Abstract
Detecting disturbances in native vegetation is a crucial component of many environmental management strategies, and remote sensing-based methods are the most efficient way to collect multi-temporal disturbance data over large areas. Given that there is a large range of datasets for monitoring, analyzing, and detecting disturbances, many methods have been well-studied and successfully implemented. However, factors such as the vegetation type, input data, and change detection method can significantly alter the outcomes of a disturbance-detection study. We evaluated the spatial agreement of disturbance maps provided by the Breaks For Additive Season and Trend (BFAST) algorithm, evaluating seven spectral indices in three distinct vegetation domains in Brazil: Atlantic forest, savanna, and semi-arid woodland, by assessing levels of agreement between the outputs. We computed individual map accuracies based on a reference dataset, then ranked their performance, while also observing their relationships with specific vegetation domains. Our results indicated a low rate of spatial agreement among index-based disturbance maps, which itself was minimally influenced by vegetation domain. Wetness indices produced greater detection accuracies in comparison to greenness-related indices free of saturation. The normalized difference moisture index performed best in the Atlantic forest domains, yet performed poorest in semi-arid woodland, reflecting its specific sensitivity to vegetation and its water content. The normalized difference vegetation index led to high disturbance detection accuracies in the savanna and semi-arid woodland domains. This study offered novel insight into vegetation disturbance maps, their relationship to different ecosystem types, and corresponding accuracies. Distinct input data can produce non-spatially correlated disturbance maps and reflect site-specific sensitivity. Future research should explore algorithm limitations presented in this study, as well as the expansion to other techniques and vegetation domains across the globe.
Highlights
Anthropogenic disturbances in tropical environments are considered one of the major drivers of biodiversity loss [1] and the second largest source of anthropogenic greenhouse gas emissions [2]
This study addressed the following questions: (1) How do vegetation disturbance maps derived from Landsat-based spectral indices spatially agree? (2) How accurate are these vegetation disturbance maps when compared to a reference dataset? (3) How does the vegetation domain influence these agreements? our objectives were: (1) To evaluate the spatial agreement between disturbance maps produced using seven spectral indices derived from Landsat Thematic Mapper/Operational Land Imager (TM/OLI) and input into Breaks For Additive Season and Trend (BFAST) Monitor, and the influence of vegetation domain on this agreement; and (2) to evaluate the accuracies of these index-derived disturbance maps and their relationship with vegetation domain
The three greenness indices adjusted for signal saturation increase the dynamic range of the vegetation signal, which induces a higher sensitivity to topographic illuminations effect and leads to significant changes in the observed spectral characteristics of areas with strong topographic relief
Summary
Anthropogenic disturbances in tropical environments (i.e., selective logging and wildfires) are considered one of the major drivers of biodiversity loss [1] and the second largest source of anthropogenic greenhouse gas emissions [2]. In the 1990–2010 period, global net losses of tropical forests averaged 6 million hectares per year (approximately 0.38% annually) [3] Such disturbance rates have received worldwide attention as tropical vegetation domains play such a key role in global systems. Mapping disturbances in tropical domains plays a pivotal role in many environmental management strategies, and remote sensing data are the only feasible way to detect and monitor these changes over large areas. This is not a trivial task, as change detection is subject to a variety of noise factors including natural forest phenology, cloud cover, atmospheric scattering, and geometric errors [8]. We require an understanding of the sensitivity and generalizability of change-detection methods across a variety of different vegetation types [9]
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