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Quickly and accurately obtaining information on rice planting areas is crucial for maintaining food security and social stability. Based on the time series of Sentinel-1A images, this study proposes an automatic rice mapping method called the Dynamic Threshold Time-Weighted Dynamic Time Warping method (DT-TWDTW). This method consists of two parts. First, rice samples are extracted automatically by a dynamic threshold method based on the unique flooding signal before and after rice transplanting. Then, the Time-Weighted Dynamic Time Warping (TWDTW) method is used for rice mapping based on the cumulative cost matrix between unknown pixels and rice samples. The results show that the DT-TWDTW method can achieve satisfactory mapping accuracy with an overall accuracy of 94.6%, higher than random forest (89.9%), and support vector machine (91.7%). This study provides a new approach and perspective for remote sensing monitoring of rice, especially for some regions without prior phenological knowledge and collection of the rice samples. [ABSTRACT FROM AUTHOR]

Efficiently obtaining tobacco planting area is significant for rationally allocating tobacco resources and realizing the balance between supply and demand. However, tobacco fields are characterized by fragmentation in hilly areas, which brings various challenges to extracting the tobacco planting area. A 16-square-kilometre tobacco planting region in Xuancheng City, Anhui Province, China, was selected as the study area in this paper. First, the single-temporal full-feature sets (STFFS), a time-series full-feature set (TFFS) and a time-series optimal-feature set (TOFS) were constructed from multi-period Sentinel-2 images, respectively. Then, we applied Random Forest (RF), Support Vector Machine (SVM), Neural Network Classification (NNC) and Maximum Likelihood Classification (MLC) to classify the feature sets and compare the classification accuracy. The experimental results demonstrate that: (1) The best growth stage of tobacco remote sensing recognition is the mulching film phase of the spherical plant stage (ST1 period). (2) The classification accuracy indicates that TOFS outperforms the STFFS. (3) TOFS can still maintain the same classification accuracy as TFFS with fewer feature dimensions. (4) The RF classifier has great stability and robustness, which achieve an overall accuracy (OA) of 90.65%, 93.50%, and 93.09% based on the feature sets of ST1 period, TFFS, and TOFS, respectively. [ABSTRACT FROM AUTHOR]

Using remote-sensing technology to accurately map the composition and distribution of tree species is vital for sustainable forest resource management. Sentinel-2 data with the dense time-series observations enable to identify tree species. However, few studies clarify the differences in classification using Sentinel-2 images in natural forest and planted forest. Two study areas with different forest environments (planted forest and natural forest) were selected to evaluate the potential of Sentinel-2 imagery. Our results show that red-edge band, short-wave infrared (SWIR) band and vegetation indices (VIs), such as Red-Edge Normalized Difference Vegetation Index (ReNDVI), Soil-Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), have important effects on tree species classification, especially in the growing season. By using two machine learning algorithms (support vector machine [SVM] and random forest [RF]), the results show that the classification accuracy for planted forests (91.27% in SVM and 88.35% in RF) significantly exceeds that of natural forests (84.34% in SVM and 81.03% in RF). This accuracy difference may be related to the spatial heterogeneity inside the forest and the surrounding environmental implications. Although the multi-temporal Sentinel-2 images produce satisfactory accuracy for classifying tree species, further research is needed to improve the classification accuracy. [ABSTRACT FROM AUTHOR]

North American ash species (Fraxinus spp.) are under dire threat from the invasive pest, emerald ash borer (Agrilus planipennis, EAB), and mapping ash trees is of utmost significance for conservation and prevention efforts. We developed remote sensing techniques to identify ash trees at the individual tree level in a mixed-species forest in Maine, USA, using hyperspectral bands combined with derived spectral vegetation indices (SVIs), texture metrics using Random Forest (RF) and Support Vector Machine (SVM) algorithms. The pixel-based SVM-Optimized model proved to be the most accurate classification method with the lowest number of input variables including six Minimum Noise Fraction (MNF)-reduced bands, four SVIs and two texture variables, achieving 78.2% overall accuracy and 81.0% ash Producer's Accuracy. The technique presented in this study could be used to map ash trees throughout Maine and other states with similar forest types for future preservation efforts. [ABSTRACT FROM AUTHOR]