Difference between revisions of "SPIS Toolbox - Land Cover"
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| − | === '''<span style="color:#879637;">Land Cover/Land Use</span>''' === | + | === '''<span style="color:#879637;">2.1 Land Cover/Land Use</span>''' === |
| − | Land Cover refers to the physical and biological cover over the surface of the earth including water, bare surfaces, forests, and artificial structures among others. Land use on the other hand refers to how people utilize the land whether for recreation, agriculture or wildlife habitats among others.<br/> | + | Land Cover refers to the physical and biological cover over the surface of the earth including water, bare surfaces, forests, and artificial structures among others. Land use, on the other hand, refers to how people utilize the land whether for recreation, agriculture or wildlife habitats among others.<br/> |
Land cover/land use is one of the fundamental parameters to be considered during the identification of potential markets for SPIS as it helps determine feasible locations for agriculture from which other parameters may be considered. Land cover is measured either through direct field observations or through remote sensing techniques involving the analysis of satellite and aerial imagery. Based on the land cover analysis, land use data can be inferred through ancillary data. The data assists decision makers and stakeholders in cross-cutting sectors to understand the dynamics of a changing environment and ensure sustainable development.<br/> | Land cover/land use is one of the fundamental parameters to be considered during the identification of potential markets for SPIS as it helps determine feasible locations for agriculture from which other parameters may be considered. Land cover is measured either through direct field observations or through remote sensing techniques involving the analysis of satellite and aerial imagery. Based on the land cover analysis, land use data can be inferred through ancillary data. The data assists decision makers and stakeholders in cross-cutting sectors to understand the dynamics of a changing environment and ensure sustainable development.<br/> | ||
| − | Land cover data typically consists of eight classes including wetlands, water bodies, urban, shrubs, grassland, forests, bare land and agricultural land. These may, however, be classified into varying classes depending on the source of data. The FAO framework for land suitability for instance, divides land into four classes ranging from highly suitable land for agriculture (S1) to currently not suitable land (S4). For the 8 classes listed above, ‘agricultural land’ can be classified as highly suitable (S1) and ‘grassland’, which requires land clearing and levelling, as moderately suitable (S2). ‘Shrub land’ and ‘bare land’, which require higher initial investment for land preparation can be classified as marginally suitable (S3) while ‘forest’, ‘water’, ‘urban’, and ‘wetlands’ can be categorized as not suitable (S4).<br/> | + | Land cover data typically consists of eight classes including wetlands, water bodies, urban, shrubs, grassland, forests, bare land and agricultural land. These may, however, be classified into varying classes depending on the source of data. The FAO framework for land suitability, for instance, divides the land into four classes ranging from highly suitable land for agriculture (S1) to currently not suitable land (S4). For the 8 classes listed above, ‘agricultural land’ can be classified as highly suitable (S1) and ‘grassland’, which requires land clearing and levelling, as moderately suitable (S2). ‘Shrub land’ and ‘bare land’, which require higher initial investment for land preparation can be classified as marginally suitable (S3) while ‘forest’, ‘water’, ‘urban’, and ‘wetlands’ can be categorized as not suitable (S4).<br/> |
| − | In assessing market potential for SPIS for a given country or region, stakeholders need to assess the irrigation viability of their target location from a land cover-land use perspective. For example, areas that are mostly classified as S1 land would have higher potential for SPIS compared to those that are highly urbanized or classified as wetlands.<br/> | + | In assessing market potential for SPIS for a given country or region, stakeholders need to assess the irrigation viability of their target location from a land cover-land use perspective. For example, areas that are mostly classified as S1 land would have a higher potential for SPIS compared to those that are highly urbanized or classified as wetlands.<br/> |
It should be noted that desktop analysis of land cover/land use data through application of remote sensing techniques should be followed by ground truthing to ascertain the land cover/land use in the selected regions prior to investment. | It should be noted that desktop analysis of land cover/land use data through application of remote sensing techniques should be followed by ground truthing to ascertain the land cover/land use in the selected regions prior to investment. | ||
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It is always important to follow up desktop analysis of landcover with actual on-ground visits to the selected areas. Satellite and aerial images are typically very accurate however if one is not using up to date datasets it becomes important to verify the selection. | It is always important to follow up desktop analysis of landcover with actual on-ground visits to the selected areas. Satellite and aerial images are typically very accurate however if one is not using up to date datasets it becomes important to verify the selection. | ||
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| + | | {{Back to SPIS Toolbox 2}} | ||
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| + | | style="width: 150px; background-color: rgb(222, 226, 192);" | <span style="color: rgb(0, 0, 0);"><span style="font-size: 90%;">'''[[SPIS_Toolbox_-_Solar_Irradiation|►Go to the Next Chapter]]'''</span></span> | ||
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Latest revision as of 12:05, 4 September 2018
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2.1 Land Cover/Land Use
Land Cover refers to the physical and biological cover over the surface of the earth including water, bare surfaces, forests, and artificial structures among others. Land use, on the other hand, refers to how people utilize the land whether for recreation, agriculture or wildlife habitats among others.
Land cover/land use is one of the fundamental parameters to be considered during the identification of potential markets for SPIS as it helps determine feasible locations for agriculture from which other parameters may be considered. Land cover is measured either through direct field observations or through remote sensing techniques involving the analysis of satellite and aerial imagery. Based on the land cover analysis, land use data can be inferred through ancillary data. The data assists decision makers and stakeholders in cross-cutting sectors to understand the dynamics of a changing environment and ensure sustainable development.
Land cover data typically consists of eight classes including wetlands, water bodies, urban, shrubs, grassland, forests, bare land and agricultural land. These may, however, be classified into varying classes depending on the source of data. The FAO framework for land suitability, for instance, divides the land into four classes ranging from highly suitable land for agriculture (S1) to currently not suitable land (S4). For the 8 classes listed above, ‘agricultural land’ can be classified as highly suitable (S1) and ‘grassland’, which requires land clearing and levelling, as moderately suitable (S2). ‘Shrub land’ and ‘bare land’, which require higher initial investment for land preparation can be classified as marginally suitable (S3) while ‘forest’, ‘water’, ‘urban’, and ‘wetlands’ can be categorized as not suitable (S4).
In assessing market potential for SPIS for a given country or region, stakeholders need to assess the irrigation viability of their target location from a land cover-land use perspective. For example, areas that are mostly classified as S1 land would have a higher potential for SPIS compared to those that are highly urbanized or classified as wetlands.
It should be noted that desktop analysis of land cover/land use data through application of remote sensing techniques should be followed by ground truthing to ascertain the land cover/land use in the selected regions prior to investment.
Outcome/Product
- Classification of land based on agricultural suitability
- Selection of optimal sites to promote solar powered irrigation
Data Requirement
- Land use - land cover data
- Land suitability classification frameworks (e.g. FAO)
People/Stakeholders
- Land Surveyors
- Remote sensing analysts
- Government land ministries
Important Issues
It is always important to follow up desktop analysis of landcover with actual on-ground visits to the selected areas. Satellite and aerial images are typically very accurate however if one is not using up to date datasets it becomes important to verify the selection.
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