Remote Sensing as a Tool for Land Degradation Neutrality Monitoring
Type: Technologies
Creation: 2019-08-23 11:21 Updated: 2020-08-31 13:00
Compilers: Hanns Kirchmeir
Reviewers: Rima Mekdaschi Studer
Country/ region/ locations where the Technology has been applied and which are covered by this assessment
- Country: Georgia
- Region/ State/ Province: Tusheti region, Akhmeta municipality
- Map: View Map
Description of the SLM Technology
Short description of the Technology
Land degradation contributes to biodiversity loss and the impoverishment of rural livelihoods in Tusheti. Above all, however, land degradation are triggered by climate change as traditional land use practise might not be adapted to new climate conditions which can cause or speed up degradation processes significantly. On the other hand, degraded land often leads to low biomass volumes and this reduces the ecosystem capability to stabilise local climate conditions. The concept of Land Degradation Neutrality (LDN) and the method of using remote sensing for monitoring land degradation are tools to identify the need for local planning processes. This showcase describes the LDN monitoring concept, national targets and the technology to assess indicators, mechanism and incentives for LDN.
Detailed description of the Technology
Purpose
The continuing global degradation of land resources threatens food security and the functioning of ecosystem services by reducing or losing their biological or economic productivity. Unsustainable land-use practices such as deforestation, overgrazing and inappropriate agricultural management systems trigger the loss and degradation of valuable land resources in Georgia. These effects are visible in all countries of the South Caucasus. About 35% of the agricultural land in Georgia is severely degraded, 60% is of low to middle production quality.
Land Degradation Neutrality (LDN)
LDN is a new international concept to combat the ongoing degradation of valuable soil resources. The LDN concept was developed by the UNCCD to encourage countries to take measures to avoid, reduce or reverse land degradation, with the vision of achieving a zero-net loss of productive land. To combat land degradation in Georgia, in 2017, the national LDN Working Group set voluntary national targets to address specific aspects of LDN, and submitted them to the UNCCD Secretariat.
To effectively set up counter measures to combat land degradation it is important to have detailed spatial information on land cover and land cover changes as well as on trends in degradation (like size of areas effected by erosion). Therefore a remote sensing toolset was developed and tested in the pilot are of Tusheti protected landscapes in the High Caucasus in Georgia. This region shows increasing soil erosion problems by uneven distribution of grazing activities and was selected for developing erosion control measures within the Integrated Biodiversity Management in the South Caucasus Program (IBiS) funded by the Deutsche Gesellschaft für internationale Zusammenarbeit (GIZ).
Sensitivity Model
The Integrated Biodiversity Management in the South Caucasus (IBiS) project in cooperation with national experts in Georgia, developed and applied a remote sensing toolset called "Erosion Sensitivity Model". This remote sensing toolset helps to assess the current state and the general erosion risk. The sensitivity model is based on the RUSLE – Revised Universal Soil Loss Equation. The tool allows the calculation of erosion caused by rainfall and surface run-off. The RUSLE equation incorporates a combination of different input factors such as precipitation (R), soil type (K), slope (LS), vegetation cover (C) and protection measures (P). In this way, the estimated average soil loss in tonnes per acre per year (A) can be calculated as follows: A = R * K * LS * C * P.
The rainfall factor (R) results from a quotient from the monthly and annual mean value of precipitation. The data come from the data platform “CHELSA – Climatologies at high resolution for the earth’s land surface areas”. For the soil type factor (K), a soil map of 1:200,000 was taken. Then, depending on the soil type, different contents of sand, silt, loam and clay were used to calculate the K factor. The slope length and steepness factor (LS) is calculated from a digital elevation model (DEM) with a raster resolution of 10x10m. The DEM is derived from the topographic map 1:25,000. The global elevation model derived from SRTM data (Shuttle Radar Topography Mission) has a resolution of 30x30 m and is available worldwide free of charge. The land cover factor (C) describes the vegetation cover that protects the soil from erosion. The vegetation cover slows down the speed of the raindrops and reduces the erosive effect of the rain. It slows down surface water runoff and stabilises the soil through root systems. The main indicators, land cover and productivity, can be assessed by remote sensing. The data from satellites need to be classified and calibrated by field data (ground truthing). The technology for the assessment of these indicators with Sentinel 2 satellite images was developed and applied in 2016 to 2018 in the Tusheti region (Akhmeta municipality) in the framework of the GIZ-IBiS project. Based on spectral information from airborne or satellite images, the density of the vegetation was calculated and mapped. There are well developed vegetation indices and classification systems to derive different land cover types and vegetation densities (mainly described by the Leaf Area Index LAI or biomass indices). The LAI is the area of the leaf surface (in square meters) per square meter ground surface. Since the real surface area of the leaves is hardly measurable, the amount of biomass is a proxy for the LAI. The P-factor is rarely considered in large-scale modelling of soil erosion risk as it is difficult to estimate it with very high accuracy. Therefore, to refine the model, a more detailed DEM (digital elevation model) is required (e.g., from satellite images). Based on the input factors, a soil erosion risk map was calculated for the whole territory of the Tusheti Protected Areas (113,660 ha). Based on the different spectral bands of the Sentinel 2 satellite image, a land cover map was calculated using the Support Vector Machine (SVM) technology and spectral image information.
The results have been integrated in the development of pasture management plans ("pasture passports"). This maps and documents are indicating areas of high erosion risk that need to be excluded from grazing and the maximum number of livestock has been calculated based on the biomass maps and will be integrated into the lease contracts.
The repetition of the remote sensing after some years (e.g. 5 years) will help to evaluate, if the measures in the pasture management have been successful to stop the degradation processes.
Photos of the Technology
- 📍 Tusheti, Akhmeta Municipality, Georgia
- 🗓 2016-11-08
- 📷 Hanns Kirchmeir
- 📍 Garabani municipality, Georgia
- 🗓 2017-12-09
- 📷 Hanns Kirchmeir
- 📍 Shenako, Akhmeta Municipality, Georgia
- 📷 Hanns Kirchmeir
- 📷 Hanns Kirchmeir
- 📷 Giorgi Mikhaladze
- 📷 Hanns Kirchmeir
- 📷 Technical drawing: Sentinel-2 is ESA's operational mission for the observation of land surfaces with a decametric resolution. ESA (http://www.cesbio.ups-tlse.fr/us/index_sentinel2.html)