the visible red channel 3. This makes it possible to distinguish vegetation areas from bare ground. The difference of reflection in channels 3 and 4 is great for vegetation areas and insignificant for bare ground.

B. The spectral signatures are processed as digital values in the satellite scanner. Here is a hypothetical example of how the LANDSAT satellite might record water, green vegetation and bare ground.

The reflection from bare ground increases slightly from the visible to the infrared range of the spectrum. There are great differences between different types of soil, dry and humid land. Different mineral compositions of the surface are also reflected in the spectral signature.

Generally, water only reflects in the visible light range. As water has almost no reflection in the near infrared range it is very distinct from other surfaces. Thus water surfaces will be clearly delimited as dark areas (low pixel values) in images recorded in the near infrared range.

The spectral signature for green plants is very characteristic. The chlorophyll in a growing plant absorbs visible and especially red light to be used in photosynthesis, whereas near infrared light is reflected very effectively as it is of no use to the plant, see illustration. In this way the plants avoid unnecessary heating and loss of juice through evaporation. Therefore the reflection from vegetation in the near infrared and in the visual ranges of the spectrum varies considerably. The degree of difference reveals how large a part of the area is covered with growing green leaves (leaf area index).

Different surface types such as water, bare ground or vegetation reflect radiation differently in various channels. The radiation reflected as a function of the wavelength is called the spectral signature of the surface.


2.1 Image ratios for information extraction

Image ration is very important to during the image classification. Image rationing is a “synthetic image layer” created from the existing bands of a multispectral image. This new layer often provides unique and valuable information not found in any of the other individual bands. Image index is a calculated results or generated product from satellite band/channels. It is help to identify different land cover from mathematical definition.

Normalized difference vegetation index (NDVI) One of the commonly used indices is NDVI. NDVI is related to vegetation is that healthy vegetation reflects very well in the near infrared part of the spectrum. NIR stands for Near Infrared.

NDVI = (NIR - red) / (NIR + red)

NDVI index values can range fro

NDVI index values can range from -1.0 to 1.0, but vegetation values typically range between 0.1 and 0.7. Free standing water (oceans, seas, lakes and rivers) which have a rather low reflectance in both spectral bands and thus result in very low positive or even slightly negative NDVI values. Soils which generally exhibit a near-infrared spectral reflectance somewhat larger than the red, and thus tend to also generate rather small positive NDVI values (say 0.1 to 0.2).

Normalized Difference Snow Index (NDSI) Difference Snow/Ice Index calculations are related to reflections different bands. Snow and ice have very high reflectance values in visible spectral bands (blue, green and red),

Figure 1: Normalized difference vegetation index of ETM+ image Figure 2: Satellite image and NDVI of Sagarmatha National Park


but very low reflectance in mid-infrared band. The value is then normalized to the range -1<=NDVI<=1 to partially account for differences in illumination and surface slope. All the snow will carry positive value.

NDSI = (green – IR) / (green + IR)

3. Methodology of image analysis for land cover mapping

The generic steps followed in land cover change assessment using satellite is given below

3.1 Image rectification

Pixels on raw satellite remote sensing images only have row, column coordinates; that is, they do not have geographic coordinates such as latitude-longitude or state plane coordinate system or raw imagery has no reference to the ground. Remote sensing images display varying degrees of geometric and location distortion. The rectification of an image transforms it to display a plane object in the image as if the picture has been taken directly

Image classification scheme

Data acquisition

Image rectification and enhancement

Field training information

Image segmentation

Generate image indices

Assign rules

Land cover map

Accuracy assessment and validation

Finalization of land cover

Figure 3: Flow diagram of land cover mapping


from the satellite. Rectification is the process of giving an image a real World coordinate system. After rectification the scale of the image is uniform, and it's the real scale, distances on the object can be measured directly on the picture.

3.2 Image classification

Image analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. The process of sorting pixels into a number of data categories based on their data file values and reducing images to information classes. Image classification refers to the task of extracting information classes from a multiband raster image. The resulting raster from image classification can be used to create thematic maps. Image classification in the spectral domain is known as pattern recognition in which the decision rules are based solely on the spectral values of the remote sensing data. In spatial pattern recognition, the decision rules are based on the geometric shape, size, texture, and patterns of pixels or objects derived from them over a prescribed neighborhood. In order to take advantage of and make good use of remote sensing data to developing land cover, extract meaningful information from the imagery is very important. During the image classification following interpretation factors need to consider:

Tone: Tone refers to the relative brightness or color of objects in an image. Generally, tone is the fundamental element for distinguishing between different targets or features. Variations in tone also allow the elements of shape, texture, and pattern of objects to be distinguished.

Shape: Shape refers to the general form, structure, or outline of individual objects. Shape can be a very distinctive clue for interpretation. Straight edge shapes typically represent urban or agricultural field targets, while natural features, such as forest edges, are generally more irregular in shape,

Size: Size of objects in an image is a function of scale. It is important to assess the size of a target relative to other objects in a scene to aid in the interpretation of that target. Pattern: Pattern refers to the spatial arrangement of visibly discernible objects. Typically an orderly repetition of similar tones and textures will produce a distinctive and ultimately recognizable pattern. Orchards with evenly spaced trees, and urban streets with regularly spaced houses are good examples of pattern.

Texture: Regular arrangement of ground objects. Examples are urban area and rural agriculture area arrangement on satellite imagery.

Shadow : Shadow is also helpful in interpretation as it may provide an idea of the profile and relative height of a target or targets which may make identification easier. Example is on the very high resolution trees will have shadow and shrub will have without shadow.


Association: Association takes into account the relationship between other recognizable objects or features in proximity to the target of interest. The identification of features that one would expect to associate with other features may provide information to facilitate identification.

Depending on the interaction between the analyst and the computer during classification, there are different types of classification procedures:

Unsupervised Supervised Knowledgebase Object base Others

3.3 Object-based image analysis (OBIA)

Classification is producing meaningful material distribution maps via identification of individual pixels or groups of pixels with similar spectral responses to incoming radiation. These pixels or groups represent different materials or classes. Object-Based Image Analysis (OBIA) – also called Geographic Object-Based Image Analysis (GEOBIA)". OBIA is a sub-discipline of geoinformation science devoted to partitioning remote sensing imagery into meaningful image-objects, and assessing their characteristics through spatial, spectral and temporal scale". The fundamental step of any object based image analysis is a segmentation of a scene—representing an image—into image objects. Thus, initial segmentation is the subdivision of an image into separated regions represented by basic unclassified image objects called ‘Image Object Primitives’.

Typically; an “Object Based Image Analysis” starts with a crucial step of Image Segmentation in which meaningful image object are created then these image objects are classified in the later step of classification. In the field of forestry; object-based techniques such as per-field or per-parcel classification (Aplin et al., 1999); and point-based segmentation algorithm (Heyman et al. (2003),) have been evolved to delineate image-objects or zones of contiguous pixels belonging to same class. Object-based image analysis provides significant information about forest ecosystem (Mallinis et al., 2008).

Initially, image objects are constructed by image segmentation, which is the subdivision of an image into separate multi-pixel regions. (Benz et al. 2004). The most frequently used segmentation algorithm ‘Multiresolution Segmentation’ has been adopted to generate image object (Kozak et al., 2008; Mallinis et al., 2008; Mathieu et al. 2007; Mather, 2004; Flanders et al., 2003; Manakos et al. 2000; Hay et al., 2005; Santos et al., 2006). ‘Multiresolution segmentation algorithm’ is based on a pair wise region merging technique in which pixels (one pixel object) or existing image object are successively


merged (Zhou et al., 2008). Definiens (eCognition) provides segmentation on several scales that is very important for a meaningful image analysis. (Benz et al. 2004).

Segmentation on several scales with different scale parameters can be carried out leading to the formation of a hierarchical network of objects. This procedure is constrained so that spatial shape of objects in one level fits hierarchically into objects of another level enabling consideration of sub-objects and super-objects and their mutual relationships in the classification step (Mallinis et al., 2008).

Figure 4: Flow diagram of Hierarchal Object based Image classification scheme

Segmentation

Build classification Hierarchy

Classification

Classification-based Segmentation

Classification

Accuracy Assessment

Vegetated Non-Vegetated

Farmland Natural vegetation

Bare Area

Water/ Snow Area

Vegetated Non-Vegetated

Farmland

Natural vegetation

Bare Area

Water/

Snow Area

Identification based on indices

Identification based on scale, shape


Classification Assumptions

• Similar features will have similar spectral responses. • The spectral response of a feature is unique with respect to all other features of

interest. • If we quantify the spectral response of a known feature, we can use this

information to find all occurrences of that feature.

Figure 5: Class Hierarchy and respective classified objects

3.4 Accuracy assessment

Accuracy assessments determine the quality of the information derived from remotely sensed data. The product of image classification is land cover maps. Their accuracy needs to be assessed so that the ultimate user is made aware of the potential problems associated with their use. Accuracy assessment is a quality assurance step in which classification results are compared with what is there on ground at the time of imaging or something that can be regarded as its acceptable substitute, commonly known as the ground reference. Evaluation of the accuracy of a classification may be undertaken for each of the categories identified and its confusion with other covers, as well as for all the


categories. The outcome of accuracy assessment is usually presented in a table that reveals accuracy for each cover category and for all categories as a whole.

A sample accuracy assessment report

3.5 Land cover change analysis

Reliable information on land cover (LC) and land-cover change is required for a wide range of environment-related topics and activities, including forest management, which has changed dramatically in the last years. Determining the effects of land-use and land-cover change on the Earth system depends on an understanding of past land-use practices, current land-use and land-cover patterns, and projections of future land use and cover, as affected by human institutions, population size and distribution, economic development, technology, and other factors. The combination of climate and land-use change may have profound effects on the habitability of Earth in more significant ways than either acting alone. While land-use change is often a driver of environmental and climatic changes, a changing climate can in turn affect land use and land cover. Climate variability alters land-use practices differently in different parts of the world, highlighting differences in regional and national vulnerability and resilience.

Land-cover changes can be driven by anthropogenic and natural alterations. Alterations in LC affect the regional or global energy balance, hydrologic cycle, biogeochemical cycles, and climate. Land-cover change can be driven by:

• climate-related change (both long and short term); • burning whether deliberate or wildfire; • cutting and clearing of forests (deforestation); • grazing activities (intensification of rangeland use);


• agricultural encroachment (farming activities) • abstraction for fuelwood; • urban expansion (urbanization);

It is possible to monitor human and natural changes in the landscape, such as deforestation, change in agricultural land use pattern, desertification, flooding, soil erosion, plant community change and sub-urbanization or urbanization, using remotely sensed products where the detection of changes can be highlighted using cyclical satellite passages. The ability to detect change is, in fact, one of the benefits of remote sensing (RS) because the use of multitemporal data sets permits discrimination of areas of change between imaging dates.

Change detection analysis with RS depends upon the ability to isolate one ‘type’ of change from among numerous changes taking place at different temporal and spatial scales (Lillesand, Kiefer and Chipman, 2004). A fundamental assumption in digital change detection is that a difference exists in the spectral response from two dates if the biophysical material within the Instantaneous Field of View (IFOV) has changed between dates (Jensen, 1996).

Change detection involves the use of multitemporal or multi-type data sets, or a combination, to discriminate areas of Land Cover between dates of imaging. It requires a very accurate choice of products to be used for the specific scope, and consequently the images chosen must undergo accurate co-registration. The land cover change interpretation process must result in a systematic description of the changes present in the study area. Change characterization can mean not only identifying intensification or reduction of LC and other land use, but also evaluating the type and the main peculiarities of changes so as to be able to understand the induced consequences on the environment. Characterizing change has certain implications, considered below.

Analysis of present features: The identification of the present LC features can be considered the first task in the process.

Detecting areas of change: The identification of areas of land-cover change in relation to a certain previous period is the second task of the research. The area of change must be delimited in order to define the exact extension and the correct location of changes in the study area.

Magnitude of change: Together with the physical extent, the intensity of change must be defined. Magnitude is quite difficult to quantify, but, simplistically, two main categories can be used for identification purposes, namely change and modification.

Three main factors control change detection accuracy.

• Spatial properties: Image resolution controls the minimum size of detectable changes determined by the IFOV. Also, the quality of the geometric registration controls the minimum size of detectable changes by adding an error term to resolution. It is strongly affected by pixel size.


• Radiometric and spectral properties (bandwidth and radiometric resolution): Contrast between cover types is dependent on bandwidth and location of the bands. These factors delimit the types of land-cover changes that can be detected. Radiometric resolution controls the size of radiance differences that are detectable.

• Temporal properties: The time interval between images and frequency of imaging must take into account not only orbiting frequency but also cloud and haze cover frequency. Moreover, the re-visit interval affects how small a change can be detected (i.e. the size of changes it is possible to detect).



Hands on Exercise


4. Hands on Exercise Using eCognition Developer

4.1 Getting started

• Go the Windows Start menu and Click Start > All Programs> eCognition Developer

8.0> eCognition Developer

Upon launching Definiens eCognition Developer 8, the following dialog appears:


Figure: eCognition 8 launch screen

Figure 6: The default display eCognition 8

Create a New Project

• To Create New Project do the following:

• Choose File > New Project on the main menu bar.


• Navigate the folder C:\GISRS_Trn\Definiens

• Select Image.img > Open (Here is image file Landsat ETM+, R136/P44)

• Then select from the appropriate file in the files type.

To open some specific file formats or structures, you have to proceed as follows:

• First select the correct driver in the Files of type drop-down list box

•Double-Click on Image Layer Alias Rename the all layers name

• Click> OK

• Click> Insert > Select DEM.img and Slope> Open


• Double-Click on Layer Alias Rename the all the layers name Layer 1 (Blue), Layer 2 (Green), Layer 3 (Red), Layer 4 (Near IR), Layer 5 (Mid-IR), Layer 7 (Mid-IR), Layer 8 (DEM), Layer 8 (Slope)

• Click > File> Save Project > Test.dpr

Subset Selection

Normally, image files are large in size and difficult to process. So we will be working with a smaller area to manage easily, which will take less memory and time. You can crop your image on the fly in the viewer by using Subset option without changing your original image file. You can create a "subset selection" when you start a project or during modification.

To open the Subset Selection dialog box, do the following:

• After importing image layers press the Subset Selection button.


• Click on the image and Drag to select a subset area in the image viewer.

• Alternatively, you may enter the subset coordinates. You can modify the coordinates by typing.

• Confirm with OK to return to the superordinate dialog box.

• You can clear the subset selection by Clicking Clear Subset in the superordinate dialog box.

Insert Thematic Layer

Geographic representations are organized in a series of data themes, which are known as thematic layers. During the image classification with eCognition, you can insert shape file as a thematic layer and you can also use it in the process of image classification (if required).

During the new project creating or modifying time, Shape files or other vector files can be inserted to viewer. To insert a thematic layer, do the followings:

• Click the Insert button


• Choose Thematic Layers > Insert on the menu bar of the dialog box.

• Right-Click inside the thematic layer list and choose Insert from the context menu.

The Import Thematic Layer dialog box opens, which is similar to the Import Image Layers dialog box.

Modify a Project

Using Modify a Project you can add/remove more image or thematic layer or you can rename project. Modify a selected project by exchanging or renaming image layers or through other operations.

To modify a project, do the following

Open a project and choose File > Modify Open Project on the main menu bar.

• The Modify Project dialog box opens.

• Modify the necessary things

• Click OK to modify the project

• Save a Project

Save the currently open project to a project file (extension .dpr).

To save a project, do the following:

• Choose File > Save Project on the main menu bar.

• Choose File > Save Project As… on the main menu bar. The Save Project dialog box opens. Select a folder and enter a name for the project file (.dpr). Click the Save button to store the file.


4.2 Image Objects by Segmentation

The fundamental step of any eCognition image analysis is to do segmentation of a scene— representing an image—into image object primitives. Thus, initial segmentation is the subdivision of an image into separated regions represented by basic unclassified image objects called image object primitives.

For successful and accurate image analysis, defining object primitives of suitable size and shape is of utmost importance. As a rule of thumb, good object primitives are as large as possible, yet small enough to be used as building blocks for the objects to be detected in the image. Pixel is the smallest possible building block of an image, however it has mixture of information. To get larger building blocks, different segmentation methods are available to form contiguous clusters of pixels that have larger property space.

Commonly, in image processing, segmentation is the subdivision of a digital image into smaller partitions according to given criteria. Different to this, within the eCognition technology, each operation that creates new image objects is called segmentation, no matter if the change is achieved by subdividing or by merging existing objects. Different segmentation algorithms provide several methods of creating of image object primitives.

The new image objects created by segmentation are stored in a new image object level. Each image object is defined by a contiguous set of pixels, where each pixel belongs to exactly one image object. Each of the subsequent image object related operations like classification, reshaping, re-segmentation, and information extraction is done within an image object level. Simply said, image object levels serve as internal working areas of the image analysis.

Quad Tree Based Segmentations

Multiresolution Segmentations


4.3 Classification of Land Cover Using Landsat ETM+ Image

Image Classification is a process of sorting pixels into a number of data categories based on their data file values and reducing images to information classes. Similar features will have similar spectral responses. The spectral response of a feature is unique with respect to all other features of interest. If we quantify the spectral response of a known feature in an image, we can use this information to find all occurrences of that feature throughout the image.

Display the Image or Edit the Image Layer Mixing

Display the Image or Edit the Image Layer Mixing is one kind of band combination process. Often an image contains valuable information about vegetation or land features that is not easily visible until viewed in the right way. For this reason, in eCognition, you have to use Display the Image or Edit the Image Layer Mixing. The most fundamental of these techniques is to change the arrangement of the bands of light used to make the image display. In order to display an image in eCognition, assigns one or RGB color to each of up to three bands of reflected visible or non-visible light.

You have the possibility to change the display of the loaded data using the ‘Edit Layer Mixing’ dialog box. This enables you to display the individual channels of a combination.

• To open the ‘Edit Image Layer Mixing’, do one of the following:

• From the View menu, select Image Layer Mixing

• Click View > Image Layer Mixing on the main menu bar.

Or Click on the Edit Image Layer Mixing button in the View Settings toolbar.


Figure 7: Edit Image Layer Mixing dialog box. Changing the layer mixing and equalizing options affects the display of the image only

Choose a layer mixing preset:

• (Clear): All assignments and weighting are removed from the Image Layer table • One Layer Gray displays one image layer in grayscale mode with the red,

green and blue together • False Color (Hot Metal) is recommended for single image layers with large

intensity ranges to display in a color range from black over red to white. Use this preset for image data created with positron emission tomography (PET)

• False Color (Rainbow) is recommended for single image layers to display a visualization in rainbow colors. Here, the regular color range is converted to a color range between blue for darker pixel intensity values and red for brighter pixel intensity values

• Three Layer Mix displays layer one in the red channel, layer two in green and layer three in blue

• Six Layer Mix displays additional layers. • For current exercise change the band combinations (B7, B2, and B1) and

Equalizing Histrogram any others • Click> OK


Create Image Objects

The fundamental step of any eCognition image analysis is a segmentation of a scene—representing an image—into image objects. Thus, initial segmentation is the subdivision of an image into separated regions represented by basic unclassified image objects called ‘Image Object Primitives’.

View Settings Toolbar

The View Settings Toolbar buttons, numbered from one to four, allow you to switch between the four window layouts. These are Load and Manage Data, Configure Analysis, Review Results and Develop Rule Sets. As much of the User Guide centers around writing rule sets – which organize and modify image analysis algorithms – the view activated by button number four, Develop Rule Sets, is most commonly used

In the ‘View Settings’ toolbar there are 4 predefined View Settings available, specific to the different phases of a Rule Set development workflow.

View Settings toolbar with the 4 predefined View Setting buttons: Load and Manage Data, Configure Analysis, Review Results, Develop Rule Sets.

Select the predefined View Setting number 4 ‘Develop Rulesets’ from the ‘View Settings’ toolbar.

For the ‘Develop Rulesets’ view, per default one viewer window for the image data is open, as well as the ’Process Tree’ and the ‘Image Object Information’ window, the ‘Feature View’ and the ‘Class Hierarchy


Insert Rule for Object Creation

This is the first step of image classification in eCognition. This is a kind of assigning condition/s. Based on this, it will create image objects or segments. Within the rule sets, you can use variables in different ways. While developing rule sets, you commonly use scene and object variables for storing your dedicated fine-tuning tools for reuse within similar projects.

Insert a Process

Insert a Parent Process A parent process is used for grouping child processes together in a hierarchy level. The typical algorithm of the parent process is "Execute child process".

• To open the Process Tree window Click Process> Process Tree

• Go to the Process Tree window, which might be empty since you did not put any process yet.

Insert a Segmentation Parent Process

• Right-Click in the ‘Process Tree’ window and select ‘Append New’ from the context menu.

New Dialog box (Edit Process) will be appeared.


In the ‘Name’ field enter the name ‘Segmentation’ and confirm with ‘OK’. It will be your Parents of Segmentation.

Insert a Child Process ( Multiresolution Segmentation) The child processes algorithm in conjunction with the no image object domain to structure to your process tree. A process with this setting serves as a container for a sequence of function related processes.

The first crucial decision you have to make is which algorithm to be used for creating objects. The initial objects you create will be the basis for all further analysis. Multiresolution Segmentation creates groups of areas of similar pixel values into objects. Consequently, homogeneous areas result in larger objects, heterogeneous areas in smaller ones.

• Select the inserted Segmentation Process and Right-Click on it. Choose ‘Insert Child’ form the context menu.

• Click Algorithm > Select Multiresolution Segmentations

• Give the level name (Level-1)


• Change the image layer weights

• Change the scale parameter and etc.

• Click > OK

Which layers to be used for creating Objects?

The basis of creating image objects is the input-data. According to the data and the algorithm you use, objects results in different shapes. The first thing you have to evaluate, which layers contain the important information. For example, we have two types of image data, the Image and the DEM. In most Segmentation algorithms you can choose whether you want to use all data available or only specific layer. It depends on where the important information is contained. In our case, we want to use VIS and NIR band for image object creation.

Which Scale Parameter to be set?

The ‘Scale parameter’ is an abstract term. It is the restricting parameter to stop the objects from getting too heterogeneous. For the ‘Scale parameter’ there is no definite rule, you have to use trial and error to find out which ‘Scale parameter’ results in the objects is useful for your further classification.


• Right-Click one the process and select execute to execute the Multiresolution Segmentation process.

Create Relational Feature

• To open the Relational Feature window, Click Tools> Feature View

Feature View will be appeared.

• Double-Click on Create new ‘Arithmetic Feature’, Edit Customize Feature will be appeared

• Assign the Feature name > NDVI

The Normalized Difference Vegetation Index (NDVI) is a simple numerical indicator that can be used to analyze remote sensing measurements. NDVI is related to vegetation, where healthy vegetation reflects very well in the near infrared part of the spectrum. Index values can range from -1.0 to 1.0, but vegetation values typically range between 0.1 and 0.7.

Free standing water (ocean, sea, lake, river, etc.) gives a rather low reflectance in both spectral bands and thus result in very low positive or even slightly negative NDVI values.

Soils which generally exhibit a near-infrared spectral reflectance somewhat larger than the red, and thus tend to also generate rather small positive NDVI values (say 0.1 to 0.2).


NDVI (ETM+) = (Band 4 - Band 3) / (Band 4 + Band 3)

• Double-Click on Layer Values and then Mean Layer appear

• Double-Click on Landsat ETM+ band and complete the formula for NDVI

For NDVI = ([Mean Layer 4 (Near IR)]-[Mean Layer 3 (Red)])/([Mean Layer 4 (Near IR)]+[Mean Layer 3 (Red)])

• Click > OK


Land & Water Mask (LWM)

Land and Water Mask index is a very useful tool to differentiate between land and water. This is very important variable to classify all type of waterbodies. Index values can range from 0 to 255, but water values typically range between 0 and 50.

Water Mask = (MIR) / (Green) * 100

• Assign the Feature name > Land & Water Mask

Land & Water Mask (LWM) = [Mean Layer 5 (Mid-IR)]/([Mean Layer 2 (Green)])*100

• Click > OK


Insert the Class/Class Hierarchy

New Dialog box will be appear

• On the Class Hierarchy Right-Click and Choose ‘Insert Class’ form the context menu and Class description dialog Box will be appeared,

• On the Class description, give the class name Deep To Medium Deep Perennial Natural Waterbodies and Click > OK

Insert a Classification Parent Process

• Right-Click in the ‘Process Tree’ window and select ‘Append New’ from the context menu.


New Dialog box will be appeared.

In the ‘Name’ field enter the name ‘Classification’ and confirm with ‘OK’. It will be your parents of Classification

• Select the inserted Classification Process and Right-Click on it. Choose ‘Insert Child’ form the context menu.

• In the ‘Name’ field, enter the name Perennial Natural Waterbodies and confirm with ‘OK’. It will be your Parents Class for a particular class (in this case, for Deep to Medium Perennial Natural Waterbodies Class).

Assign Class Algorithm

The Assign Class algorithm is the most simple classification algorithm. It determines by means of a threshold condition whether the image object is a member of the class or not.


This algorithm is used when one threshold condition is sufficient to assign an Image Object to a Class.

Classify the Deep To Medium Deep Perennial Natural Waterbodies

• Select the inserted Classification Process and Right-Click on it. Choose ‘Insert Child’ form the context menu and Assign Class Algorithm

• In the Edit Process dialog box, select assign class from the Algorithm list.


• In the algorithm parameter Use class, select Deep To Medium Deep Perennial Natural Waterbodies.

• In the Image Object Domain group Click > Select image object level

• In the Image Object Domain group set the Parameter Click on Level> Select Level-1


• In the Class Filter dialog box, Select unclassified from the classification list.

• In the Image Object Domain (Parameter) group Click the Threshold condition; it is labeled … if condition is not selected yet.

• From the Select Single Feature box’s Double-Click on Land & Water Mask (LWM) assign the threshold <= 20 Click > OK to apply your settings


• Right-Click one the process and select execute to execute the Perennial Natural Waterbodies process or Using F5 Execute the Process.

Classify the Lake



• In the ‘Name’ field, enter the name Lake and confirm with ‘OK’

Assign Class Algorithm for Lake




Classify the Lake



• In the algorithm parameter Use class, select Lake.








• Right-Click one the process and select execute to execute the Lake process or Using F5 Execute the Process.


*Note: Based on the LWM algorithm others land cover area has been classified as Lake. So you have to use few more conditions for refining the Lake area.

• In the Edit Process dialog box, select merge region from the Algorithm list and Fusion super objects Yes

• In the Image Object Domain Select Level-1 and In the Class filter Select > Lake > OK

• Using F5 Execute the algorithm

• In the Edit Process dialog box, select assign class from the Algorithm list and Use class unclassified


• In the Image Object Domain select image object level and parameter Level > Level-1, Class> Lake

• In the parameter Click on Threshold condition and to apply your bellow settings

Feature select Area and Threshold <= 3600000

• Using F5 Execute the Lake algorithm


Figure Classified lake area


Classify the River


• In the ‘Name’ field, enter the name River and confirm with ‘OK’

Assign Class Algorithm for River




Classify the River



• In the algorithm parameter Use class, select River.








• Right-Click one the process and select execute to execute the River process or Using F5 Execute the Process.


*Note: Based on the LWM algorithm others land cover area has been classified as River. So you have to use few more conditions for refining the River area.

• In the Edit Process dialog box, select assign class from the Algorithm list and Use class unclassified

• In the Image Object Domain select image object level and parameter Level > Level-1, Class> River

• In the parameter Click on Threshold condition and to apply your bellow settings


Feature select Length/Area and Threshold <= 1.6

Similar way add following condition for river and • Using F5 Execute the Lake algorithm

Classify the Broadleaved Tree Crop

• Select the inserted Classification Process and right-click on it. Choose ‘Insert Child’ form the context menu.


New Dialog box will be appear

In the ‘Name’ field enter the name ‘Broadleaved Tree Crop’ and confirm with ‘OK’. It will be your parents of Classification


Classify the Broadleaved Tree Crop




• In the algorithm parameter Use class, select Broadleaved Tree Crop.






• From the Select Single Feature box’s Double-Click on NDVI assign the threshold => 0.35 Click > OK to apply your settings


• Right-Click one the process and select execute to execute the Broadleaved Tree Crop process or Using F5 Execute the Process.

*Note: Based on the LWM algorithm others land cover area has been classified as Broadleaved Tree Crop. So you have to use few more conditions for refining the Broadleaved Tree Crop area.

Similar way add other condition for Broadleaved Tree Crop and • Using F5 Execute the Broadleaved Tree Crop algorithm


Please set following condition for others land cover

Please note classification criteria in the examples below are simplified for the sake of the exercise and normally would need to be more elaborate.

Bare Soil in seasonally flooded area

SLAVI stands for: Specific leaf area vegetation index

Bare Soil

Urban and Industrial Areas

Irrigated Herbaceous Crop

Rainfed Herbaceous Crop


Closed to Open Rooted Forb

Closed to Open Grassland

Small Herbaceous Crops in sloping land

Closed to Open Seasonally Flooded Shrubs

Closed to Open Shrubland

Small Sized Field Of Tree Crop


Broadleaved Tree Crop

Broadleaved Open Forest

Broadleaved Closed Forest

Classified Land cover


*Note The entire classification process shown base on single variable. For better results more variable need to use.


4.4 Manual Editing

Manual editing of image objects and thematic objects allows you to manually influence the result of an image analysis. The main manual editing tools are Merge Objects Manually, Classify Image Objects Manually and Cut an Object Manually.

While manual editing is not commonly used in automated image analysis, it can be applied to highlight or reclassify certain objects or to quickly improve the analysis result without adjusting the applied rule set.

To open the Manual Editing toolbar choose View > Toolbars > Manual Editing on the main menu.

Change Editing Mode

The Change Editing Mode drop-down list on the Manual Editing toolbar is set to Image Object Editing by default. If you work with thematic layers and want to edit them by hand, choose Thematic editing from the drop-down list.

Selection Tools

Objects to be fused or classified can be selected from the Manual Editing toolbar in one of the following ways:

1 Single Selection Mode selects one object. Select the object with a single click.

2 Polygon Selection selects all objects that lie within or touch the border of a polygon.

Set vertices of the polygon with a single click. Right-click and choose Close Polygon to close the polygon.

3 Line Selection selects all objects along a line. Set vertices of the line with a single click. A line can also be closed to form a polygon by right-clicking and choosing Close Polygon. All objects that touch the line are selected.


Rectangle Selection selects all objects within or touching the border of a rectangle. Drag a rectangle to select the image objects.

Merge Objects Manually

The manual editing tool Merge Objects is used to manually merge selected

neighboring image or thematic objects.

Note: Manual object merging operates only on the current image object level.

Tools > Manual Editing > Merge Objects from the main menu bar or press the Merge Objects Manually button on the Manual Editing toolbar to activate the input mode. Or you can use right click.

Note: You should have at list two objects.



Classify Image Objects Manually

The manual editing tool Classify Image Objects allows easy class assignment of selected image objects.

Manual image object classification can be used for the following purposes:

• Manual correction of previous classification results including classification of previously unclassified objects.

• Classification without rule sets (in case the creation of an appropriate rule set is more time-consuming), using the initial segmentation run for automated digitizing. Precondition: To classify image objects manually, the project has to contain at least one image object level and one class in the Class Hierarchy.

To perform a manual classification, do one of the following:

• Choose Tools > Manual Editing > Classify Image Objects from the menu bar.

• Click the Classify Image Objects button on the Manual Editing toolbar to activate

the manual classification input mode.

In the Select Class for Manual Classification drop-down list box, select the class to which you want to manually assign objects. Note that selecting a class in the Legend window or in the Class Hierarchy window (if available) will not determine the class for manual editing; the class has to be selected from the before-mentioned drop-down list.

Now objects can be classified manually with a single mouse-click. To classify objects, do one of the following:

• Select the Classify Image Objects button and the Class for Manual

Classification. Click the image objects to be classified.

• Select the image object(s) you want to classify first. Select the Class for Manual Classification and press the Classify Image Objects button to classify all selected objects.

• Select one or more image objects, right-click into the image object(s) and select Classify Selection from the context menu.

When the object is classified, it is painted in the color of the respective class.


If no class is selected, a mouse-click deletes the previous class assignment; the image object becomes unclassified.

To undo a manual classification on a previously unclassified object, simply click the object a second time. If the object was previously classified, then clicking again does not restore the former classification; instead, the object becomes unclassified.

Export Results

To export results, open the Export Results dialog box by choosing Export > Export Results from the main menu bar.

• Choose Shapefile/Raster from the Export Type drop-down list.

• From the Content Type drop-down list, choose to export shape file for: Classes


• The Format has to be *.shp.

• Select the image object Level for which you want to export results: Level-1.

• Change the default file name in the Export File Name text field if desired.

• To save the shape file to disk, press Export.

Note: Definiens Trial version cannot export the results

Segmentation Creates a New Image Object Level

The new image objects created by segmentation are stored in what is called an new image object level. Each image object is defined by a contiguous set of pixels, where each pixel belongs to exactly one image object. Each of the subsequent image object related operations like classification, reshaping, re-segmentation, and information extraction is done within an image object level. Simply said, image object levels serve as internal working areas of the image analysis.

Delete Image Object Level

Delete an image object level. This enables you to work with image object levels that are temporary, or that might be required for testing processes while developing rule sets.

To delete an image object level do the following:

• Choose Image Objects > Delete Levels on the main menu bar.

• The opening Delete Level dialog box displays a lists of all image object levels according to the image object hierarchy.

• Select the image object level to be deleted

• Confirm with OK. The selected image object levels will be removed from the image object hierarchy.


4.5 Annex I: Information on Landsat and Indices

Satellite Sensor Band Resolution

Landsat ETM+ Band 1 0.45 - 0.52µm (Blue)

Band 2 0.52 - 0.60µm (Green)

Band 3 0.63 - 0.69µm (Red)

Band 4 0.75 - 0.90µm (NIR)

Band 5 1.55 - 1.75µm (IR)

Band 6 10.4 - 12.50µm (TIR)

Band 7 2.08 - 2.35µm (NIR)

Band 8 0.52 - 0.90µm (Pan)

30 meter

30 meter

30 meter

30 meter

30 meter

60 meter

30 meter

15 meter

Normalized Difference Vegetative Index (NDVI)


(ETM+) NDVI = (Band 4 - Band 3) / (Band 4 + Band 3)

Normalized Difference Snow/Ice Index (NDSII)

NDSII = (green – infra-red) / (green + infra-red)

(ETM+) NDSII = (Band 2 - Band 5) / (Band 2 + Band 5)

Land and Water Masks (LWM)

Water Mask = (infra-red) / (green + .0001) * 100

(ETM+) Water Mask = (Band 5) / (Band 2 + .0001) * 100

Modification of Normalized Difference Water Index (NDWI)

NDWI=(NIR – IR / (NIR + IR)

(ETM+) NDWI = (Band 4 - Band 5) / (Band 4 + Band 5)


Normalized Burn Ratio

NBR=(NIR – TIR) / (NIR + TIR)

(ETM+) NBR = (Band 4 - Band 7) / (Band 4 + Band 7)

Ratio vegetation index

RVI=NIR / red

(ETM+) RVI = Band 4 / Band 3

Green normalized difference vegetation index

GNDVI=(NIR - Green) ÷ (NIR + Green)

(ETM+) GNDVI = (Band 4 - Band 2) / (Band 4 + Band 2)

Specific leaf area vegetation index (SLAVI)

SLAVI = NIR ÷ (Red + infra-red)

(ETM+) SLAVI = Band 4 / (Band 3 + Band 5)

Normalized Difference Moisture Index (NDMI)

NDMI = (NIR-IR)/ (NIR+IR)

(ETM+) NDMI = (Band 4 - Band 5) / (Band 4 + Band 5)


5. References and Useful links

User Guide eCognition Developer www.ecognition.com/

Global Land Cover Facility http://www.glcf.umd.edu/index.shtml

Global SRTM Datasets http://srtm.csi.cgiar.org/

International Centre for Integrated Mountain Development http://www.icimod.org

Mountain Geoportal http://geoportal.icimod.org

Satellite imagery http://edcsns17.cr.usgs.gov/NewEarthExplorer/

Satellite imagery http://glovis.usgs.gov/

Bajracharya, B., Uddin, K., Chettri , N., and Shrestha, B., Siddiqui , S., (2010) ‘Understanding Land Cover Change Using a Harmonized Classification System in the Himalaya A Case Study From Sagarmatha National Park, Nepal' Mountain Research and Development 30(2):143-156.

Cihlar, J. (2000). Land-cover mapping of large areas from satellites: Status and research priorities. International Journal of Remote Sensing, 21, 1093−1114.

Di Gregorio, A (2005) Land Cover Classification System (LCCS), version 2: Classification Concepts and User Manual. FAO Environment and Natural Resources Service Series, No. 8, Rome: FAO

Franklin, S. E., & Wulder, M. A. (2002). Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, 26, 173−205.

Houghton, R.A., (1998). Historic role of forests in the global carbon cycle. In: G.H. Kohlmaier, M. Weber, and R.A. Houghton, eds. Carbon dioxide mitigation in forestry and wood industry. Berlin: Springer, 1 24.

John R. Townshend, Jeffrey G. Masek, Chengquan Huang, Eric F. Vermote, Feng Gao, Saurabh Channan, Joseph O. Sexton, Min Feng, Raghuram Narasimhan, Dohyung Kim, Kuan Song, Danxia Song, Xiao-Peng Song, Praveen Noojipady, Bin Tan, Matthew C. Hansen, Mengxue Li & Robert E. Wolfe (2012): Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges, International Journal of Digital Earth, 5:5, 373-397

Pandey, D.N., (2002). Sustainability science for tropical forests. Conservation Ecology, 6, 13.

Strategic Plan for the Climate Change Science Program Final Report, July 2003

http://www.ecognition.com/�

http://www.glcf.umd.edu/index.shtml�

http://srtm.csi.cgiar.org/�

http://www.icimod.org/�

http://geoportal.icimod.org/�

http://edcsns17.cr.usgs.gov/NewEarthExplorer/�

http://glovis.usgs.gov/�


Vogelmann, J. E., Howard, S. M., Yang, L. M., Larson, C. R., Wylie, B. K., & Van Driel, N. (2004). Completion of the 1990s National Land Cover Data set for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources. Photogrammetric Engineering and Remote Sensing, 67, 650−662.

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