RESUMO
The Earth's surface dynamics provide essential information for guiding environmental and agricultural policies. Uncovered and unprotected surfaces experience several undesirable effects, which can affect soil ecosystem functions. We developed a technique to identify global bare surface areas and their dynamics based on multitemporal remote sensing images to aid the spatiotemporal evaluation of anthropic and natural phenomena. The bare Earth's surface and its changes were recognized by Landsat image processing over a time range of 30 years using the Google Earth Engine platform. Two additional products were obtained with a similar technique: a) Earth's bare surface frequency, which represents where and how many times a single pixel was detected as bare surface, based on Landsat series, and b) Earth's bare soil tendency, which represents the tendency of bare surface to increase or decrease. This technique enabled the retrieval of bare surfaces on 32% of Earth's total land area and on 95% of land when considering only agricultural areas. From a multitemporal perspective, the technique found a 2.8% increase in bare surfaces during the period on a global scale. However, the rate of soil exposure decreased by ~4.8% in the same period. The increase in bare surfaces shows that agricultural areas are increasing worldwide. The decreasing rate of soil exposure indicates that, unlike popular opinion, more soils have been covered due to the adoption of conservation agriculture practices, which may reduce soil degradation.
RESUMO
The demand for information on the soil resource to support the establishment of public policies for land use and management has grown exponentially in the last years. However, there are still difficulties to the proper use of already existing information for soil mapping. Here we aimed to establish a protocol for soil mapping using legacy data, magnetic signature and soil attributes evaluation. A total of 493 soil samples were collected at 0-0.20â¯m in the geological domain of Western Plateau of São Paulo State. This work has three parts: First, we performed a classification analysis using soil mapping units (SMU) extracted from conventional soil map and Support Vector Machines algorithm (SVM). As covariates, we used categorical information, such as geology, dissection and landform maps. Second, we used soil attributes to perform a cluster analysis using k-means as partitioning method. To choose the optimal number of clusters, the same number of SMU showed in the conventional soil map (e.g. 34 clusters) were used. The last step was to compare soil and clusters maps predicted by SVM with the conventional soil map. Results showed good performance of SVM for both classifications (clusters and SMU), with overall accuracy of 0.60 and 0.90 respectively. In addition, the distribution of soil attributes within each cluster was more homogeneous and well distributed than within SMU, showing that is very possible to use numerical classification for soil mapping. Future soil surveys could use cluster analysis as a preliminary evaluation for better understanding of tropical soil variations.