The wheat powdery mildew (WPM) is one of the most severe crop diseases worldwide, affecting wheat growth and causing yield losses. The WPM was a bottom-up disease that caused the loss of cell integrity, leaf wilting, and canopy structure damage with these symptoms altering the crop's functional traits (CFT) and canopy spectra. The unmanned aerial vehicle (UAV)-based hyperspectral analysis became a mainstream method for WPM detection. However, the CFT changes experienced by infected wheats, the relationship between CFT and canopy spectra, and their role in WPM detection remained unclear, which might blur the understanding for the WPM infection. Therefore, this study aimed to propose a new method that considered the role of CFT for detecting WPM and estimating disease severity. The UAV hyperspectral data used in this study were collected from the Plant Protection Institute's research demonstration base, Xinxiang city, China, covering a broad range of WPM severity (0-85 %) from 2022 to 2024. The potential of eight CFT [leaf structure parameter (N), leaf area index (LAI), chlorophyll a + b content (Cab), carotenoids (Car), Car/Cab, anthocyanins (Ant), canopy chlorophyll content (CCC) and average leaf angle (Deg)] obtained from a hybrid method combining a radiative transfer model and random forest (RF) and fifty-five narrow-band hyperspectral indices (NHI) was explored in WPM detection. Results indicated that N, Cab, Ant, Car, LAI, and CCC showed a decreasing trend with increasing disease severity, while Deg and Car/Cab exhibited the opposite pattern. There were marked differences between healthy samples and the two higher infection levels (moderate and severe infection) for Cab, Car, LAI, Deg, CCC, and Car/Cab. N and Ant only showed significant differences between the healthy samples and the highest infection level (severe infection). As Cab, Car, and Ant decreased, the spectral reflectance in the visible light region increased. The decrease in N and LAI was accompanied by a reduction in reflectance across the entire spectral range and the near-infrared area, which was exactly the opposite of Deg. The introduction of CFT greatly improved the accuracy of the WPM severity estimation model with R2 of 0.92. Features related to photosynthesis, pigment content, and canopy structure played a decisive role in estimating WPM severity. Also, results found that the feature importance showed a remarkable interchange as increasing disease levels. Using features that described canopy structure changes, such as optimized soil adjusted vegetation index, LAI, visible atmospherically resistant indices, and CCC, the mild infection stage of this disease was most easily distinguished from healthy samples. In contrast, most severe impacts of WPM were best characterized by features related to photosynthesis (e.g., photochemical reflectance index 515) and pigment content (e.g., normalized phaeophytinization index). This study help deepen the understanding of symptoms and spectral responses caused by WPM infection.

The occurrence of hurricanes in the southern U.S. is on the rise, and assessing the damage caused to forests is essential for implementing protective measures and comprehending recovery dynamics. This work aims to create a novel data integration framework that employs LANDSAT 8, drone-based images, and geographic information system data for change detection analysis for different forest types. We propose a method for change vector analysis based on a unique spectral mixture model utilizing composite spectral indices along with univariate difference imaging to create a change detection map illustrating disturbances in the areas of McDowell County in western North Carolina impacted by Hurricane Helene. The spectral indices included near-infrared-to-red ratios, a normalized difference vegetation index, Tasseled Cap indices, and a soil-adjusted vegetation index. In addition to the satellite imagery, the ground truth data of forest damage were also collected through the field investigation and interpretation of post-Helene drone images. Accuracy assessment was conducted with geographic information system (GIS) data and maps from the National Land Cover Database. Accuracy assessment was carried out using metrics such as overall accuracy, precision, recall, F score, Jaccard similarity, and kappa statistics. The proposed composite method performed well with overall accuracy and Jaccard similarity values of 73.80% and 0.6042, respectively. The results exhibit a reasonable correlation with GIS data and can be employed to assess damage severity.

Global warming, increasing population, and parched soils are escalating the frequency and intensity of forest fires. Global warming raises temperatures and extends droughts, making forests more susceptible to fires. A growing population pressures forest areas for settlement and agriculture, increasing fire risk. Dry soils and vegetation ignite easily, accelerating fire spread. After fires, damage assessment and reforestation are crucial. This study examines the impact of the July 18, 2023, forest fire on Rhodes Island's vegetation. Using spectral analyses of Landsat 8 images, the fire's damage to vegetation was assessed. The NBR (Normalized Burn Ratio) index determined pre- and post-fire vegetation changes. The burned area was calculated using dNDVI and dNBR. While dNDVI measures vegetation health, dNBR detects burned areas before and after a fire. The burned area was 16.037 ha using dNDVI and 17.678 ha using dNBR, showing consistent results. The burned area signals significant ecological consequences like habitat loss, negative impacts on biodiversity, and increased soil erosion. These analyses are essential for planning ecosystem recovery and developing appropriate restoration strategies after a fire.

Background. Agricultural lands play a key role in ensuring the food security of the population and the development of the country's economy. However, excessive wetting poses a significant threat to these lands, as a result of which the conditions for the formation of soils with signs of glaciation and low fertility are formed within the lower relief elements, which significantly reduces their potential. In order to highlight the problems of geospatial identification of micro -recessed landforms (MRLF) on agricultural lands, the article uses spectral indices based on the data of RSE. Methods. 6 spectral indices were selected for the research. They were used to obtain data on areas of soil subsidence on arable lands, namely: NDWI, NWI, NDMI, NDVI, OSAVI, WRI. Solving research tasks involved the use of data from the Sentinel -2A satellite system. In order to visualize the spread of MRLF on the research territory, a high -resolution image (0.2 m per 1 pixel) obtained in the Digitals Professional 5.0 software was used. Processing and geospatial visualization of the RSE data were performed in the Arc Map environment of the Arc GIS 10.8 program using the raster calculator tool. Results. Within the reference fields, the dynamics of the values of water and vegetation indices were constructed and analyzed, and the identification ability for the geospatial separation of soil areas with signs of hydromorphism was evaluated. It is shown that the identification capacity of the indices depends not only on the level of soil moisture, but also on the biomass of vegetation (scales of crop damage), which is indicated by the high information capacity of the traditional vegetation index NDVI. The most informative index ranges were established: for NDVI, the range is from -0.117 to -0.024 with an identification percentage of 98.0 %; for OSAVI - 78.0 % with a range of 0.255-0.313; for NDMI with a range variation of -0.041 to -0.149 and an identification percentage of 56.0. Conclusions. The results of remote identification of the areas of the MRLF enabled to obtain information about the moisture content of the soils of the arable lands of the research area. The ability of the specified indices during the geospatial identification of microrecessed landforms (MRLF) and soil areas within them with signs of hydromorphism was evaluated. It is demonstrated that the use of orthophotos with a resolution of 0.2 m per 1 pixel serves as important supporting aids of successful completion of the specified tasks. It was found that the identification ability of water indices on test fields without existing vegetation is too low. On the other hand, the shielding of the soil surface by vegetation with areas of damaged crops makes it possible to isolate MRLF. The obtained information can be used during the development of the methodology of soil science surveying and planning of largescale soil survey activities.