Abstract
This paper presents a new approach for rapidly assessing the extent of land use and land cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of fraction images derived from the linear spectral mixing model (LSMM) instead of original bands. The LSMM was applied to the Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data composites from 2015 (~73 scenes/year, cloud-free images, in theory), generating vegetation, soil, and shade fraction images. These fraction images highlight the LULC components inside the pixels. The other new idea is to reduce these time series to only six single bands representing the maximum and standard deviation values of these fraction images in an annual composite, reducing the volume of data to classify the main LULC classes. The whole image classification process was conducted in the Google Earth Engine platform using the pixel-based random forest algorithm. A set of 622 samples of each LULC class was collected by visual inspection of PROBA-V and Landsat-8 Operational Land Imager (OLI) images and divided into training and validation datasets. The performance of the method was evaluated by the overall accuracy and confusion matrix. The overall accuracy was 92.4%, with the lowest misclassification found for cropland and forestland (<9% error). The same validation data set showed 88% agreement with the LULC map made available by the Landsat-based MapBiomas project. This proposed method has the potential to be used operationally to accurately map the main LULC areas and to rapidly use the PROBA-V dataset at regional or national levels.
Highlights
The Mato Grosso state presents the second largest area of deforestation and forest degradation in the Brazilian Legal Amazonia (BLA), a Brazilian political division that encompasses the states of Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima, Tocantins, and part of Maranhão [1,2].Mato Grosso is the main grain and cattle beef producer in the BLA [3,4]
In terms of vegetation fraction, forestland, cropland, and pastureland classes presented relatively high responses due to the high absorption of photosynthetically active radiation by leaves and canopies throughout the year. These land use and land cover (LULC) classes differ by their seasonality, which is more evident in croplands and pasturelands than in forests, as noted by the high standard deviation of the vegetation fraction (Figure 6D)
A novel method to produce LULC maps based on the classification of the three maximum fraction images and three standard deviations derived from annual composites of the 100-m spatial resolution Project for On-Board Autonomy-Vegetation (PROBA-V) dataset was presented
Summary
The Mato Grosso state presents the second largest area of deforestation and forest degradation in the Brazilian Legal Amazonia (BLA), a Brazilian political division that encompasses the states of Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima, Tocantins, and part of Maranhão [1,2].Mato Grosso is the main grain and cattle beef producer in the BLA [3,4]. The Mato Grosso state presents the second largest area of deforestation and forest degradation in the Brazilian Legal Amazonia (BLA), a Brazilian political division that encompasses the states of Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima, Tocantins, and part of Maranhão [1,2]. Its northern part is covered by the Amazonia biome, while the southern part is covered by the Cerrado and Pantanal biomes This state has a key role in understanding the Brazilian land use and land cover (LULC) dynamics, since several processes of change in LULC that are occurring in Mato Grosso are being repeated in other. Brazilian states covered by the Amazonia and Cerrado biomes These are the cases of the states of Pará, Land 2020, 9, 139; doi:10.3390/land9050139 www.mdpi.com/journal/land. LULC monitoring is crucial to estimate CO2 emissions from deforestation and degradation losses [10,11], as well as to prevent soil erosion, the introduction of invasive species, and resource losses such as timber and water supply for local inhabitants [12,13]
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