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

Abstract

Tropical seasonal biomes (TSBs), such as the savannas (Cerrado) and semi-arid woodlands (Caatinga) of Brazil, are vulnerable ecosystems to human-induced disturbances. Remote sensing can detect disturbances such as deforestation and fires, but the analysis of change detection in TSBs is affected by seasonal modifications in vegetation indices due to phenology. To reduce the effects of vegetation phenology on changes caused by deforestation and fires, we developed a novel object-based change detection method. The approach combines both the spatial and spectral domains of the normalized difference vegetation index (NDVI), using a pair of Operational Land Imager (OLI)/Landsat-8 images acquired in 2015 and 2016. We used semivariogram indices (SIs) as spatial features and descriptive statistics as spectral features (SFs). We tested the performance of the method using three machine-learning algorithms: support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The results showed that the combination of spatial and spectral information improved change detection by correctly classifying areas with seasonal changes in NDVI caused by vegetation phenology and areas with NDVI changes caused by human-induced disturbances. The use of semivariogram indices reduced the effects of vegetation phenology on change detection. The performance of the classifiers was generally comparable, but the SVM presented the highest overall classification accuracy (92.27%) when using the hybrid set of NDVI-derived spectral-spatial features. From the vegetated areas, 18.71% of changes were caused by human-induced disturbances between 2015 and 2016. The method is particularly useful for TSBs where vegetation exhibits strong seasonality and regularly spaced time series of satellite images are difficult to obtain due to persistent cloud cover.

Estimating Aboveground Biomass Loss from Deforestation in the Savanna and Semi-arid Biomes of Brazil between 2007 and 2017

“…In general, these data are empirically linked to AGB measurements of field plots, ranging from simple linear regression to complex machine learning algorithms (MLA) [17,18], and regression Kriging technique [19,20]. Landsat images are the most mediumresolution data commonly used due the longest data record along with a spatial resolution of 30 m. However, Savannas and Semi-arid woodland biomes are the Brazilian biomes that have most suffered human-induced disturbances [5,21,22], and are among the most fragmented and threatened ecosystems in the world [23]. Estimating Aboveground Biomass Loss from Deforestation in the Savanna and Semi-arid Biomes… DOI: http://dx.doi.org /10.5772/intechopen.85660 The expectations regarding their future are not very optimistic.…”

Quantized ternary pattern and singular value decomposition for the efficient mining of sequences in SRSI images

“…The moving window method was adopted to analyze the vegetation pattern. The LIAS helped in increasing the accuracy of classifying the spatial patterns [14,24] In the network connection using wireless sensors, the sequential patterns were discovered using an incremental mining algorithm to make the mining process more efficient. A traditional mining algorithm, namely.…”

On a Data-Driven Approach for Detecting Disturbance in the Brazilian Savannas Using Time Series of Vegetation Indices

“…When compared to band reflectance, VIs can reduce data variability associated with the geometry of data acquisition and with terrain illumination or topographic effects. This is especially valid for VIs with a band reflectance normalization on their formulations [20][21][22]. Algorithms like the Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) retrieve the annual rates of disturbance from Landsat time series [16,23].…”