Abstract
In forested areas that experience strong seasonality and are undergoing rapid land cover conversion (e.g., Brazilian savannas), the accuracy of remote sensing change detection is affected by seasonal changes that are erroneously classified as having changed. To improve the quality and consistency of regionally important forest change maps, we aim to separate process related change (for example, spectral variability due to phenology) from changes related to deforestations or fires. Seasonal models are typically used to account for seasonality, but fitting a model is difficult when there are insufficient data points in the time series. In this research, we utilize remotely sensed data and related spectral trends and the spatial context at the object level to evaluate the performance of geostatistical features to reduce the impact of seasonality from the NDVI (Normalized Difference Vegetation Index) of Landsat time series. The study area is the São Romão municipality, totaling 2440 km2, and is part of the Brazilian savannas biome. We first create image objects via multiresolution segmentation, basing the objects on the characteristics found in the first image (2003) of the 13-year time series. We intersected the objects with the NDVI images in order to extract semivariogram indices, the RVF (Ratio Variance—First lag) and AFM (Area First lag—First Maximum), and spectral information (average and standard deviation of NDVI values) to generate the time series from these features and to derive Spatio-Temporal Metrics (change and trend) to train a Random Forest (RF) algorithm. The NDVI spatial variability, captured by the AFM semivariogram index time series produced the best result, reaching 96.53% of the overall accuracy (OA) to separate no-change from forest change, while the greatest inter-class confusion occurred using the average of the NDVI values time series (OA = 63.72%). The spatial context approach we presented is a novel approach for the detection of forest change events that are subject to seasonality (and possible miss-classification of change) and mitigating the effects of forest phenology without the need for specific de-seasoning models.
Highlights
Savannas are the dominant biome in South Hemisphere, covering approximately 45% of the area of South America [1]
The timing of the change is an important feature of forest monitoring, we did not attempt to model the time of the change, as our main focus is not hinged on the capacity to date forest changes, rather we focus on the advantages of using variability measures as input data to train the Random Forest (RF) algorithm
We have developed a method to mitigate the presence of phenological effects from time series using NDVI trends to detect changes over a savannah forest ecosystem
Summary
Savannas are the dominant biome in South Hemisphere, covering approximately 45% of the area of South America [1]. In Brazil, this biome consists of a mosaic of land cover types, undergoing a strong seasonality in climate, accompanied with a widespread occurrence of fires that impose environmental pressures with the most rapid land conversion in Brazil, exceeding that of the tropical forests [2]. Much of the effort estimating forest changes in Brazil has been focused on the tropical rainforests with less attention dedicated to the less humid seasonal regions [4]. Many different models can be used to estimate deforestation trends in tropical regions [5], in Brazil, most of these models were developed for the Amazon region with a few studies representing the Savannas biome [4,6]. Human-induced disturbances are threatening these ecosystems, systematic investigations of land cover change are lacking [7]
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