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Mapping and monitoring disturbances in vegetation over large areas demand reliable approaches and accurate end-user maps. Methods and algorithms have been developed to meet satisfactory disturbance map accuracies, and the combination of multiple approaches has shown promise as a reliable alternative to any single method. However, extracting meaningful disturbance information from these combined methods is still challenging. Data variance from environmental conditions and disturbance drivers leads to spatial-temporal heterogeneity in land surfaces over large areas, which results in mapping errors. We evaluated the effectiveness of ensemble classification and data-driven regionalization for mapping vegetation disturbances at a broad scale. Using Google's Earth Engine cloud computing platform, our ensemble approach combines multispectral LandTrendr outputs reflecting preliminary disturbance information in a Random Forest model to map disturbances in Minas Gerais, Brazil. We then applied an unsupervised clustering technique to perform data-driven regionalization of our study area using several sources of environmental and anthropogenic information and analysed gains and losses in map accuracies. Our results indicated gains in accuracy by the ensemble method compared to non-ensemble methods of disturbance mapping, which ranged from 7.3 to 29.9% in overall accuracy at the 5% significance level. Data-driven regionalization addressed complexities arising from variability in vegetation types, local climate, and topography across our study area, identifying climate and seasonal metrics as important variables for reducing uncertainties in vegetation disturbance maps. The integration of these techniques has revealed significant potential for increasing map accuracy and has provided important insights into the development of disturbance mapping methods in heterogeneous environments. [ABSTRACT FROM AUTHOR]

Global change has tremendous impacts on savanna systems around the world. Processes related to climate change or agricultural expansion threaten the ecosystem’s state, function and the services it provides. A prominent example is the Brazilian Cerrado that has an extent of around 2 million km 2 and features high biodiversity with many endemic species. It is characterized by landscape patterns from open grasslands to dense forests, defining a heterogeneous gradient in vegetation structure throughout the biome. While it is undisputed that the Cerrado provides a multitude of valuable ecosystem services, it is exposed to changes, e.g. through large scale land conversions or climatic changes. Monitoring of the Cerrado is thus urgently needed to assess the state of the system as well as to analyze and further understand ecosystem responses and adaptations to ongoing changes. Therefore we explored the potential of dense Landsat time series to derive phenological information for mapping vegetation gradients in the Cerrado. Frequent data gaps, e.g. due to cloud contamination, impose a serious challenge for such time series analyses. We synthetically filled data gaps based on Radial Basis Function convolution filters to derive continuous pixel-wise temporal profiles capable of representing Land Surface Phenology (LSP). Derived phenological parameters revealed differences in the seasonal cycle between the main Cerrado physiognomies and could thus be used to calibrate a Support Vector Classification model to map their spatial distribution. Our results show that it is possible to map the main spatial patterns of the observed physiognomies based on their phenological differences, whereat inaccuracies occurred especially between similar classes and data-scarce areas. The outcome emphasizes the need for remote sensing based time series analyses at fine scales. Mapping heterogeneous ecosystems such as savannas requires spatial detail, as well as the ability to derive important phenological parameters for monitoring habitats or ecosystem responses to climate change. The open Landsat and Sentinel-2 archives provide the satellite data needed for improved analyses of savanna ecosystems globally. [ABSTRACT FROM AUTHOR]