This paper aims to map vegetation fuel types using a combination of remote sensing data in a complex and diverse plant cover of central Portugal. This study employs Sentinel-1 (S1) and Sentinel-2 (S2) bands, digital elevation model (DEM), and vegetation indices (VIs). Gray-level co-occurrence matrix (GLCM) texture features were generated for the first three principal components (PCs), after applying principal component analysis (PCA) on the S2A spectral bands. First, the fuel type classes based on the FirEUrisk Hierarchical Multipurpose Fuel Classification System (FirEUrisk-HMFCS) were established, then the Random Forest (RF) classifier was employed. Moreover, the feature selection method was used to improve classifier performance. The proposed methodology increased the overall accuracy (OA) of the classification up to 91.89% due to the consideration of the feature selection in the synergy of multisource data, and the role of texture feature data.

The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95).

Ensuring sustainable forest management under continuously changing environmental, economic, and social conditions is currently a pressing concern all over the globe, where climate change, rural abandonment, and high labor costs are considered the main obstacles to overcome. Novel technologies capable of autonomously giving an accurate and relevant indication of the status of the forest are being sought and developed to provide an answer to these problems. High spatial and temporal resolution forest mapping constitutes an essential tool for forest management. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and different spectral regions. Despite the rapid development in remote sensing technologies and methodologies in recent years, data acquired from a single satellite sensor does not allow for a complete understanding of forest ecosystem functions, processes, and dynamics. Fortunately, multi-source data fusion breaks through the constraints of a single sensor and effectively integrates the advantages of multiplatform complementary observations, thus providing opportunities to achieve more accurate and comprehensive forest classification and monitoring. This chapter provides a broad review of applications of remote sensing for forest management purposes. Emphasis is given to recent breakthroughs on multi-source and multi-sensor data fusion approaches, especially satellite and Unmanned Aerial Vehicle (UAV)/drone data fusion, available in the literature of forest ecosystems. Finally, future research trends are discussed.

Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and the threat of wildfires. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, making it an essential tool for vegetation mapping and biomass management. Due to the ever-increasing availability of free data and software, satellites have been predominantly used to map, analyze, and monitor natural resources for conservation purposes. This study aimed to map vegetation from Sentinel-2 (S2) data in a complex and mixed vegetation cover of the Lousã district in Portugal. We used ten multispectral bands with a spatial resolution of 10 m, and four vegetation indices, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI). After applying principal component analysis (PCA) on the 10 S2A bands, four texture features, including mean (ME), homogeneity (HO), correlation (CO), and entropy (EN), were derived for the first three principal components. Textures were obtained using the Gray-Level Co-Occurrence Matrix (GLCM). As a result, 26 independent variables were extracted from S2. After defining the land use classes using an object-based approach, the Random Forest (RF) classifier was applied. The map accuracy was evaluated by the confusion matrix, using the metrics of overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and kappa coefficient (Kappa). The described classification methodology showed a high OA of 90.5% and kappa of 89% for vegetation mapping. Using GLCM texture features and vegetation indices increased the accuracy by up to 2%; however, classification using GLCM texture features and spectral bands achieved the highest OA (92%), indicating the texture features′ capability in detecting the variability of forest species at stand level. The ME and CO showed the highest contribution to the classification accuracy among the GLCM textures. GNDVI outperformed other vegetation indices in variable importance. Moreover, using only S2A spectral bands, especially bands 11, 12, and 2, showed a high potential to classify the map with an OA of 88%. This study showed that adding at least one GLCM texture feature and at least one vegetation index into the S2A spectral bands may effectively increase the accuracy metrics and tree species discrimination.

This paper aims to study the effect of different geometrical and electrical parameters, including the voltage, frequency, dielectric thickness, gap width between electrodes, length of electrodes, number of electrodes, and shapes of electrodes, on the induced velocity by the Dielectric Barrier Discharge (DBD) plasma actuators in quiescent air. In addition, the effect of the course of induced velocity evolution in the downstream of actuators has been investigated for different geometries. The streamwise velocity was obtained through the total and static pressure measurement using silicon tubes. The model is a flat plate equipped with a DBD plasma actuator. These experiments were performed for the peak-to-peak voltage range between 8 and 15 kV, and two values of frequency are equivalent to 5 and 10 kHz. The results showed that the multilinear DBD plasma actuator has a maximal induced velocity in the same voltage and frequency as of a single DBD plasma actuator. Evaluation of the induced velocity along the streamwise direction for multilinear, serpentine, and horseshoe actuators showed that these actuators had more than one induced velocity peak.