Bangladesh Forest Department had undertaken co-management of protected area (PA) initiatives for halting the rapid rate of forest degradation and deforestation in the country. However, only few research has been conducted to assess the effectiveness of co-management. In this study, drawing empirical data from two co-managed PA, namely, Bariyadhala National Park (BDNP) and Hazarikhil Wildlife Sanctuary (HWS), we investigated the impacts of co-management on spatio-temporal patterns of land use and land cover change, forest fragmentation, and local peoples’ perception on how co-management approach was underpinning these changes in the studied PA landscape. Results showed that forest cover (4800 ha gain) and forest patches (26 patch gain) had increased in two studied PA while agricultural practices and resources extractions from forest had decreased during the study period. The increase in forest coverage might be attributed to the reforestation program (3253.81 ha) undertaken under the co-management project. More than 90% of respondents admitted that various activities on co-management such as awareness creation, restriction on hunting, and forest products collection have had positive impacts on biodiversity conservation. The findings of this research would be useful to formulate future course of actions regarding forest PA co-management in Bangladesh, and elsewhere.

Climate change affects plant phenology, spatial distribution, and even extinction of vulnerable species. Dipterocarpus turbinatus, locally known as garjan, is a valuable but vulnerable native tree species of Bangladesh whose spatial distribution under future climate change scenarios is not fully understood. The aim of this study was to examine the effects of present and future climatic scenarios on spatiotemporal distribution of D. turbinatus. We used maximum entropy species distribution modeling to perform the present and future habitat suitability of garjan under different climate scenarios. The representative concentration pathways (RCP) 2.6 and 8.5 were considered for bioclimatic variables from the Global Climate Model – Hadley Global Environment Model 2 Atmosphere-Ocean. The predictive accuracy of the model was more than 97% in both the training and test data. The prediction results suggest that compared to present areas (7624 km²) under moderate habitat class it will be 2755 km² and 1239 km², respectively, in 2050 and 2070 under RCP2.6 scenario and decreases more rapidly under RCP8.5 scenario. Besides, the prediction also indicates that the habitat of the species will shift toward the high altitudinal south-eastern corner of the country whereas local extinction might occur in the north-eastern part during 2070.

In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R² = 0.76), photosystem II efficiency (R² = 0.78), maximum fluorescence (R² = 0.64), and apparent transpiration rate (R² = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R² = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R² = 0.56) and vapor pressure deficit (R² = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R² = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress.

Woody canopies regulate exchanges of energy, water and carbon, and their three-dimensional (3D) structure supports much of terrestrial biodiversity. Remote sensing technologies such as airborne laser scanning (ALS) now enable the 3D mapping of entire landscapes. However, we lack the large, harmonized and geographically representative ALS collections needed to build a global picture of woody ecosystem structure. To address this challenge, we developed the Global Canopy Atlas (GCA): 3,458 ALS acquisitions transformed into standardized and analysis-ready maps of canopy height and elevation at 1 m ² resolution. The GCA covers 56,554 km ² across all major biomes. 19% of this area has been scanned multiple times, and 87% of all GCA products are openly available, covering 95% of the total area. To showcase its wide range of applications, we applied the GCA in three case studies. First, we validated three global satellite-derived canopy height maps, finding poor performance at native resolution (1-30 m, R ² < 0.38) and moderate performance at 250 m resolution (R ² < 0.65). Second, analyzing global patterns in canopy gap size frequency we discovered an unexpectedly large variation of power law exponents from branch to stand level (α = 1.52 to 2.38), pointing to a fundamental scale-dependence of forest structure. Third, we developed a framework to standardize forest turnover quantification from multi-source, multi-temporal ALS. In a temperate forest in North America it revealed that 21% of canopy gaps closed within 12 years of opening and would thus be missed by infrequent monitoring. As demonstrated by these case studies, the GCA provides a novel data source for ecologists, foresters, remote sensing scientists and the ecosystem modelling community that substantially advances our ability to understand the structure and dynamics of woody ecosystems at global scales.

Recent developments in Unmanned Aerial Vehicles (UAVs) and Light Detection and Ranging (LiDAR) technology have enhanced the ability to capture precise three‐dimensional forest information at a lower cost. This article reports four types of high‐resolution digital datasets derived from UAV‐LiDAR measurements and associated fieldwork. The dataset includes 5 cm resolution Digital Terrain Models (DTMs) and Canopy Height Models (CHMs), ortho‐mosaic photos with sub‐2.7 cm resolution, and 4328 tree crown polygons with information on 151 species, stem girth at breast height, and canopy height. Data were collected from 22 approximately 1‐ha long‐term monitoring plots across Japan, spanning major climate zones from subtropical to subarctic. These plots encompass evergreen conifer, broadleaf and conifer mixed, deciduous broadleaf, and evergreen broadleaf forests. UAV surveys were conducted from May 2022 to October 2023. Based on these data, we also report basic profiles of these natural forests: a median slope of 22.2° (ranging from 2.5° to 35.0° across sites), median canopy height of 19.2 m (10.5–25.2 m), median maximum canopy height of 29.4 m (17.6–39.8 m), median gap ratios of 1.7% (0–10.7%) and 7.7% (0–28.9%) depending on gap definitions, and median crown area of 42.9 m ² (10.7–80.2 m ² ) for canopy trees. This dataset is the first publicly available collection of forest structure and individual tree crown information for Japanese natural forests. We hope this dataset will be useful for a wide range of studies and analyses, from site‐specific case studies to global‐scale meta‐analyses. The metadata is available in MetaCat in JaLTER at https://jalter.diasjp.net/data/ERDP-2025-04 .