Resilience and pastoralism: satellite-based decision support system for pastures

畜牧业占肯尼亚土地面积的37.5%，占其GDP的12%，占其农业部门的40%，但由于过度放牧，容易受到频繁干旱和退化的影响。在这种情况下，这项由苏塞克斯大学领导的工作将评估新的地球观测（EO）数据集（例如哨兵1号和2号）的潜力，以提供对畜牧业有用和可获取的生物量的近实时监测和预测。牧场的可达性将采用与当地利益相关者共同参与的方法绘制地图，以确定与土地权属、保护要求、移民模式、水和其他社会文化因素相关的问题。有用生物量的监测将依赖于绘制主要植物功能类型（PFTs），然后跟踪这些PFTs的生物量动态、植物健康和物候周期。PFTsin牧场包括一年生和多年生牧草，落叶和常绿灌木。这种分类将利用光谱混合分析或机器学习技术来实现，利用Sentinel光谱的光谱和时间差异。草场动态将从以下方面进行监测：(1)与Sentinel-1雷达后向散射和干涉高度相关的生物量动态与地面生物量观测；ii)利用植被和水分指数（如NDVI、NDWI）监测植被健康和胁迫，以及利用3个红边Sentinel-2波段监测植物光合活性；Iii)物候学通过拟合各指标的logistic函数。最后，建立牧场预测模型，并根据观测数据和气候数据的实时更新进行定期更新。物候周期、生物量和植物健康的变化将与气象变量和其他地理空间数据集有关。土壤质地，牲畜)。预测将依赖于时间序列分析，如自回归移动平均方法，高斯过程与季节性气候预测相结合

Livestock accounts for 37.5% of Kenya's land area, 12% of its GDP and 40% of itsagricultural sector, but is susceptible to frequent droughts and degradation due toovergrazing. In this context, this Sussex University-led work will assess the potential ofnew earth observation (EO) datasets (e.g., Sentinel 1 and 2) to deliver near real-timemonitoring and prediction of useful and accessible biomass for pastoralism.The accessibility of pasture areas will be mapped using a participatory approach withlocal stakeholders to identify issues related to land tenure, conservation requirements,migration patterns, water, and other socio-cultural factors.Monitoring of useful biomass will rely on mapping major plant functional types (PFTs), andthen tracking biomass dynamics, plant health, and phenological cycles of these PFTs. PFTsin pastures include annual and perennial grasses and deciduous and evergreen shrubs. Thisclassification will be achieved using spectral mixture analysis or machine learningtechniques, taking advantage of spectral and temporal differences in Sentinel's spectra.Pasture dynamics will be monitored from i) biomass dynamics relating Sentinel-1 radarbackscatter and interferometric height with ground biomass observations; ii) vegetationhealth and stress using vegetation and water indices (e.g., NDVI, NDWI), as well asmonitoring plant photosynthetic activity using the 3 red-edge Sentinel-2 bands; iii)phenology through fitting logistic functions on various indices.Finally, predictive models of pastures will be developed which will be updated regularlywith the real-time update of EO and climate data. Changes to phenological cycles, biomassand plant health will be related to meteorological variables and other geospatial datasets(e.g. soil texture, livestock). Forecasting will rely on time-series analyses such asautoregressive moving average methods, Gaussian Processes in combination with seasonalclimate forecast