Farhadur Rahman

Summary: Elucidating interspecific variation in growth strategies across environments is critical for understanding tree diversity and distribution. However, a systematic analysis across diverse forests remains scarce due to limited large-tree crown measurements. We analysed 3,814 trees from 107 canopy species in 23 natural forests using forest census data and unmanned aerial vehicle. Relative growth rate (growth rate/biomass) was decomposed into growing space efficiency (growth rate/crown area) and crown area ratio (crown area/biomass). Relative growth rates were similar among functional groups (deciduous broadleaf, evergreen broadleaf, evergreen conifer), but their underlying components differed. Deciduous trees exhibited 34–43% higher crown area ratio but 24–27% lower growing space efficiency than evergreen broadleaf and conifer trees. Across environments, growing space efficiency correlated positively with mean annual temperature, likely from extended growing seasons while crown area ratio correlated negatively, particularly in deciduous species. Snow loading limited crown area ratio in evergreen species, while deciduous achieved higher values through avoiding snow loads. Correlation between crown area ratio and specific leaf area indicated a coordinated strategy between crowns and leaves. Different functional groups achieve comparable growth efficiency either through crown expansion or biomass production, explaining their distinct distribution as well as co-occurrence across broad environments.

Precise early-stage cotton yield prediction remains critical for optimizing crop management and improving productivity for smallholder farmers. UAV-based yield modelling approaches in smallholder cotton systems face dual hurdles: restricted ground truth data collection and inherent multicollinearity among common spectral vegetation indices. While machine learning models show promise, effective deployment of such methods requires innovative solutions within resource constraints. This study comparatively assesses agronomic data-only, remote sensing data-only, and hybrid data modeling approaches to define an optimized framework integrating remote sensing imagery and non-destructive field measurements. The approach compared Multiple linear regression, Stepwise selection, LASSO, Elastic Net, and Support Vector Machines to evaluate predictive ability and identify the earliest growth stage, permitting reliable yield prediction. Multispectral airborne imagery and extensive agronomic data were gathered systematically over two experimental plots with varied water and nitrogen fertilization treatments at six critical phenological stages. Hybrid models outperformed single data source models, with LASSO regression attaining the highest precision (R2 = 0.80, RMSE = 0.22) by synergizing multispectral data and plant height data at 90 DAS. Phases between 50% flowering and boll growth were determined as optimal windows for accurate yield estimation with remote sensing observations alone. The outcomes identify regularized regression strengths in limited data situations because of the capacity to address overfitting due to multicollinearity. Our hybrid approach develops yield prediction for small farms by combining UAV imagery and field measurements using regularized machine learning, avoiding multicollinearity and requiring little ground data. This study demonstrates the potential of this approach to provide actionable intelligence 75 days earlier than traditional methods, offering scalable precision agriculture technology for data-poor regions.

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.

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.

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 .

Tropical storms, such as typhoons in Asia, cause significant damage to forests. But the intensity of damage due to wind throw may vary with forest structure and forest types. Thus, efficient and cost-effective methods are needed to estimate the wind-induced damages and examine the influence forest structure and forest types on forest damage vulnerability. In this study, high-resolution satellite images were used to delineate damaged forest areas, and high-resolution airborne LiDAR (ALS) was used to estimate standing biomass and its loss due to a major typhoon. From analyses of ALS data in combination with the random forest algorithm, we found that 2.47 km2 was damaged by a single major typhoon in 2018 within 230 km2 (1.07%) of the forested area north of Kyoto City, Japan. Overall, the ALS-derived canopy height agreed well with actual canopy height (R2 = 0.87) and enabled prediction of aboveground biomass with a generalized linear mixed model (R2 = 0.62). The total estimated standing biomass of the study area was 6396 Gg (295 Mg ha-1), of which 1.44% were lost due to the typhoon although these were conservative estimates due to the inability of satellite images to detect numerous small-scale damages. Among the vegetation types, the majority of the damages occurred in cedar plantations which had greater canopy height than other vegetation types (biomass loss of 2.4% vs. 0.16 ~ 0.61% in plantations vs. other vegetation types, respectively). We suggest that a combination of satellite and ALS data can inform adaptive management toward more resilient forests in the face of increasing frequency and strength of storms.

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.