Forest growth in tropical regions is regulated in part by climatic factors, such as precipitation and temperature, and by soil factors, such as nutrient availability and water storage capacity. We examined a decade of growth data from Eucalyptus clonal plantations from over 113,000 forest inventory plots across a 10 million-ha portion of Mato Grosso do Sul in southwestern Brazil. From this full dataset, three subsets were screened: 71,000 plots to characterize growth and yield across water table depth classes, 17,000 plots to build generalized models, and 50,000 plots for clone-based analyses. Average precipitation varied little across the region (1150 to 1270 mm yr(-1)), but water table depth ranged from less than 10 m to over 100 m. Where the water table was within 10 m of the surface, about 20 % of the total water used by trees came from this saturated zone. Water tables deeper than 50 m contributed very little to tree water use. Sites with a water table within 10 m averaged 47 m(3) ha(-1) yr(-1) in stem growth (mean annual increment, MAI) across a full rotation, compared to less than 37 m(3) ha(-1) yr(-1) for sites with water tables deeper than 50 m. Drought-induced canopy damage rose from 7 % to 30 % along the water tables depth gradient, while tree mortality rose nearly fourfold. The optimal stocking level was about 1360 trees ha(-1) where water tables were accessible, declining to 1080 trees ha(-1) where they were not. Among the 15 most planted Eucalyptus clones, increases in MAI from the lowest to highest water table depths ranged from + 4.8 to + 16.8 m(3) ha(-1) yr(-1) , reflecting significant genotype-environment interactions. On average, MAI decreased by 0.8 m(3) ha(-1) yr(-1) (ranging from 0.4 to 1.4) for every 10 m increase in water table depth. Similarly, the Site Index at base age 7 years declined from 31 m to 27 m, with an average reduction of 0.25 m per 10 m increase in water table depth. Physiographic modeling of water table depths offers useful information for forest management practices like forest inventory and planning, clonal allocation, optimized planting densities, fertilization strategies, coppice techniques, and other landscape-specific strategies like tree breeding zones.

Early water stress detection is important for water use yield and sustainability. Traditional methods using the Internet of Things (IoT), such as soil moisture sensors, usually do not provide timely alerts, causing inefficient water use and, in some cases, crop damage. This research presents an innovative early water stress detection method in lettuce plants using Thermal Infrared (TIR) and RGB images in a controlled lab setting. The proposed method integrates advanced image processing techniques, including background elimination via Hue-Saturation- Value (HSV) thresholds, wavelet denoising for thermal image enhancement, RGB-TIR fusion using Principal Component Analysis (PCA), and Gaussian Mixture Model (GMM) clustering to segment stress regions. The leaves stressed areas annotated in the RGB image through yellow pseudo-coloring. This approach is predicated on the fact that when stomata close, transpiration decreases, which causes an increase in the temperature of the affected area. Experimental results reveal that this new approach can detect water stress up to 84 h earlier than conventional soil humidity sensors. Also, a comparative analysis was conducted where key components of the proposed hybrid framework were omitted. The results show inconsistent and inaccurate stress detection when excluding wavelet denoising and PCA fusion. A comparative analysis of image processing performed on a single- board computer (SBC) and through cloud computing over 5 G showed that SBC was 8.27% faster than cloud computing over a 5 G connection. The proposed method offers a more timely and accurate identification of water stress and promises significant benefits in improving crop yield and reducing water usage in indoor farming.

Soil freeze-thaw state influences multiple terrestrial ecosystem processes, such as soil hydrology and carbon cycling. However, knowledge of historical long-term changes in the timing, duration, and temperature of freeze-thaw processes remains insufficient, and studies exploring the combined or individual contributions of climatic factors-such as air temperature, precipitation, snow depth, and wind speed-are rare, particularly in current thermokarst landscapes induced by abrupt permafrost thawing. Based on ERA5-Land reanalysis, MODIS observations, and integrated thermokarst landform maps, we found that: 1) Hourly soil temperature from the reanalysis effectively captured the temporal variations of in-situ observations, with Pearson' r of 0.66-0.91. 2) Despite an insignificant decrease in daily freeze-thaw cycles in 1981-2022, other indicators in the Qinghai-Tibet Plateau (QTP) changed significantly, including delayed freezing onset (0.113 d yr- 1), advanced thawing onset (-0.22 d yr- 1), reduced frozen days (-0.365 d yr- 1), increased frozen temperature (0.014 degrees C yr- 1), and decreased daily freeze-thaw temperature range (-0.015 degrees C yr- 1). 3) Total contributions indicated air temperature was the dominant climatic driver of these changes, while indicators characterizing daily freeze-thaw cycles were influenced mainly by the combined effects of increased precipitation and air temperature, with remarkable spatial heterogeneity. 4) When regionally averaged, completely thawed days increased faster in the thermokarstaffected areas than in their primarily distributed grasslands-alpine steppe (47.69%) and alpine meadow (22.64%)-likely because of their stronger warming effect of precipitation. Locally, paired comparison within 3 x 3 pixel windows from MODIS data revealed consistent results, which were pronounced when the thermokarst-affected area exceeded about 38% per 1 km2. Conclusively, the warming and wetting climate has significantly altered soil freeze-thaw processes on the QTP, with the frozen soil environment in thermokarstaffected areas, dominated by thermokarst lakes, undergoing more rapid degradation. These insights are crucial for predicting freeze-thaw dynamics and assessing their ecological impacts on alpine grasslands.

This study analyzes the effects of Hurricane Eta on the Chiriqui Viejo River basin, revealing the significant impact of extreme weather events on the hydrological dynamics of the region. The maximum rainfall recorded on November 4, 2020, reached 223.8 mm, while the flow in Paso Canoa reached 638.03 m3/s, demonstrating the magnitude of the event and the inability of the basin to handle such high volumes of water. Through a detailed analysis, it was observed that soil saturation resulted in direct runoff of up to 70.0 mm that same day, which shows that the infiltration capacity of the soil was quickly exceeded. Despite the damage observed, there are currently no advanced hydrological studies on extreme events in critical basins such as the Chiriqui Viejo River. This lack of research reflects a serious lack of planning and assessment of the risks associated with phenomena of this magnitude. One of the most critical problems found is the lack of specialized hydrology professionals, who are essential to carry out detailed studies and ensure sustainable management of water resources. In a context where climate change increases the frequency and intensity of extreme events, the absence of hydrologists in the region puts the resilience of the basin to future disasters at risk. The basin's hydraulic system demonstrated its inability to handle high flows, underscoring the need to improve flood control and water retention infrastructure. In addition, the lack of effective hydrological planning and coordination in the management of hydraulic infrastructures compromises both the safety of downstream communities and the sustainability of hydroelectric reservoirs, vital for the region.

This article investigates the influence of climatic and geographical characteristics in south-western region of Bangladesh on the temporal dynamics of post-cyclone impacts, with a critical focus on biophysical contexts. By quantitatively assessing the environmental consequences of cyclones Amphan (2020), Yaas (2021), Mocha (2023) and Remal (2024), the study offers a nuanced understanding of flood damage extent and vegetation health, measured through advanced remote sensing and geospatial techniques. Using Sentinel-1 (GRD) and Sentinel-2 (MSI) satellite imageries from 2020 to 2024, the study has examined post-cyclone changes of vegetation health and flood damage extent using available indices such as Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI). The results exhibit substantial spatial disparities occurred due to the cyclone events, with NDVI variations ranging from - 0.124 to 0.546 (Amphan), - 0.033 to 0.498 (Mocha), - 0.086 to 0.458 (Yaas), and - 0.061 to 0.362 (Remal), indicating significant ecological stress. Corresponding SAVI changes ranged from - 0.001 to 0.396 (Amphan), - 0.029 to 0.338 (Mocha), - 0.002 to 0.345 (Yaas), and - 0.0524 to 0.269 (Remal). Negative indices underscore potential vegetation degradation, while positive values indicate resilience or post-cyclone recovery. Furthermore, flood damage analysis indicates to a more severe and unevenly distributed impact than previously recognized, particularly in areas with pre-existing vulnerabilities with the damage extent variations between - 35.918 to - 2.0093 (Amphan), - 35.334 to - 4.4059 (Mocha), - 34.806 to - 0.94921 (Yaas), and - 48.469 to 0.00255 (Remal). The Geographically Weighted Regression (GWR), model demonstrates a robust relationship, with r2 values of 0.894, 0.889, 0.899, and 0.95, indicating that approximately 85% of the ecological changes are driven by fluctuations of vegetation due to flood. The insight from this research provides a foundation of flood damage assessment technique occurred by cyclones in a short span of time to aid immediate policy recommendations to enhance resilience in remote areas of the coastal regions of Bangladesh.

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.

Agricultural drought is a natural and damaging phenomenon that is especially harmful to rainfed agriculture. It occurs when there is insufficient soil moisture in the root zone for plants to survive between two rainfall events. In the absence of soil moisture, a variety of losses, including soil evaporation and plant transpiration, cause an imbalance between water supply and water loss. An evapotranspiration-based index was used here to assess agricultural drought. We applied this framework to a less studied area near Fariman City in the northeast part of IRAN. Two time periods were selected for comparison including 2015 and 2016 spring season that are associated with dry and wet conditions, respectively. To calculate the drought index, actual and potential evapotranspiration were estimated by the Surface Energy Balance Algorithm for Land (SEBAL), the upgraded Priestley-Taylor method and remote sensing data. The Relative Water Deficit Index (RWDI) illustrated that lack of water in rainfed lands and pastures for the dry period was obtained from 80 to 100 percent, whereas this was between 50 and 70% for the wet period.

In the mountainous headwaters of the Colorado River episodic dust deposition from adjacent arid and disturbed landscapes darkens snow and accelerates snowmelt, impacting basin hydrology. Patterns and impacts across the heterogenous landscape cannot be inferred from current in situ observations. To fill this gap daily remotely sensed retrievals of radiative forcing and contribution to melt were analyzed over the MODIS period of record (2001-2023) to quantify spatiotemporal impacts of snow darkening. Each season radiative forcing magnitudes were lowest in early spring and intensified as snowmelt progressed, with interannual variability in timing and magnitude of peak impact. Over the full record, radiative forcing was elevated in the first decade relative to the last decade. Snowmelt was accelerated in all years and impacts were most intense in the central to southern headwaters. The spatiotemporal patterns motivate further study to understand controls on variability and related perturbations to snow water resources.

The recent increase of the air temperature due to the global climate change is considered as one of the important reasons for the wildfires increase in the world, even in areas where the wildfires are not that common. In addition to the various physical damages adversely affecting the ecological balance, harmful gases and solid particles are released into the atmosphere due to wildfires, causing serious health problems. In this study, impacts of the most serious forest fire in modern history of the country lasting 16 days from 23rd of July 2022 in the National Park Bohemian Switzerland in the D & ecaron;& ccaron;& iacute;n district, Czech Republic, were investigated using remote sensing satellite datasets by cloud-based Google Earth Engine (GEE) platform. The normalized difference moisture index (NDMI), normalized burn ratio index (NBR), normalized difference vegetation index (NDVI), land surface temperature (LST) and soil moisture index (SMI) were calculated from Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI and TIRS) dataset for the dates of 31st October 2021, 18th June 2022, and 31st October 2022. Relationship of the remote sensing indices were calculated to estimate the impacts of the wildfire. Furthermore, distribution of nitrogen dioxide (NO2) was extracted using Sentinel-5P TROPOMI (Tropospheric Monitoring Instrument) to observe changes before and after the forest fire in the study region. The burnt area approximately 13.20 km2 from the total area of 79.28 km2 was detected using different time series of the remote sensing indices in the national park.

Soil erosion poses a considerable threat to ecosystem services around the world. Among these, it is extremely problematic for archaeological sites, particularly in arable landscapes where accelerated soil degradation has been widely observed. Conversely, some archaeological deposits may obtain a certain level of protection when they are covered by eroded material, thereby lessening the impacts of phenomena such as plow damage or bioturbation. As a result, detailed knowledge of the extent of colluvial deposition is of great value to site management and the development of appropriate methodological strategies. This is particularly true of battlefield sites, where the integrity of artifacts in the topsoil is of great importance and conventional metal detection (with its shallow depth of exploration) is relied upon as the primary method of investigation. Using the Napoleonic battlefield of Waterloo in Belgium as a case study, this paper explores how different noninvasive datasets can be combined with ancillary data and a limited sampling scheme to map colluvial deposits in high resolution and at a large scale. Combining remote sensing, geophysical, and invasive sampling datasets that target related phenomena across spatial scales allows for overcoming some of their respective limitations and derives a better understanding of the extent of colluvial deposition.