Long-Term Grass Biomass Estimation of Pastures from Satellite Data

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2020-07, Vol.12 (13), p.2160
• Main Authors: Clementini, Chiara, Pomente, Andrea, Latini, Daniele, Kanamaru, Hideki, Vuolo, Maria Raffaella, Heureux, Ana, Fujisawa, Mariko, Schiavon, Giovanni, Del Frate, Fabio
• Format: Article
• Language: English
• Subjects: Agriculture; Algorithms; Biomass; biomass estimation; Cattle; Classification; Climate change; Climate effects; Drought; Environmental impact; Estimates; Feeds; Food; Geographical distribution; Geographical locations; Grasses; Grasslands; International organizations; land cover classification; Livestock; long-term analysis; multitemporal data; Neural networks; Pasture; Precipitation; Productivity; Remote sensing; Studies; Time series; Vegetation; Water shortages; Windows (intervals)
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12132160

The general consensus on future climate projections poses new and increased concerns about climate change and its impacts. Droughts are primarily worrying, since they contribute to altering the composition, distribution, and abundance of species. Grasslands, for example, are the primary source for grazing mammals and modifications in climate determine variation in the available yields for cattle. To support the agriculture sector, international organizations such as the Food and Agriculture Organization (FAO) of the United Nations are promoting the development of dedicated monitoring initiatives, with particular attention for undeveloped and disadvantaged countries. The temporal scale is very important in this context, where long time series of data are required to compute consistent analyses. In this research, we discuss the results regarding long-term grass biomass estimation in an extended African region. The results are obtained by means of a procedure that is mostly automatic and replicable in other contexts. Zambia has been identified as a significant test area due to its vulnerability to the adverse impacts of climate change as a result of its geographic location, socioeconomic stresses, and low adaptive capacity. In fact, analysis and estimations were performed over a long time window (21 years) to identify correlations with climate variables, such as precipitation, to clarify sensitivity to climate change and possible effects already in place. From the analysis, decline in both grass quality and quantity was not currently evident in the study area. However, pastures in the considered area were found to be vulnerable to changing climate and, in particular, to the water shortages accompanying drought periods.