Using Spatial Features to Reduce the Impact of Seasonality for Detecting Tropical Forest Changes from Landsat Time Series

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 km 2 , 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.

Reducing the effects of vegetation phenology on change detection in tropical seasonal biomes

“…These results are in agreement with several previous studies that used spatial information to detect changes (e.g. Acerbi Junior et al 2015;Sertel, Kaya, and Curran 2007;Silveira et al 2018aSilveira et al , 2018b. Acerbi Junior et al (2015) analyzed the potential of semivariograms generated from NDVI values to detect changes in Brazilian savannas.…”

“…The images were downloaded from the United States Geological Survey (USGS) with geometric and atmospheric corrections. We used NDVI (Rouse et al 1973) because the spatial domain of this index has been explored in several LULCC studies (Hamunyela et al, 2016;Silveira et al 2018aSilveira et al , 2018b. However, the proposed approach may be applied to any index.…”

“…We also tested the non-parametric RF algorithm (Breiman 2001) because it has the ability to accommodate many predictor variables with accuracy and efficiency (Breiman 2001;DeVries et al 2016;Ghimire, Rogan, and Miller 2010;Silveira et al 2018a;Zhu et al 2016). We set the number of decision trees (Ntree), to 500 (Lawrence, Wood, and Sheley 2006) and the number of variables for the best split when growing the trees (Mtry) to the default value (log of the number of features + 1) (Millard and Richardson 2015).…”

“…The influence is also reduced when geostatistical features (spatial domain) are incorporated into the analysis of bi-temporal NDVI images in an objectbased change detection approach (Silveira et al 2018b). Although the integration between remote sensing and geostatistical theory was consolidated in the late 1980s, only recent studies have demonstrated that the semivariogram (a geostatistical tool) has strong potential for LULCC detection (Acerbi Junior et al 2015;Gil-Yepes et al 2016;Silveira et al 2018aSilveira et al , 2018b.…”

“…Tropical seasonal biomes (TSBs), such as savannas (also known as Cerrado) and semiarid woodlands (also known as Caatinga), cover 35% of Brazil and consist of several vegetation types ranging from grasslands to forests (Silveira et al 2018a). However, human-induced disturbances, such as deforestation and fires, are threatening these ecosystems (Silva et al 2006;Hansen et al 2013).…”