Estimating grassland biomass and livestock carrying capacity using Sentinel data to strengthen grazing management on local to national scales in Armenia

Kurzfassung

Livestock farming is an important part of the Armenian agricultural development strategy. The agricultural sector employs more than one third of Armenia’s labor force and accounts for 13% of GDP, hence threats to livestock and pastures can significantly impact livelihoods. For sustaining and developing that sector, fodder provision from grasslands is a key factor. Grasslands constitute 39% of the total territory of Armenia and 57% of the agricultural lands. Apart from resources for livestock, they provide important areas for biodiversity and ecosystem services. The condition of natural pastures and grasslands, however, is being deteriorated due to anthropogenic pressure and unsustainable management practices, leading to overgrazing and erosion. These risks are potentially further aggravated through climatic changes such as more frequent droughts, heat waves, and lack of snow cover. Hence, the setup of an integrated management approach for local decision-making becomes important, emphasizing the need of robust and up-to-date spatial data. In the context of the “GrassAM” project conducted by DLR and GIZ, we aimed at mapping grassland extent, grasslands types, grassland above ground biomass (AGB) and livestock carrying capacities at 10 m spatial resolution in the entire country of Armenia in the year 2020. In order to create a grassland mask for Armenia, a land use and land cover (LULC) classification was carried out using in situ data together with Sentinel-1, Sentinel-2, and digital elevation (DEM) data in a random forest classification approach implemented on Google Earth Engine. 400 sample points of 7 classes (“pasture”, “meadow”, “other grasslands”, “annual arable land”, “perennial arable land”, “bushland”, “bare soil”) were collected by the partner organization ICARE during summer 2020, distributed over all districts and ecological zones. To complement the classification, additional points were sampled on screen for the class “water”. Urban and forest areas were masked using DLR’s World Settlement Footprint 2015 [1] at 10 m resolution as well as the Hansen Global Forest Change maps [2] at 30 m resolution, respectively. The resulting classification achieved an overall accuracy of 80%, while the grassland area was slightly overestimated with 79% user’s accuracy and 92% producer’s accuracy. Of the 400 in situ sites, 147 pasture and meadow points also included wet and dry AGB samples. These measurements have been collected in one 30 x 30 cm plot per field (mowing at 2 cm height), which was assumed to be representative for the surrounding 30 x 30 m. The fresh plant mass was placed in a paper container, labeled and weighed with precision of 0.1 grams. Samples were then dried at room temperature for 48 - 72 hours and weighed again. The measured green AGB ranges from 1.733 – 27.800 kg/ha, with a mean of 12.367 kg/ha, and dry AGB ranges from 1.011 – 14.300 kg/ha, with a mean of 5.416 kg/ha. The AGB measurements were split 60/40 in training and validation data. To create a spatially balanced training data, the selection of training samples was based on spatial allocation of points in hexagon tessellation (1 point per grid cell; 2 points if there are more than four samples are available per cell). Biomass was modeled in a next step using a random forest regression model. The training samples have been used to test a set of 730 different geospatial features (monthly statistics and bi-weekly interpolated features of B2 - B12 Sentinel-2 bands and of eight vegetation indices, elevation, slope, monthly mean temperature at 2 m, monthly precipitation sums) as predictors using a Sequential Forward Feature Selection. Six features (Sentienl-2 mid-June and mid-July NDVI, Band 12 median, May precipitation, June temperature, elevation) were selected and achieved a R-square of 0.66 with an RMSE of 4.013 kg/ha for green AGB. The country-wide biomass maps are the basis to model grassland carrying capacity, i.e. the maximum number of cattle equivalent animals that can be sustained in a given grassland area in a season. AGB was multiplied with a proper use factor of 0.65 as it was suggested by [3] to estimate the available fodder. This amount is divided by the daily requirement of fodder per animal unit (equivalent of 400 kg live weight of cows) multiplied by pasture season length. For both quantities, landscape-zone specific assumptions have been made, resulting in an optimal stocking density of 1- 3 animals per hectare. Test for improving biomass and carrying capacity models as well as the input data sets are still ongoing. The resulting maps, that characterize the allowable grazing pressure on a country-wide scale, could be used to improve grassland management and to increase the resilience of grassland ecosystems to future climate conditions. [1] Marconcini, M., Metz-Marconcini, A., Üreyen, S., Palacios-Lopez, D., Hanke, W., Bachofer, F., Zeidler, J., Esch, T., Gorelick, N., Kakarla, A., & Strano, E. (2020). Outlining Where Humans Live –The World Settlements Footprint 2015. Scientific Data7(242). doi.org/10.1038/s41597-020-00580-5. [2] Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. [3] de Leeuw, J. Rizayeva, A., Namazov, E., Bayramov, E., Marshall, M. T., Etzold, J., Neudert,R. (2019): Application of the MODIS MOD 17 Net Primary Production product in grassland carrying capacity assessment, International Journal of Applied Earth Observation and Geoinformation 78, 66-76, https://doi.org/10.1016/j.jag.2018.09.014.