Search Results (845)

Accurate estimation of aboveground biomass (AGB) in dryland savanna woodlands is constrained by sparse field data, which has motivated widespread fusion of field plots with spaceborne LiDAR reference data from the Global Ecosystem Dynamics Investigation (GEDI). Here, we show that such fusion can substantially inflate apparent model accuracy when the two reference sources sample different spatial domains. Using 44 field plots from the Abu-Gadaf Natural Reserved Forest (AGNRF), Sudan, and 56 GEDI L4A footprints drawn from a 50 km buffer surrounding the reserve, we trained Random Forest (RF), Gradient Boosting (GB) and Classification and Regression Tree (CART) models on Sentinel-1, Sentinel-2, SRTM and Dynamic World predictors and evaluated them under 10-fold, 2 km block spatial cross-validation. The merged dataset yielded apparently moderate performance (RF: RMSE = 9.40 Mg ha⁻¹, R² = 0.33). However, GEDI-derived AGB was 2.1 times higher than field-measured AGB (18.71 vs. 8.89 Mg ha⁻¹; Kolmogorov–Smirnov D = 0.53, p < 0.001), and decomposing performance by source revealed that predictive skill within the field plot population was effectively absent (R² = 0.001–0.023). A classifier trained to discriminate data source from the predictor stack alone achieved 85% accuracy against a 56% baseline, quantile calibration removing the inter-source level difference reduced pooled R² from 0.33 to 0.13, and restricting GEDI footprints to within 20 km of the reserve reduced R² to 0.008. Apparent accuracy therefore derived largely from between-source separation rather than from structural prediction of AGB. We conclude that spatial cross-validation does not detect population heterogeneity arising from multi-source reference fusion, and that source-stratified validation is necessary. The AGB maps presented are interpreted as relative spatial patterns rather than validated absolute estimates. Full article

Effective control and management of the encroaching and invasive Vachellia karroo (V. karroo) in grassland and savanna biomes depends on accurate information about its spatial distribution, making remote sensing approaches essential for mapping the extent of affected areas. Although Sentinel-2 satellite data are widely used for land use and land cover applications, they often lack the spatial details required to distinguish woody species like V. karroo. The fusion of Sentinel-2 data with high-resolution UAV imagery offers a promising approach to enhance spectral information for species-level discrimination. This study evaluated UAV, Sentinel-2, and fused UAV–Sentinel-2 imagery for discrimination of V. karroo in grassland and savanna biomes of the Eastern Cape, South Africa. Field data and imagery were collected in October 2022 and classified using Random Forest (RF) and Support Vector Machine (SVM) algorithms to distinguish V. karroo. The findings showed that V. karroo was more prevalent in the savanna biome. SVM marginally outperformed RF in classifying V. karroo in the grassland biome, achieving overall accuracies ranging from 68.9% to 97.4%, compared to 57.8% to 97.4% for RF. Among the datasets, the fused UAV–Sentinel-2 images yielded the highest classification accuracy, with an overall accuracy of 97.4% and a kappa coefficient of 0.96. The UAV images also demonstrated high classification accuracy, with an overall accuracy of 91.67% and a kappa coefficient of 0.77, confirming its value for fine-scale mapping and reference data support. In contrast, the Sentinel-2 images produced lower classification accuracy, with an overall accuracy of 84.6% and a kappa coefficient of 0.75, mainly due to their coarser spatial resolution. Classification was more challenging in the savanna site, where mixed vegetation structure increased confusion between V. karroo and grass. These findings show that fused UAV–Sentinel-2 images can improve species-level discrimination, while UAV and Sentinel-2 data remain complementary for fine-scale mapping and broader monitoring of bush encroachment in grassland and savanna ecosystems. Full article

Understanding how land-use change affects habitat fragmentation and connectivity is essential for assessing landscape degradation and conservation effectiveness in protected areas globally. It is particularly acute in tropical protected areas where anthropogenic pressures are intensifying. This study investigated long-term landscape dynamics, forest fragmentation, and structural connectivity in the Bena Mulumbu Hunting Domain, a Category VI protected area located in the Miombo woodland region of southeastern Democratic Republic of the Congo. Landsat imagery acquired in 1995, 2005, 2015, and 2025 was classified using the Random Forest algorithm into six land-cover classes (Miombo woodland, savanna, agricultural land, mining areas, built-up/bare land, and water bodies) to quantify land-cover changes over 30 years. Landscape composition was assessed using the percentage of landscape (PLAND), Shannon diversity metrics, and transition analyses. At the same time, fragmentation and structural connectivity of Miombo woodland were evaluated along an edge-to-core gradient (0–2 km, 2–4 km, 4–6 km, and >6 km) using landscape metrics. Results showed that savanna remained the dominant land-cover type throughout the study period. However, the landscape underwent progressive reorganization characterized by recurrent transitions among Miombo woodland, savanna, and agricultural land, leading to increased spatial heterogeneity. Fragmentation analyses revealed significant spatial differences in total core area among zones (Kruskal–Wallis: H = 8.12, p = 0.044); however, after normalization by zone area, no consistent edge-to-core gradient was observed for core habitat proportion, indicating that raw differences primarily reflect zone size rather than a systematic ecological gradient. Despite increasing fragmentation, structural connectivity remained high across the hunting domain. The CONNECT index increased significantly from the edge toward the core zone (p = 0.003), highlighting better-connected forest networks in interior sectors. These findings suggest that the Bena Mulumbu Hunting Domain is experiencing an intermediate stage of landscape transformation, where forest fragmentation is evident but has not yet resulted in widespread connectivity loss. Maintaining existing forest cores and connectivity corridors should therefore be prioritized to prevent further degradation of ecological integrity. These findings challenge the assumption that landscape degradation in protected tropical Miombo woodlands necessarily follows a simple edge-to-core gradient. Full article

Cathetus yuanjiangensis Huan C. Wang, Feng Yang et Chao Chen (Phyllantheae, Phyllanthaceae), a new species from the dry-hot valleys of Southwest China, is described and illustrated herein. Morphologically, the new species can be easily distinguished from its congeners by the following combination of characters: leaves 1.5–4.5 cm long, 1–2 cm wide; staminate flowers sepals spreading 1.5–2.0 mm long; filaments connate into a 2–3 mm long column; styles 3, ca. 1.6 mm long, connate into a ca. 0.5 mm long column at base; stigmas spreading, slender, oblong, each bifid to 1/2, the lobes linear. Phylogenetic analyses based on two nuclear (ITS and PHYC) and three chloroplast (matK, accD-psaI and trnS-trnG) DNA makers recover C. yuanjiangensis as the sister clade to C. fasciculate among the sampled taxa. Detailed morphological descriptions, photographs, pollen morphology, and complete plastid genome characteristics of the new species are provided, along with a distribution map and a preliminary conservation assessment. Additionally, we propose a new combination, Cathetus beillei (Hutch.) Huan C. Wang et Feng Yang, based on the basionym Phyllanthus beillei Hutch., which was originally published in Flora of Tropical Africa. An updated identification key for all recognized species of C. subgen. Cathetus is provided. Full article

In the Neotropical Savanna domain, few equations are available for estimating biomass stocks in Cerrado sensu stricto (CSS), despite the importance of such tools for estimating carbon stocks, understanding ecosystem functioning, and supporting conservation actions. We conducted forest inventories in 40 temporary 1000 m² plots in southeastern Brazil to estimate total and compartmental biomass stocks and to model biomass using structural and edaphic predictors. Total biomass (TB) included aboveground woody biomass (AGWB), necromass, litter, and belowground biomass (BGB). AGWB was estimated for trees with basal diameter ≥ 5 cm using a previously fitted regional equation. Root biomass was sampled using a 1 m³ trench excavated at a single point adjacent to each plot. Necromass was quantified using the line-intersect method along a 50 m transect, considering debris with diameter ≥ 3 cm. Litter was sampled using a 0.25 m² frame placed at the center of each plot. Biomass was modeled on an area basis using a hierarchical approach for TB, total tree biomass (TTB = AGWB + BGB), and AGWB. Mean stocks (Mg ha⁻¹ ± s.d.) were 45.24 ± 17.32 (TB), 20.47 ± 11.26 (AGWB), 18.47 ± 10.88 (BGB), 5.49 ± 4.18 (litter), and 0.81 ± 1.62 (necromass). Models selected using the Akaike Information Criterion (AIC) and validated by repeated k-fold cross-validation achieved rŷy = 0.76, 0.72, and 0.94 and RMSE = 24.40%, 27.06%, and 18.10% for TB, TTB, and AGWB. As the equations were calibrated for CSS under the environmental conditions of the Brazilian semiarid region, their transferability to other Cerrado regions should be considered with caution. The inclusion of soil variables (e.g., Al, Mg, and sand) improved predictions, reducing relative costs and taxonomic dependence, while incorporating nutritional adaptations to the acidic soils characteristic of the biome. Full article

Burned-area totals are useful for national monitoring, but they do not reveal how, when or under what social and ecological conditions a landscape burns. We developed an event-based fire-regime and stewardship framework for Eswatini, a topographically compressed southern African country where protected areas, communal rangelands, cropland margins, plantation landscapes and peri-urban interfaces occur in close proximity. Global Fire Atlas event histories for 2001–2025 were organised by fire year and intersected with approximately 10 km² hexagonal units. The burned-area rate, event frequency, recurrence, seasonality, large-fire dominance, pyrodiversity and trend were used to classify fire-regime types independently of socio-ecological predictors. An XGBoost regression model, evaluated on a 20% held-out test set, was interpreted using exact TreeSHAP diagnostics. Fire activity was strongly seasonal: July–September accounted for 78.2% of the burned area, with August alone accounting for 34.2%. Eight fire-regime types were identified, ranging from low-information and episodic units to frequent small-fire mosaics, large-fire-dominated areas and emerging burned-area intensification regimes. The burned-area-rate model performed well on held-out data (R2 = 0.71; Spearman rho = 0.75). Human modification, goat density, elevation, forest probability, fuelwood dependence and precipitation seasonality ranked among the most influential predictors, but their fitted effects were non-linear and often bidirectional. The combined diagnostics supported six adaptive management zones covering protected-area stewardship, conservation-sensitive management, settlement–livelihood interfaces, late-season risk reduction, monitoring and integrated landscape management. Although the Eswatini results are context-specific, the workflow offers a transferable way to connect fire histories, socio-ecological contexts and place-based stewardship in African mosaic landscapes. Full article

The countermovement jump (CMJ) is a common measure of lower-body power and neuromuscular performance. Incorporating an arm swing (AS) enhances CMJ outcomes, yet the contributions of anthropometrics, body composition (BC), and muscular power to performance are not fully understood in recreationally active adults. This study examined these factors in CMJ trials with and without AS. Thirty adults (15 males, 15 females; 18–25 years) performed CMJs on a force plate. Upper-body power was assessed via an 8 kg medicine ball throw, and lower-body power using a maximal cycling-based power test. Three-dimensional body scanning captured anthropometric data. Descriptive statistics, Pearson correlations, and hierarchical multiple regressions were conducted for the total sample and by sex. Jump height and peak power were significantly greater with AS than without (p < 0.001). Strong positive correlations were observed among jump- and power-related variables (r = 0.73–0.92). Body fat percentage showed moderate-to-strong negative associations with relative peak power, particularly in females. Regression analyses revealed that BC accounted for the largest proportion of variance in AS Peak Power, whereas upper-body power did not provide additional predictive value beyond anthropometrics and BC. These findings indicate that CMJ performance may be influenced primarily by lower-body mechanical capacity and BC, with adiposity consistently reducing relative power output. The results underscore the importance of BC in explosive performance training and support the use of AS-restricted jumps to isolate lower-body power during performance assessments. Full article

Field-based phenotyping of water-related traits for precision irrigation in tropical agroecosystems poses a persistent methodological challenge, driven by high climatic variability and the complex water-use physiology of Crassulacean Acid Metabolism (CAM) crops such as pineapple (Ananas comosus var. MD2). We developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status. A six-month field campaign (March–August 2022) across 25 georeferenced commercial pineapple plots in the Colombian Orinoquia piedmont yielded a spatiotemporally balanced dataset of $N=150$ observations. Soil-adjusted vegetation indices (OSAVI, MSAVI) outperformed standard NDVI for capturing water-related canopy traits, effectively decoupling spectral responses from substrate noise. A Gradient Boosting regressor achieved $R^2=0.842$ and $RMSE=0.0705$ on a normalized target scale, corresponding to a $7.05\%$ error over the prediction range, while the traffic-light Decision Support System (DSS) for irrigation scheduling reached $91.1\%$ accuracy (Cohen’s Kappa $=0.91$). Incorporating daily soil moisture depletion as a mechanistic feature improved predictive accuracy over a spectral-only baseline ($\mathrm{\Delta }{R}^2=+0.052$) and anchored predictions within a physically consistent framework based on the FAO-56 water balance, with no false negatives observed for water deficit detection in the hold-out validation set. This framework advances high-throughput, population-scale phenotyping of water-related traits in open-canopy CAM crops, establishing a transferable methodology for operational precision irrigation under tropical savanna conditions. Full article

Grasslands represent one of the most extensive terrestrial biomes globally, covering approximately one-third of the Earth’s land surface, yet they are increasingly threatened by land-use change and overgrazing, underscoring the need for reliable monitoring approaches. This study compares the performance of machine learning and deep learning algorithms for grassland mapping using multi-source remote sensing data derived from Sentinel-1, Sentinel-2, and terrain variables. The research was conducted in Mpumalanga Province, South Africa, a heterogeneous landscape comprising lowland savannas, high-altitude grasslands, escarpments, and riverine wetlands. Random Forest (RF) and Support Vector Machine (SVM) classifiers were implemented in Google Earth Engine using fused satellite and terrain datasets with field-collected samples for training and validation, while a One-Dimensional Convolutional Neural Network (1D-CNN) was developed in Python 3.13.5 using the same inputs. Results demonstrate that integrating multi-source data improves classification accuracy, with radar-based features contributing the most. RF achieved the highest performance, with an overall accuracy of 97.7% and grass-class precision, recall, and F1-score exceeding 0.97, closely followed by the 1D-CNN with 91% overall accuracy and complete grass detection. In contrast, SVM performed notably lower with an overall accuracy of 80,8%. These findings highlight the effectiveness of advanced learning approaches for grassland mapping and support their application in ecological restoration and environmental management. Full article

Although the green belt around Greater Lomé (GBGL) is a vital ecological buffer, it is currently facing significant degradation. This decline appears to be associated with a combination of various socioeconomic uses by the local community and formal operations of established businesses. Grounded in the cultural materialism framework, this study aims to contribute to a better understanding of the dynamics of the socioeconomic uses of the green belt around Greater Lomé in a context of degradation and investigates the dynamics of these socioeconomic uses and their environmental impacts through a multidisciplinary methodology. This approach combines anthropological analysis based on field observation, 53 semi-structured interviews and 5 focus groups, a quantitative questionnaire survey (n = 384) and an analysis of land use and land cover (LULC) dynamics derived from Landsat imagery (2003–2023). The results reveal six main types of socioeconomic uses of the GBGL (notably land transactions, agriculture, breeding and grazing, exploitation of wood energy, timber and utility wood, sand mining, and waste disposal), which lead to complex social dynamics ranging from conflicts to alliances among stakeholders. The LULC dynamics analysis indicates a staggering 468.26% expansion in built-up areas over the last 20 years, at the expense of swamp vegetation/gallery forest (−76.79%), tree-and-shrub savanna (−53.47%) and plantations (−49.43). This study provides a scientific basis supporting the urgent necessity to establish the GBGL as a legally protected entity and argues in favour of an inclusive management model that is designed to reconcile the socioeconomic survival needs of local populations with sustainable preservation of essential ecosystem services. Full article

This study quantified total biomass stocks in Carrasco (CAR, n = 12), a dense tropical deciduous vegetation type from the Brazilian semi-arid region for which biomass information remains scarce. We also evaluated differences in floristic composition, diversity, structure, and biomass allocation patterns relative to Cerrado sensu stricto (CSS, n = 40). Forest inventories were conducted in southeastern Brazil. Woody biomass was estimated using a regional allometric equation. Roots were sampled in a position adjacent to the plots, and litter was collected at the center of each plot using a frame. Necromass was assessed along a linear transect corresponding to the length of each plot using the line-intersect method. Biomass differences between vegetation types were assessed using generalized linear and mixed-effects models (GLMs and GLMMs). Total biomass reached 45.24 Mg ha⁻¹ in CSS and 59.01 Mg ha⁻¹ in CAR. In CSS, woody biomass predominated (20.47 Mg ha⁻¹; 45%), followed by roots (18.47 Mg ha⁻¹; 41%), litter (5.49 Mg ha⁻¹; 12%), and necromass (0.81 Mg ha⁻¹; 2%). In CAR, roots were the dominant component (32.37 Mg ha⁻¹; 55%), followed by woody biomass (16.57 Mg ha⁻¹; 28%), litter (8.39 Mg ha⁻¹; 14%), and necromass (1.68 Mg ha⁻¹; 3%). CSS and CAR shared only 10% of their species and showed significant differences in total biomass (TB) and belowground biomass (BGB), while aboveground biomass (AGB), aboveground woody biomass (AGWB), litter, and necromass did not differ significantly (α = 0.05). The BGB/AGWB ratio was <1 in CSS and >1 in CAR, resembling global patterns of savanna/shrubland and grassland formations, respectively. Considering the sampling design adopted, despite the higher stem density in CAR, larger individuals in CSS compensated for structural differences, resulting in similar aboveground biomass stocks. Our findings reinforce the floristic and structural distinctiveness of Carrasco and reveal contrasting biomass allocation strategies, with a strong dominance of belowground biomass in CAR. These results demonstrate that aboveground-based assessments can substantially underestimate total biomass in semi-arid transitional vegetation and highlight the need to incorporate non-forest ecosystems into biomass inventories, conservation planning, and climate change mitigation strategies. Full article

Fire is an essential component of species, ecosystems, and atmospheric dynamics. However, human activity has caused changes in fire regimes over the past two decades. In many cases, the spatial patterns of vegetation change after fire at the landscape scale remain unknown. The aim of this study was to evaluate spatial vegetation trends in burned areas across the Americas (2001–2024), using non-parametric tests and analyzing Normalized Difference Vegetation Index (NDVI) remote sensing products. Over a period of 24 years, fire activity burned a total area of 429.7 million hectares in 44 countries or territories and 269 ecoregions in the Americas. Regarding fire recurrence, the data indicates that 244.7 Mha (56.9%) burned only once (≤1), while 185.0 Mha (43.1%) burned multiple times (≥2), with certain regions experiencing up to 39 fires. The NDVI trend analysis showed that burned areas with increasing trends (p < 0.05) represented a total of 149.6 Mha (34.8%), primarily in Brazil (54.6 Mha, 12.7%), Argentina (17.8 Mha, 4.2%), the United States (14.4 Mha, 3.4%). In terms of decreasing NDVI trends (p < 0.05), these represented a total of 91.8 Mha (21.37%), primarily in Brazil (29.1 Mha, 6.8%), Canada (23.4 Mha, 5.4%), and the United States (14.2 Mha, 3.3%). The ecoregions with the largest areas showing increasing NDVI trends (p < 0.05) were the Cerrado (33.8 Mha, 7.8%), the Llanos (13.3 Mha, 3.1%) and the Humid Chaco (7 Mha, 1.6%). In contrast, the ecoregions with the largest areas showing decreasing NDVI trends (p < 0.05) were the Dry Chaco (9.2 Mha, 2.1%), the Cerrado (8.6 Mha, 2.0%), and the Boreal Shield (8.3 Mha, 1.9%). In terms of land cover types, savannas (37.2%) exhibited the highest proportions of increasing NDVI trends (p < 0.05), while decreasing trends were also present in savannas (28.0%) and grasslands (22.1%). Identifying spatiotemporal trends in vegetation change after fires is a fundamental step in implementing strategies and public policies to ensure ecological restoration. Moreover, given the high costs of restoration efforts, governments must work together to prevent these ecosystems from burning repeatedly. Full article

Buildings in hot–humid climates experience increasing thermal stress due to urban heat islands and climate change, leading to greater reliance on air conditioning. Passive cooling is therefore a crucial low-carbon strategy for maintaining thermal comfort. This paper reviews thermal comfort ranges and passive-cooling techniques across Köppen–Geiger hot–humid climate classes. A two-stage approach was adopted: thermal comfort data from 35 field studies were analyzed by climate class and ventilation mode, while more than 70 application studies were qualitatively reviewed to assess mechanisms, performance, and climate suitability. The results indicate that occupants in hot–humid areas exhibit broad thermal tolerance, particularly in naturally ventilated buildings, with neutral temperatures ranging from 19.5 °C in humid subtropical climates to 36.3 °C in tropical savanna climates. Natural ventilation is the most widely applicable passive-cooling strategy, but its effectiveness depends on integration with climate-responsive measures. Ventilation, combined with solar protection and courtyards, is most effective in Af and Am climates, whereas shading, solar chimneys, evaporative cooling, night ventilation, thermal mass, and phase-change materials provide greater benefits in Aw, Cfa, and Cwa climates. However, no single strategy is sufficient across all climates. The review provides climate-specific guidance for designing low-carbon, thermally resilient buildings in hot–humid regions. Full article

This study quantified the extent of cropland abandonment in relation to bush/shrub encroachment and natural rangeland in Sencherere village, Limpopo Province, from 1994 to 2024. Landsat 5, 7, 8, and 9 images were used to classify three land-cover categories using a Random Forest algorithm, with overall accuracies ranging from 80% to 85% and Kappa coefficients between 0.73 and 0.80. Results show that cropland abandonment followed a non-linear trend, decreasing from 498 ha (37.7%) in 1994 to 200 ha (15.14%) in 2014, suggesting a period of recovery or re-cultivation during this interval. However, this trend reversed thereafter, with abandonment increasing again to 473 ha (35.81%) in 2024, indicating renewed abandonment of cultivated areas. This pattern suggests that cropland use in the study area is not a progressive one-directional abandonment process, but rather a cyclical interaction between abandonment and reclamation influenced by changing environmental and socio-economic conditions over time. Bush or shrub cover expanded substantially over the 30 years, increasing from 51 ha (3.86%) in 1994 to 354 ha (26.8%) in 2024, indicating a strong shift toward woody vegetation dominance. Natural rangeland cover fluctuated considerably from 195 ha in 1994 to 385 ha in 2004, declining to 65 ha in 2014 before partially recovering to 115 ha in 2024. Rainfall variability showed no clear long-term trend, suggesting that climatic patterns alone do not explain the observed land-cover changes; therefore, other drivers may have influenced this. The study highlights dynamic local trends of cropland abandonment and woody vegetation expansion, underscoring the need for continued monitoring and targeted investigation into the socio-economic and ecological drivers shaping these changes to support effective land-use planning and rangeland management in semi-arid communal systems. Full article

In transitional tropical ecosystems such as the Amazonia–Cerrado ecotone, dominant tree species experience strong environmental heterogeneity, requiring coordinated functional strategies to cope with drought, nutrient limitation, and disturbance. However, how these species integrate leaf morphoanatomical traits and wood density to persist in such environments remains poorly understood. We assessed the coordination among leaf anatomical and morphological traits and their relationship with wood density in five dominant tree species across three savanna park sites in the Amazonia–Cerrado transition. Morphological traits included leaf thickness, specific leaf area, leaf dry matter content, and wood density, alongside 17 anatomical leaf traits. We analyzed inter- and intraspecific variation and covariation patterns to identify trait-based ecological strategies along the acquisitive–conservative spectrum. We found strong coordination among traits related to protection (e.g., cuticle thickness and trichomes) and resource use, as well as clear alignment between leaf and wood traits. Species identity explained most trait variation, although leaf thickness showed notable intraspecific plasticity. Species with conservative traits exhibited thicker leaves and higher wood density, whereas species with acquisitive strategy showed higher specific leaf area and lower leaf dry matter content. Overall, trait coordination reflects integrated ecological strategies shaped by environmental heterogeneity, highlighting the role of multi-trait syndromes in driving functional adaptation in ecotonal systems. Full article