Search Results (54)

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

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

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

High-resolution imagery is essential for monitoring heterogeneous grassland ecosystems, yet the performance of generative adversarial network (GAN) super-resolution under varying landscape heterogeneity and operational application scenarios remains unclear. This study presents a landscape-aware evaluation of super-resolution methods in semi-arid savanna grasslands of the Edwards Plateau (Texas, USA) using paired multispectral imagery from PlanetScope (3 m) and unmanned aerial vehicle (UAV) platforms (0.03 m). Two GAN models, SRGAN and ESRGAN, were compared with a bicubic interpolation baseline. Image tiles were systematically stratified along ecologically relevant gradients of vegetation condition (NDVI quartiles), spatial structure (woody patch-based clusters), and textural complexity (GLCM entropy quartiles). Model performance was evaluated across three operational frameworks: intra-sensor downscaling, cross-sensor downscaling, and intra-to-cross generalization. Reconstruction fidelity was quantified using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), complemented by variability analysis to assess performance stability. Landscape heterogeneity strongly influenced downscaling outcomes. SRGAN performance declined in areas with dense vegetation, aggregated woody structure, and high-entropy textures, with large variability under cross-sensor and generalization scenarios. In contrast, ESRGAN demonstrated consistently robust performance across landscape gradients, whereas bicubic interpolation performed well only under intra-sensor conditions and drastically degraded under sensor transfer. These results demonstrate that vegetation condition, structural heterogeneity, and sensor-transfer scenarios jointly constrain super-resolution performance. Rather than serving as a model comparison exercise, this study emphasizes a landscape-aware framework for understanding how ecological heterogeneity and operational domain shifts jointly shape super-resolution behavior in grassland ecosystems, providing guidance for more reliable applications of deep learning-based remote sensing methods. Full article

Atmospheric aerosols influence the terrestrial carbon cycle through diverse radiative and biogeochemical effects, yet their net impact on vegetation productivity remains contentious and region-specific. To address this, we analyzed the spatiotemporal coupling between aerosol optical depth (AOD) and net primary productivity (NPP) over three African biomes (2013–2023), using multi-source datasets (MODIS, CERES, ERA5, CRU TS). We explicitly distinguished statistically significant relationships (p < 0.05) from non-significant ones when interpreting correlation patterns. Because AOD is an optical measure and does not provide aerosol composition, interpretations involving dust versus smoke are treated as qualitative and indirect. Through structural equation modeling (SEM), we identified two contrasting mechanistic pathways: in the humid Congo Basin rainforest, aerosols were associated with lower NPP via a cooling-mediated pathway (increased cloud albedo leading to reduced temperature and light availability), whereas in the arid savanna, they were associated with more substantial limitations on NPP via a warming-aggravated pathway (increased temperature and potentially coupled water stress). SEM fit was poor for the semi-arid South African plateau, underscoring the dominant role of water availability in strongly water-limited systems. This framework reconciles the paradox of dual aerosol effects by demonstrating that the net impact is dictated by regional climate context. Overall, our conclusions emphasize context-dependent associations rather than direct causal attribution from correlations alone. Our findings provide a process-based understanding that is critical for improving carbon cycle models and for formulating targeted climate adaptation strategies in Africa. Full article

Savanna ecosystems of the Sahel are heavily affected by climate change, leading to drier subtropical regions. These ecosystems play a fundamental role in food security of the region, so that an improved understanding of how these ecosystems are affected by these weather events is thereby critical. Several studies have assessed the herbaceous production level, dynamics during the rainy season, and biotic and abiotic factors that could impact this production. Some authors argue that rain is the main factor positively influencing the biomass production in semi-arid areas where the rainfall ranges between 200 and 750 mm. This study aims to assess the contribution of some meteorological variables to biomass production in a Sahelian semiarid savannah. From 2008 to 2018, rainfall (mm), soil moisture (%), soil temperature (°C) at 5 cm depth, number of times there was no rain (NTNR), number of rainy days, and the herbaceous biomass were monitored. The contributions of meteorological variables to biomass production were calculated using the XGBoost regression model. The most influential meteorological parameters on herbaceous biomass production are soil temperature, amount, and distribution of rainfall. Full article

Water scarcity is a growing global issue, especially in arid and semi-arid rangelands, primarily due to climate change and population growth. Groundwater is a crucial resource for vegetation in these ecosystems, yet its role in supporting plant life is often not fully understood. This review explores the interactions between groundwater and vegetation dynamics in various rangeland types. Groundwater serves as a critical water source that helps sustain plants, but changes in its availability, depth, and quality can significantly impact plant health, biodiversity, and ecosystem stability. Research indicates that groundwater depth affects vegetation types and their distribution, with specific plants thriving at certain groundwater levels. For instance, in grasslands, shallow groundwater can support diverse herbaceous species, while deeper conditions may favor drought-tolerant shrubs and trees. Similarly, in forest ecosystems, extensive root systems access both groundwater and soil moisture, playing a vital role in water regulation. Savanna environments showcase complex interactions, where trees and grasses compete for water, with groundwater potentially benefiting trees during dry seasons. Climate change poses additional challenges by altering rainfall patterns and temperatures, affecting groundwater recharge and availability. As a result, it is crucial to develop effective management strategies that integrate groundwater conservation with vegetation health. Innovative monitoring techniques, including remote sensing, can provide valuable information about groundwater levels and their impact on vegetation, enhancing water resource management. This review emphasizes the importance of understanding groundwater–vegetation interactions to guide sustainable land and water management practices. By enhancing our knowledge of these connections and utilizing advanced technologies, we can promote ecosystem resilience, secure water resources, and support biodiversity in rangeland systems. Collaborative efforts among local communities, scientists, and policymakers are essential to address the pressing issues of water scarcity and to ensure the sustainability of vital ecosystems for future generations. Full article

Pre-rain green-up is a distinctive phenological phenomenon observed in arid and semi-arid regions, featuring the sprouting of plants before the onset of the rainy season. This phenomenon indicates the intricate controls of vegetation phenology other than precipitation, yet its global distribution patterns and underlying causes remain unclear. In this study, we used remotely sensed phenology and rainfall data to map the global distribution of pre-rain green-up vegetation for the first time in arid and semi-arid savanna areas. The results revealed that over one-third of pre-rain green-up vegetation is in mountainous regions. Furthermore, to explore the potential effect of groundwater accessibility on pre-rain green-up, we employed high-resolution imagery to quantify phenological parameters and analyzed the relationship between pre-rain green-up and elevation at the watershed scale in a typical mountainous pre-rain green-up region in Africa. We found that within the pre-rain green-up area, 60.64% of sub-watersheds show a significant negative correlation (p < 0.05) between the start of the season (SOS) and elevation, indicating that the SOS occurs earlier at higher elevations despite the complex spatial variability overall. Our study provides a global picture of the pre-rain green-up phenomenon in tropical drylands and suggests that tree internal water regulation mechanisms rather than groundwater accessibility control the pre-rain green-up. Full article

This study analyzed terrestrial water storage (TWS) changes across the Inner Niger Delta (IND) in Mali (West Africa) from April 2002 to September 2022 using Gravity Recovery and Climate Experiment (GRACE), GRACE-Follow-On (GRACE-FO), and Global Land Data Assimilation System (GLDAS) products. TWS changes exhibited strong seasonal patterns (−170 mm to 330 mm) with a high correlation between GRACE/GRACE-FO and GLDAS (r = 0.92, RMSE = 35 mm). The TWS trend was positive (7.3 to 9.5 mm/year). Maximum TWS changes occurred in September, while minimum values were observed between April and May. Wavelet analysis identified dominant seasonal cycles (8–16 months). Finally, we examined the climatic effects on TWS changes along the Niger River, from its source in the humid zones of Guinea to the semi-arid Sahelian zones of the IND in Mali. Precipitation (P) and potential evapotranspiration (PE) influence TWS changes only in the humid regions (r = 0.18–0.26, p-value < 10⁻²). Surface water bodies (SWB) exhibited a significant correlation with TWS in all regions, with r exceeding 0.50 in most cases. Groundwater changes, estimated from GRACE/GRACE-FO and GLDAS, showed strong agreement (r > 0.60, RMSE < 120 mm), with recharge rates increasing in semi-arid and Sahelian regions (r > 0.70, p-value < 10⁻³). This study highlights that precipitation, surface water bodies, and groundwater recharge appear as primary drivers of TWS in different regions: precipitation in the humid forest of Guinea, surface water bodies in the Southern and Northern Guinea Savanna along the Guinea–Mali border, and groundwater recharge in the semi-arid and IND Sahelian regions of central Mali. Full article

Regional mapping of Above Ground Biomass Density (AGBD) using Remote Sensing data has shown high accuracy but lacks replicability at a global scale. In contrast, global models capture AGBD variability across biomes but struggle with biome-specific accuracy. To address this gap, we develop and assess the performance of a Deep Learning model for mapping AGBD at 10-m resolution using multi-source satellite data (Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI) across four biomes: Mediterranean, taiga (boreal forests), tropical rainforests, and semi-arid savannas. The model is trained and validated separately for each biome, yielding four regional models with normalized RMSEs of 0.43–0.67 and correlation coefficients (r) of 0.61–0.77 against forest inventories. We compare predictions from these models to a benchmark dataset and to a model trained on all four biomes combined. The regional models consistently outperform both, achieving better metrics than the benchmark. Additionally, an analysis of prediction drivers reveals biome-specific differences, reinforcing the importance of per-biome mapping approaches. This study highlights the advantages and limitations of regional against global modeling, creating the basis for biome-specific, replicable, scalable and multi-temporal AGBD mapping. Full article

Wet rainfall pulses control vegetation growth through evapotranspiration in most dryland areas. This topic has not been extensively analyzed with respect to the vast semi-arid ecosystems of Central Australia. In this study, we investigated vegetation water responses to in situ root zone soil moisture (SM) variations in savanna woodlands (Mulga) in Central Australia using satellite-based optical and thermal data. Specifically, we used the Land Surface Water Index (LSWI) derived from the Advanced Himawari Imager on board the Himawari 8 (AHI) satellite, alongside Land Surface Temperature (LST) from MODIS Terra and Aqua (MOD/MYD11A1), as indicators of vegetation water status and surface energy balance, respectively. The analysis covered the period from 2016 to 2021. The LSWI increased with the magnitude of wet pulses and showed significant lags in the temporal response to SM, with behavior similar to that of the Enhanced Vegetation Index (EVI). By contrast, LST temporal responses were quicker and correlated with daily in situ SM at different depths. These results were consistent with in situ relationships between LST and SM, with the decreases in LST being coherent with wet pulse magnitude. Daily LSWI and EVI scores were best related to subsurface SM through quadratic relationships that accounted for the lag in vegetation response. Tower flux measures of gross primary production (GPP) were also related to the magnitude of wet pulses, being more correlated with the LSWI and EVI than LST. The results indicated that the vegetation response varied with SM depths. We propose a conceptual model for the relationship between LST and SM in the soil profile, which is useful for the monitoring/forecasting of wet pulse impacts on vegetation. Understanding the temporal changes in rainfall-driven vegetation in the thermal/optical spectra associated with increases in SM can allow us to predict the spatial impact of wet pulses on vegetation dynamics in extensive drylands. Full article

This study examines climate change impacts on evapotranspiration in Inner Mongolia, analyzing potential (PET) and actual (AET) evapotranspiration shifts across diverse land-use classes using the SEBAL model and SSP2-4.5 and SSP5-8.5 projections (2030–2050) relative to a 1985–2015 baseline. Our findings reveal substantial PET increases across all LULC types, with Non-Vegetated Lands consistently showing the highest absolute PET values across scenarios (931.19 mm under baseline, increasing to 975.65 mm under SSP5-8.5) due to limited vegetation cover and shading effects, while forests, croplands, and savannas exhibit the most pronounced relative increases under SSP5-8.5, driven by heightened atmospheric demand and vegetation-induced transpiration. Monthly analyses show pronounced PET increases, particularly in the warmer months (June–August), with projected SSP5-8.5 PET levels reaching peaks of over 500 mm, indicating significant future water demand. AET increases are largest in densely vegetated classes, such as forests (+242.41 mm for Evergreen Needleleaf Forests under SSP5-8.5), while croplands and grasslands exhibit more moderate gains (+249.59 mm and +167.75 mm, respectively). The widening PET-AET gap highlights a growing vulnerability to moisture deficits, particularly in croplands and grasslands. Forested areas, while resilient, face rising water demands, necessitating conservation measures, whereas croplands and grasslands in low-precipitation areas risk soil moisture deficits and productivity declines due to limited adaptive capacity. Non-Vegetated Lands and built-up areas exhibit minimal AET responses (+16.37 mm for Non-Vegetated Lands under SSP5-8.5), emphasizing their limited water cycling contributions despite high PET. This research enhances the understanding of climate-induced changes in water demands across semi-arid regions, providing critical insights into effective and region-specific water resource management strategies. Full article

The semi-arid Caatinga biome is particularly susceptible to fire dynamics. Periodic droughts amplify fire risks, while anthropogenic activities such as agriculture, pasture expansion, and land-clearing significantly contribute to the prevalence of fires. This research aims to evaluate the effectiveness of a fire detection model and analyze the spatial and temporal patterns of burned areas, providing essential insights for fire management and prevention strategies. Utilizing deep neural network (DNN) models, we mapped burned areas across the Caatinga biome from 1985 to 2023, based on Landsat-derived annual quality mosaics and minimum NBR values. Over the 38-year period, the model classified 10.9 Mha (12.7% of the Caatinga) as burned, with an average annual burned area of approximately 0.5 Mha (0.56%). The peak burned area reached 0.89 Mha in 2021. Fire scars varied significantly, ranging from 0.18 Mha in 1985 to substantial fluctuations in subsequent years. The most affected vegetation type was savanna, with 9.8 Mha burned, while forests experienced only 0.28 Mha of burning. October emerged as the month with the highest fire activity, accounting for 7266 hectares. These findings underscore the complex interplay of climatic and anthropogenic factors, highlighting the urgent need for effective fire management strategies. Full article

Climate change is expected to alter the environmental suitability of land use and land cover (LULC) classes globally. In this study, we investigated the potential impacts of climate change on the environmental suitability of the most representative LULC classes in the southern Brazilian semiarid region. We employed the Random Forest algorithm trained with climatic, soil, and topographic data to project future LULC suitability under the Representative Concentration Pathway RCP 2.6 (optimistic) and 8.5 (pessimistic) scenarios. The climate data included the mean annual air temperature and precipitation from the WorldClim2 platform for historical (1970–2000) and future (2061–2080) scenarios. Soil data were obtained from the SoilGrids 2.1 digital soil mapping platform, while topographic data were produced by NASA’s Shuttle Radar Topography Mission (SRTM). Our model achieved an overall accuracy of 60%. Under the worst-case scenario (RCP 8.5), croplands may lose approximately 8% of their suitable area, while pastures are expected to expand by up to 30%. Areas suitable for savannas are expected to increase under both RCP scenarios, potentially expanding into lands historically occupied by forests, grasslands, and eucalyptus plantations. These projected changes may lead to biodiversity loss and socioeconomic disruptions in the study area. Full article

The viewpoint and reaction of a country towards climate change are shaped by its political, cultural, and scientific backgrounds, in addition to the distinct characteristics of its evolving climate and the anticipated and actual consequences of the phenomenon in the times ahead. A region’s climate has a significant impact on how water is managed and used, mostly in the primary sector, and both the distribution of ecosystem types and the amount and spreading of species on Earth. As a result, the environment and agricultural practices are affected by climate, so evaluating both distribution and evolution is extremely pertinent. Towards this aim, the climate distribution and evolution in the São Francisco River basin (SFRB) is assessed in three periods (1970–2000, 1981–2022) in the past and 2041–2060 in the future from an ensemble of GCMs under two SSPs (Shared Socioeconomic Pathways), SSP2-4.5 and SSP5-8.5. The Köppen-Geiger (KG) climate classification system is analyzed, and climate change impacts are inferred for this watershed located in central-eastern Brazil, covering an area equivalent to 8% of the country. Results predict the disappearance of the hot summer (Csa) and warm summer (Csb) Mediterranean climates, and a reduction/increase in the tropical savanna with dry winter (Aw)/dry summer (As). A striking increase in the semi-arid hot (BSh-steppe) climate is predicted with a higher percentage (10%) under SSP5-8.5. The source and the mouth of SFRB are projected to endure the major impacts of climate change that are followed by a predicted increase/decrease in temperature/precipitation. Future freshwater resource availability and quality for human use will all be impacted. Consequences on ecosystems, agricultural, and socioeconomic sectors within the SFRB might deepen the current contrasts between regions, urban and rural areas, and even between population groups, thus translating, to a greater extent, the inequality that still characterizes Brazilian society. Maps depicting land use and cover changes in SFRB from 1985 to 2022 highlight tendencies such as urbanization, agricultural expansion, deforestation, and changes in shrubland and water bodies. Urban areas fluctuated slightly, while cropland significantly increased from 33.57% to 45.45% and forest areas decreased from 3.88% to 3.50%. Socioeconomic data reveals disparities among municipalities: 74.46% with medium Human Development Index (HDI), 0.59% with very high HDI, and 9.11% with low HDI. Most municipalities have a Gross Domestic Product (GDP) per capita below US$6000. Population distribution maps show a predominance of small to medium-sized urban and rural communities, reflecting the basin’s dispersed demographic and economic profile. To achieve sustainable adaptation and mitigation of climate change impacts in SFRB, it is imperative that integrated measures be conducted with the cooperation of stakeholders, the local population, and decision-makers. Full article