The Tropical Andes region includes biodiversity hotspots of high conservation priority whose management strategies depend on the analysis of forest dynamics drivers (FDDs). These depend on complex social and ecological interactions that manifest on different space–time scales and are commonly evaluated through regression analysis of multivariate datasets. However, processing such datasets is challenging, especially when time series are used and inconsistencies in data collection complicate their integration. Moreover, regression analysis in FDD characterization has been criticized for failing to capture spatial variability; therefore, alternatives such as geographically weighted regression (GWR) have been proposed, but their sensitivity to multicollinearity has not yet been solved. In this scenario, we present an innovative methodology that combines techniques to: 1) derive remote sensing time series products; 2) improve census processing with dasymetric mapping; 3) combine GWR and random forest (RF) to derive local variables importance; and 4) report results based in a clustering and hypothesis testing. We applied this methodology in the northwestern Ecuadorian Amazon, a highly heterogeneous region characterized by different active fronts of deforestation and reforestation, within the time period 2000–2010. Our objective was to identify linkages between these processes and validate the potential of the proposed methodology. Our findings indicate that land-use intensity proxies can be extracted from remote sensing time series, while intercensal analysis can be facilitated by calculating population density maps. Moreover, our implementation of GWR with RF achieved accurate predictions above the 74% using the out-of-bag samples, demonstrating that derived RF features can be used to construct hypothesis and discuss forest change drivers with more detailed information. In the other hand, our analysis revealed contrasting effects between deforestation and reforestation for variables related to suitability to agriculture and accessibility to its facilities, which is also reflected according patch size, land cover and population dynamics patterns. This approach demonstrates the benefits of integrating remote sensing–derived products and socioeconomic data to understand coupled socioecological systems more from a local than a global scale.

Land degradation has not been comprehensively addressed at the global level or in developing countries. A suitable economic framework that could guide investments and institutional action is lacking. This study aims to overcome this deficiency and to provide a framework for a global assessment based on a consideration of the costs of action versus inaction regarding desertification, land degradation, and drought (DLDD). Most of the studies on the costs of land degradation (mainly limited to soil erosion) give cost estimates of less than 1 percent up to about 10 percent of the agricultural gross domestic product (GDP) for various countries worldwide. But the indirect costs of DLDD on the economy (national income), as well as their socioeconomic consequences (particularly poverty impacts), must be accounted for, too. Despite the numerous challenges, a global assessment of the costs of action and inaction against DLDD is possible, urgent, and necessary. This study provides a framework for such a global assessment and provides insights from some related country studies.

Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel 1) data. Several indicators were derived such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), Tasseled cap indices and Sentinel-1 based backscattering intensity and relative surface moisture. We used logistic regression to evaluate the drought-induced variability of remotely sensed parameters estimated for different phases of crop growth. The parameters with the highest prediction rate were further used to estimate thresholds for drought/non-drought classification. The models were evaluated using the area under the receiver operating characteristic curve and validated with in-situ data. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions. Growing season maximum NDVI and NDMI (70–75%) and SAR derived metrics (60%) reflect specifically the impact of agricultural drought. These metrics also depict stress affected areas with a larger spatial extent. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86% and 71%, respectively. The developed approach can be further used to assess crop condition and to support decision-making in areas which are more susceptible and vulnerable to drought.

Eastern Cape Province in South Africa has experienced extreme drought events during the last decade. In South Africa, different land management systems exist belonging to two different land tenure classes: commercial large scale farming and communal small-scale subsistence farming. Communal lands are often reported to be affected by land degradation and drought events among others considered as trigger for this process. Against this background, we analyzed vegetation response to drought in different land management and land tenure systems through assessing vegetation productivity trends and monitoring the intensity, frequency and distribution of the drought hazard in grasslands and communal and commercial croplands during drought and non-drought conditions. For the observation period 2000–2016, we used time series of 250 m Vegetation Condition Index (VCI) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) and Climate Hazard Group InfraRed Precipitation with Station data (CHIRPS) precipitation data with 5 km resolution. For the assessment of vegetation dynamics, we: (1) analyzed vegetation productivity in Eastern Cape over the last 16 years with EVI; (2) analyzed the impact of drought events on vegetation productivity in grasslands as well as commercial and communal croplands; and (3) compared precipitation-vegetation dynamics between the drought season 2015/2016 and the non-drought season 2011/2012. Change in total annual vegetation productivity could detect drought years while drought dynamics during the season could be rather monitored by the VCI. Correlation of vegetation condition and precipitation indicated areas experiencing significant vegetation productivity trends showing low and even negative correlation coefficients indicating other drivers for productivity change and drought impact besides rainfall.

Essential climate variables (ECVs) have been recognized as crucial information for achieving Sustainable Development Goals (SDGs). There is an agreement on 54 ECVs to understand climate evolution, and multiple rely on satellite Earth observation (abbreviated as s-ECVs). Despite the efforts to encourage s-ECV use for SDGs, there is still a need to further integrate them into the indicator calculations. Therefore, we conducted a systematic literature review to identify s-ECVs used in SDG monitoring. Results showed the use of 14 s-ECVs, the most frequent being land cover, ozone, precursors for aerosols and ozone, precipitation, land surface temperature, soil moisture, soil carbon, lakes, and leaf area index. They were related to 16 SDGs (mainly SDGs 3, 6, 11, 14, and 15), 33 targets, and 23 indicators. However, only 10 indicators (belonging to SDGs 6, 11, and 15) were calculated using s-ECVs. This review raises research opportunities by identifying s-ECVs yet to be used in the indicator calculations. Therefore, indicators supporting SDGs must be updated to use this valuable source of information which, in turn, allows a worldwide indicator comparison. Additionally, this review is relevant for scientists and policymakers for future actions and policies to better integrate s-ECVs into the Agenda 2030.