The Chambal region, straddling the tri-junction of Madhya Pradesh, Rajasthan, and Uttar Pradesh, is one of the world’s most geomorphologically complex landscapes. Its infamous badlands, known locally as beehads, were formed by intense gully erosion driven by the Chambal, Kunwari, and Asan rivers. These deeply dissected ravines, with their steep and unstable slopes, have long posed formidable challenges to agriculture and rural livelihoods. Because land degradation affects an estimated 20 percent of Earth’s vegetated surface and more than 1.3 billion people, with economic costs reaching up to US$10.6 trillion, understanding how such landscapes evolve is a matter of global urgency.

Traditional ground-based methods for assessing degradation are labor-intensive, spatially limited, and temporally inconsistent, making long-term monitoring over large areas impractical. The research team, led by Farid Ahmed of Jamia Millia Islamia in New Delhi, instead turned to the sky. They assembled multi-temporal Landsat imagery from sensors 5, 7, 8, and 9, resampled to a uniform 30-meter resolution, and computed eight spectral indices capturing different facets of surface condition: vegetation vigor, soil exposure, moisture, salinity, built-up surfaces, and soil texture.

Among these indicators, the Bare Soil Index measures exposed soil using red, near-infrared, blue, and shortwave-infrared bands, while the Topsoil Grain Size Index detects coarsening of surface particles, a hallmark of erosion. The Soil-Adjusted Vegetation Index corrects for the bright soil backgrounds that plague vegetation monitoring in semi-arid environments, and the Soil Salinity Index flags salt-affected surfaces. The Normalized Difference Moisture Index and Modified Normalized Difference Water Index track vegetation and surface water, respectively. Together, these indices form a multidimensional portrait of land health that no single measure could provide.

But combining eight indices naively would introduce redundancy and statistical distortion. The team therefore applied Pearson correlation analysis as a screening step, using a threshold of |r| greater than or equal to 0.95 to flag overlapping variables, then applied ecological reasoning to decide which indices to keep. NDVI and SAVI were correlated at essentially 1.000, but because SAVI better handles soil brightness in sparsely vegetated terrain, it was retained while NDVI was excluded. Similarly, BSI and the built-up index correlated at roughly 0.99, so NDBI was dropped. Six non-redundant indices survived the screening: BSI, SAVI, NDMI, MNDWI, SSI, and TGSI.

These six were then fused through Principal Component Analysis, a multivariate technique that transforms correlated variables into uncorrelated components ranked by the variance they explain. The outcome was striking. The first principal component accounted for 81 percent of total landscape variance in 1992 and 87 percent in 2024, revealing that soil exposure, moisture deficits, salinization, and textural coarsening do not operate as isolated phenomena but as a single, synchronized degradation gradient. This dominance provided a strong statistical rationale for using PC1 as an objective, data-driven Land Degradation Index, free of the subjective weighting schemes that plague conventional overlay methods.

When the index was classified into five risk categories and mapped, the trajectory became clear. High and Very High degradation zones shrank from 29.37 percent of the division’s area in 1992 to 21.75 percent in 2024, while Low and Very Low categories expanded from 41.66 to 52.65 percent. The most dramatic recovery occurred in the ravines themselves: high-risk ravine exposure fell by 25.85 percent, with nearly half the ravine landscape classified as high or very high risk in 1992 dropping to just over 22 percent by 2024. Barren land showed similar stabilization, and agricultural land remained the most secure category, with nearly 62 percent of its extent in the lowest risk classes by 2024.

The researchers attribute this stabilization largely to large-scale land reclamation policies, including land levelling, gully plugging, terracing, and check-dam construction that have converted badlands into productive agricultural units. Yet the story is not uniformly positive. Forest areas moved in the opposite direction, with high and very high degradation rising from 24.13 percent of forest cover in 1992 to 35.40 percent in 2024, an 11.27 percent increase in vulnerability. The very high risk class within forests nearly doubled. High-resolution imagery confirms these are sparse, scrub-dominated systems with discontinuous canopies and extensive exposed soil, where grazing pressure, fuelwood extraction, and continued erosion are driving surface degradation even as greenness indices show modest gains, a divergence the authors caution may partly reflect soil background effects rather than genuine recovery.

Moisture and salinity dynamics add further nuance. Peak vegetation moisture improved over the study period, but minimum surface water index values declined, indicating intensified hydrological contrast and localized dryness, particularly in the southwestern forest sector. Salinity stress eased in irrigated agricultural zones of the west while increasing in certain forest patches, where sparse, unmanaged vegetation allows capillary action to draw salts to exposed soil surfaces. The findings resonate with national evidence that India contains roughly 6.74 million hectares of salt-affected soils, an area that continues to expand and threatens long-term food security.

The study’s implications extend well beyond the Chambal. By demonstrating that engineered stabilization can succeed while ecological safeguards lag behind, it offers both a model and a warning for semi-arid regions worldwide pursuing Land Degradation Neutrality under Sustainable Development Goal 15.3. The authors argue that policy must adopt a landscape-level approach balancing agricultural expansion, forest conservation, and hydrological regulation, supported by community participation. Future work, they note, should integrate higher-resolution data, ground validation, and socio-ecological modeling to track how ravine-to-agriculture conversions affect biodiversity, groundwater recharge, and forest fragmentation. For now, the Chambal stands as proof that degraded landscapes can heal, provided the healing is measured honestly and extended to every corner of the ecosystem.

Subject of Research: Land degradation assessment in the semi-arid Chambal region using remote sensing indices and multivariate statistics

Article Title: Integrating remote sensing indices and multivariate statistics for land degradation assessment in the semi-arid Chambal region

Article References: Integrating remote sensing indices and multivariate statistics for land degradation assessment in the semi-arid Chambal region. (n.d.). https://doi.org/10.1007/s44288-026-00743-8

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00743-8

Keywords: land degradation, remote sensing, principal component analysis, Chambal ravines, soil erosion, satellite imagery, Landsat, forest degradation, soil salinity, semi-arid, SDG 15, land degradation neutrality

The research, led by Taufique Warsi of the Raintree Foundation and the WOTR Centre for Resilience Studies, together with Siddhant Sanjay Sonde and George Biswas, focuses on roughly 32 square kilometres of the Deccan Traps, the vast stack of Late Cretaceous to Palaeocene basalt flows that underlies much of western and central India. The region endures hot, dry pre-monsoon months with summer temperatures approaching 42 degrees Celsius, and receives most of its roughly 737 millimetres of annual rainfall during the brief June-to-September monsoon. Water security here depends on percolation tanks and other recharge interventions, including one tank that was desilted during 2016 and 2017, making the careful placement of such structures a matter of practical urgency.

The core insight of the study is deceptively simple: a location can look perfect on a surface map and still be a poor place to sink water into the ground. In basaltic terrain, groundwater is stored and transmitted not through the rock matrix itself but through secondary porosity — weathered mantles, joints, fractures, vesicles and interflow zones. These productive zones are spatially discontinuous, and a successful borewell depends on the coincidence of recharge, storage and transmissive pathways rather than on rock type alone. A spot that scores highly on a surface-derived potential map may sit above compact, unfractured basalt that blocks downward percolation entirely.

To capture where groundwater is likely to occur, the team built a Groundwater Potential Zone map using seven thematic layers prepared in a geographic information system: drainage density, lineament density, slope, land use and land cover, lithology, soil and geomorphology. Each layer represents a first-order control on groundwater occurrence. Drainage density acts as an inverse indicator of infiltration opportunity, since dense stream networks shed water quickly. Lineaments — the surface traces of fractures and joints — signal potential permeability pathways. Slope controls the split between runoff and infiltration, while soil, geomorphology and lithology frame the storage and weathering conditions beneath. The layers were weighted using the Analytical Hierarchy Process, a structured decision method in which expert judgements are encoded in pairwise comparisons on a one-to-nine importance scale, and the resulting weights are checked for consistency. Only comparison matrices with a consistency ratio of 0.10 or below were accepted for the weighted overlay, a discipline the authors argue is often missing from local-scale mapping studies.

The results show just how restricted productive groundwater really is in this landscape. The very high potential class covers only about 2.07 percent of the mapped area, with the high class adding another 8.28 percent. These favourable pockets cluster where low-lying geomorphic positions, low drainage density, higher lineament density and suitable lithology coincide — typically pediment-pediplain surfaces and areas near ponds and water bodies, rather than the dissected structural plateaus that dominate about 2,530 hectares of the terrain. For a dissected basaltic landscape, the authors note, this limited extent is hydrogeologically reasonable: productivity is controlled by localised weathering and fracture networks, not by broad regional patterns.

For the second map, the Groundwater Recharge Zone, the team added something most mapping studies leave out: actual measurements of how fast water soaks into the soil. Using a double-ring infiltrometer, with inner and outer rings held at an equal head of roughly 10 to 15 centimetres to suppress lateral leakage, the researchers ran tests lasting two to four hours at sites within the study area and recorded the near-steady infiltration rate, defined as the mean of the final three valid readings. The audited values span a remarkable range, from about 8.41 to 168.88 millimetres per hour across tank beds and downstream settings, reflecting differences in soil texture, clay content, silt accumulation and geomorphic position. Low rates were associated with waterlogging and clayey or silty surfaces, while higher rates occurred in more permeable pediment settings. Notably, the team refused to treat repeated time-step readings within a single test as independent data points, avoiding the statistical trap of pseudo-replication, and declined to use inferential significance testing where class membership was incomplete — a conservative stance that lends the maps credibility.

When the measured infiltration layer was folded into a second AHP model, the picture changed. Very high recharge suitability covers about 8.86 percent of the area — more than four times the extent of very high groundwater potential — and occurs mainly where favourable geomorphology, low drainage density, high lineament density and high infiltration capacity coincide. Low and very low recharge classes dominate the rest of the landscape. The mismatch between the two maps is the study’s central message: groundwater occurrence and recharge acceptance are related but non-equivalent decisions, and conflating them risks placing recharge structures where water cannot enter, or borewells where water cannot be found.

The third strand of evidence came from below the surface. The team conducted eight Schlumberger vertical electrical soundings, injecting current through outer electrodes and measuring the potential difference between inner ones to calculate apparent resistivity, which was then modelled as one-dimensional layered profiles and assembled into pseudo-sections and geoelectric sections. The soundings revealed a consistent architecture: shallow weathered and fractured basalt near the tanks, commonly within about 7 to 10 metres below ground level, underlain locally by compact, resistive basalt extending to depths of roughly 30 metres or more. Around Karewadi, one section also hinted at a deeper fractured or permeable zone between about 10 and 50 metres toward the northeast. The interpretation was deliberately hydrogeological rather than purely colour-based, weighing resistivity magnitudes against curve shapes, depth continuity and field geological context — a caution the authors stress is essential, since conductive anomalies in Deccan basalt are not automatic proof of aquifer productivity.

The practical implications are concrete. Artificial recharge, the authors argue, should never be placed on surface suitability alone. Priority locations are those where moderate to high infiltration, gentle slopes and low drainage density, lineaments or weathered zones providing storage and transmissivity, and an absence of compact basalt barriers all overlap. In Moha, high infiltration near tank-associated surfaces signals genuine recharge opportunity, but the soundings show compact basalt beneath the shallow weathered material, so interventions should be designed to enhance shallow storage rather than assume unlimited deep percolation. Low-infiltration areas are not hopeless — desiltation, surface treatment, farm ponds and check structures can improve them — but they demand site-specific engineering rather than blanket classification as favourable.

More broadly, the workflow offers a transparent, field-constrained screening tool for the countless data-limited hard-rock watersheds where managed aquifer recharge is being promoted as a climate adaptation strategy. The authors are careful about its limits: final site selection should still incorporate site-wise infiltration data, well-yield observations and repeated water-level monitoring where available. But by insisting that potential and recharge be mapped separately, validated against independent physical evidence, and reconciled before any structure is built, the study sets a standard that could save communities across the Deccan and similar terrains worldwide from expensive wells that run dry and tanks that never fill.

Subject of Research: Groundwater potential and recharge zoning in semi-arid Deccan basalt terrain using GIS-based AHP, infiltration testing and vertical electrical soundings

Article Title: Groundwater potential and recharge zoning in semi-arid Deccan basalt using AHP, infiltration testing and VES

Article References: Warsi, T., Sonde, S. S., & Biswas, G. (2026). Groundwater potential and recharge zoning in semi-arid Deccan basalt using AHP, infiltration testing and VES. Discover Geoscience, 4(1), Article 382. https://doi.org/10.1007/s44288-026-00756-3

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00756-3

Keywords: groundwater, Deccan Traps, AHP, infiltration rate, vertical electrical sounding, managed aquifer recharge, hydrogeology, Maharashtra, semi-arid, GIS mapping, basalt aquifer, recharge zoning

The study, published in Earth Science Informatics, focused on the Taleghan Dam watershed, a vast catchment of roughly 124,062 hectares in Alborz Province. This semi-arid landscape, tucked into the Alborz mountain range, experiences the classic problems of mountainous drylands: intense seasonal rainfall, erodible slopes, and a delicate balance between water scarcity and destructive floods. The research team, led by Omid Asadi Nalivan of the University of Maragheh together with colleagues from Iran and India, set out to answer two intertwined questions: which locations are most suitable for building watershed dams and check dams, and which parts of the landscape are most prone to flooding in the first place.

What makes the approach remarkable is its grounding in real-world evidence rather than purely theoretical assumptions. The researchers compiled an inventory of 67 stone masonry check dams that had already been built in the watershed, treating these implemented structures as verified examples of successful siting decisions. They also assembled 160 historical flood occurrence points, documenting places where flooding had actually happened. These datasets served as the ground truth for training and testing three distinct machine learning algorithms: Maximum Entropy, better known as MaxEnt; Support Vector Machine, or SVM; and Artificial Neural Network, or ANN. Each algorithm learns differently, and comparing them reveals which approach best captures the complex interplay of factors that make a site suitable.

The models were fed an unusually rich set of environmental information. Twelve conditioning factors, spanning topography, hydrology, geology, and land cover, were compiled at a fine spatial resolution of 10 by 10 meters, meaning every pixel of the final maps represents a patch of ground about the size of a small living room. Before modeling began, the team checked all twelve variables for multicollinearity, the statistical problem that arises when input factors overlap so heavily that models become unstable. Every factor passed the test, with variance inflation factors below 10 and tolerance values above 0.1, the conventional thresholds that signal acceptable independence among predictors.

The results were striking. All three algorithms achieved area under the curve values, the standard measure of predictive skill, in the range of 0.80 to 0.93 for check dam site suitability, a band the researchers classify as very good to excellent. MaxEnt emerged as the clear winner, reaching a validation AUC of 0.93, meaning it correctly distinguished suitable from unsuitable locations with remarkable reliability. For flood susceptibility, MaxEnt again performed strongly, with training and validation AUC values of 0.91 and 0.89 respectively. The datasets were split 70:30 between training and validation, a standard practice that ensures models are judged on data they have never seen, guarding against the trap of simply memorizing the training examples.

Perhaps the most scientifically interesting outcome came from the Jackknife analysis, a technique that measures how much each environmental variable contributes to model performance by systematically removing one factor at a time. Five predictors stood out as the most informative: the Stream Power Index, stream order, slope, drainage density, and rainfall. The Stream Power Index, which combines the accelerating effect of slope with the accumulating effect of upstream flow area, essentially quantifies the erosive energy of flowing water at any point on the landscape. Its dominance makes intuitive sense: check dams work precisely where water has enough energy to cause erosion and flooding, but where the terrain allows a barrier to be built and to function effectively.

Statistical testing reinforced this picture. For flood susceptibility, stream order emerged as the most significant factor, with a coefficient of 0.349 and a p-value of 0.002, while the Stream Power Index showed a strong negative coefficient of minus 1.149 with a p-value below 0.001. In plain terms, the position of a location within the stream network hierarchy, and the erosive power of water moving through it, largely determine whether floods occur there. Higher-order stream reaches, the main channels where tributaries converge, concentrate flow and therefore concentrate both flood risk and the potential benefit of well-placed barriers.

When the trained models were applied across the entire watershed, they identified 147 prioritized sites with substantial potential for watershed dam construction. These locations share a consistent profile: they lie along higher-order stream reaches where flow is concentrated, on moderate slope gradients that allow construction without excessive engineering challenges, and on geotechnically stable rock formations that can anchor a structure securely. Just as revealing are the areas the models rejected. Gypsum-bearing geological units, mapped as the Ekgy formation, were consistently rated as low suitability, and so were zones close to active faults. Gypsum dissolves slowly in water, undermining the foundations of any structure built on it, while fault-proximate zones carry seismic risk that could crack or collapse a dam. The models, in effect, rediscovered sound engineering judgment from the data alone.

The dual mapping of check dam suitability and flood susceptibility gives watershed managers something they have rarely had before: a single, spatially explicit framework that shows both where floods threaten and where interventions will succeed. Instead of allocating conservation budgets across a whole province uniformly, or relying on the intuition of individual surveyors, planners can now direct investment to the 147 identified priority sites, confident that each one combines hydrological need with physical feasibility. The approach is also transferable. Because it relies on freely available geospatial data layers such as digital elevation models, satellite-derived land cover, and geological maps, the same workflow can be applied to mountainous semi-arid catchments anywhere in the world, from Central Asia to the Mediterranean to the American Southwest.

The timing could hardly be better. Around the world, check dams are enjoying renewed attention as climate change intensifies both floods and droughts, and as countries seek nature-based and low-cost solutions to water security. Recent studies from China’s Loess Plateau, where hundreds of thousands of check dams have transformed eroded landscapes, to Jordan, Syria, Nigeria, and Ghana, show growing global interest in these structures. Yet failures happen too, often because dams were sited on unstable ground or in channels where they could not withstand the forces they were meant to control. By demonstrating that machine learning models trained on real construction records can predict suitability with AUC values above 0.9, the Taleghan study offers a template for making every future check dam count. For semi-arid regions facing harsher, less predictable climates, that could mean the difference between conservation budgets that build resilience and money that simply washes downstream.

Subject of Research: Machine learning-based site selection for watershed conservation structures and flood susceptibility mapping in a semi-arid Iranian watershed

Article Title: Feasibility assessment of watershed conservation structure site selection using machine learning models in a semi-arid area, Iran

Article References: Nalivan, O. A., Yousefi, S., Shahbazi, A., & Shahi, N. R. (2026). Feasibility assessment of watershed conservation structure site selection using machine learning models in a semi-arid area, Iran. Earth Science Informatics, 19(11), Article 194. https://doi.org/10.1007/s12145-026-02251-2

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02251-2

Keywords: check dams, machine learning, MaxEnt, flood susceptibility, watershed management, Taleghan watershed, Iran, GIS, soil conservation, semi-arid, Stream Power Index, site suitability

The study area covers approximately 50,746 square kilometers of Rio Grande do Norte, a state where 155 of 167 municipalities fall within the semi-arid domain. Rainfall there is low, irregular, and highly variable from year to year, driven mainly by the seasonal migration of the Intertropical Convergence Zone, while the humid eastern coast receives moisture from Atlantic easterly disturbances and trade winds. This climatic gradient, superimposed on a dramatic geological contrast between ancient crystalline basement and younger sedimentary basins, produces a mosaic of hyperxerophilous and hypoxerophilous Caatinga, Atlantic Forest transitions, mangroves, dunes, and salt flats that has long frustrated automated mapping systems.

To capture that complexity, the researchers segmented the state into six analytical subunits by intersecting hydrographic basin subdivisions with geomorphological domains. The Apodi-Mossoró and Piranhas-Açu basins were each split into crystalline and sedimentary portions, and a cluster of smaller eastern basins was likewise divided, yielding AM Crystalline, AM Sedimentary, PA Crystalline, PA Sedimentary, A3 Crystalline, and A3 Sedimentary units. The logic is physical: sedimentary terrains carry deeper, flatter, more fertile soils suited to mechanized and irrigated agriculture, while crystalline landscapes are shallow, stony, and reddened by iron oxides, favoring open Caatinga and extensive livestock. Splitting the territory along these lines reduced spectral confusion between land use classes and bare soil, a persistent problem when a single classifier must handle an entire heterogeneous biome.

The classification engine itself was the random forest algorithm, an ensemble of decision trees trained by bootstrap aggregation, run on the Google Earth Engine cloud platform. Landsat-8 OLI imagery for 2023 was converted to surface reflectance and filtered for clouds and shadows, then enriched with an extraordinary stack of 153 predictor bands: six spectral bands, dozens of vegetation and water indices, tasseled cap brightness, greenness, and wetness transformations, fraction images from a linear spectral mixture model, land surface temperature derived from the thermal band, and estimated precipitation. The rainfall layer was itself a machine learning product, built from 198 rain gauges and modeled against elevation, slope, vegetation indices, temperature, humidity, and the De Martonne aridity index, averaged over 100 regression runs.

Training data came from 11,079 samples, combining manual photointerpretation of false-color Landsat mosaics, 59 field-collected GPS points, and 271 points adapted from MapBiomas. The classes followed the IBGE Land Use Classification System and MapBiomas hierarchy across five categories and thirteen level-II classes, from urban areas and aquaculture through croplands, pasture, forest and savanna vegetation, mangrove, herbaceous restinga, and hypersaline tidal flats, to water bodies and non-vegetated areas. Hyperparameter tuning was exhaustive: the number of trees was tested from 100 to 2,000 in ten repetitions per subunit, with out-of-bag error and overall accuracy guiding the choice, and the entire classification pipeline was run 100 times with different random seeds, consolidated by majority vote into a final map for each fragment.

The results were strikingly consistent. Accuracy metrics ranged from 78 to 88 percent and kappa values from 0.75 to 0.85 across the six subunits, with the crystalline portion of the Apodi-Mossoró basin posting the best performance at 0.88 overall accuracy and 0.85 kappa. Forest vegetation and the rivers, lakes, and ocean class achieved precision above 90 percent, and savanna vegetation exceeded 84 percent in crystalline areas. The trouble spots were revealing: salt marshes and herbaceous restinga in the Piranhas-Açu estuarine complex showed inconsistencies, aquaculture proved hard to separate from open water, and croplands were repeatedly confused with pasture, particularly where cassava and cashew grow on sandy soils with sparse cover, or where corn and sugarcane at the start of their cycles reflect light much like grazed land.

An uncertainty index, the normalized variety metric, mapped where the 100 iterations disagreed most. High uncertainty, above 35 percent, clustered in intensively managed croplands such as Serra do Mel and the irrigated fruit-growing hub of the Piranhas-Açu basin, in the heterogeneous urban fabric of Parnamirim and Macaíba, and in the desertification-prone Seridó microregion, where open savanna-steppe vegetation on shallow crystalline soils blurs into pasture. Dense natural vegetation on mountain slopes and massifs, by contrast, showed almost no disagreement, confirming that the model is most confident exactly where the landscape is most coherent.

Perhaps the most consequential finding lies in the variable importance rankings. Across every geomorphological compartment, the most influential predictors were not spectral indices but spatial position, longitude and latitude, altitude, and climatic conditions, namely precipitation and land surface temperature. This makes ecological sense: latitude governs solar input, altitude shapes local temperature and rainfall, and the amount and timing of rain drive the leafing and flowering cycles of seasonally dry vegetation. Surface temperature, meanwhile, separates irrigated fields and dense canopy from exposed soil and degraded pasture. The forest vegetation class, restricted mainly to rainier western and eastern highlands, was especially dependent on these topographic and climatic cues, while water bodies and urban areas remained best distinguished by spectral signatures.

The mapped landscape tells its own sobering story. Native vegetation, whether preserved, degraded, or recovering, still dominates at roughly 58 percent of the territory, about 3 million hectares, with savanna-steppe covering some 2.88 million hectares and forest formations a mere 37,740 hectares concentrated in the wetter ranges. But more than 41 percent of the biome in Rio Grande do Norte now bears anthropogenic uses: pastures span about 1.77 million hectares, croplands around 135,000 hectares, and a growing footprint of wind farms, solar installations, mining, oil extraction, aquaculture, and urban infrastructure reshapes coastal and upland scenery alike. The contrast between the irrigated fruit export economy of the sedimentary Açu-Mossoró hub and the rainfed subsistence farming of the crystalline Sertaneja Depression mirrors the underlying geology, a dichotomy the classifier captured through the sheer weight of longitude and elevation in its decision trees.

When the team compared their geomorphological approach against the traditional method of classifying whole river basins, overall accuracy was statistically similar, but the spatial errors told a different tale. Basin-wide classification inflated croplands in drainage lines, merged urban areas with other uses, and generalized exposed rock and dunes into broad anthropogenic categories, while the terrain-aware segmentation kept those distinctions intact. The authors caution that spatial coordinates can also let a model merely memorize training patterns rather than generalize, a known pitfall of spatially autocorrelated predictors. Still, their conclusion is clear: for seasonally dry tropical forests facing climate projections of rising heat and declining tree cover, mapping systems that understand relief, water, and position, not just reflected light, offer a genuinely better window on one of the world’s most threatened drylands.

Subject of Research: Machine learning land use and vegetation cover mapping in the Caatinga biome using morphometric and climatic variables

Article Title: Integrating morphometric and climatic variables into the mapping of land use and vegetation cover in the Caatinga biome

Article References: Guedes da Silva, A. D., Flor de Souza, S. F., Lucena, R. L., & Reis, J. S. (2026). Integrating morphometric and climatic variables into the mapping of land use and vegetation cover in the Caatinga biome. Environmental Monitoring and Assessment, 198(10), Article 1132. https://doi.org/10.1007/s10661-026-15949-z

Image Credits: AI Generated

DOI: 10.1007/s10661-026-15949-z

Keywords: Caatinga, random forest, land use land cover, Google Earth Engine, remote sensing, semi-arid, Rio Grande do Norte, geomorphology, precipitation, machine learning, dry tropical forest, MapBiomas

The research, conducted by Flora Felix Manyama of the University of Dodoma, focused on four habituated baboon troops, two of olive baboons (Papio anubis) and two of yellow baboons (Papio cynocephalus), within the 871-square-kilometre reserve that straddles the Kondoa and Chemba districts of the Dodoma region. Swagaswaga is a classic semi-arid savannah ecosystem: rolling hills, rocky outcrops and miombo woodlands punctuated by drainage systems such as the Makati River, where annual rainfall of 600 to 1000 millimetres falls almost entirely between November and April. From May to October, the landscape is defined by hot, sunny days, cooler nights and steadily vanishing water sources, an environmental rollercoaster that makes the reserve an ideal natural laboratory for studying how animals respond to resource scarcity.

The fieldwork was demanding in its precision. Across 67 observation days totalling 440 hours, baboon troops were followed on foot from six o’clock in the morning, before they left their sleeping sites, until they settled into a new sleeping site around half past six or seven in the evening. Observers, working at an average distance of five metres, recorded troop movements continuously using hand-held Garmin 520Hcx GPS units, a technique considered among the most reliable for quantifying ranging behaviour in primates. Data collection ran through the wet season from January to March 2024 and the dry season from June to August 2024, allowing direct seasonal comparisons for the same troops under radically different ecological conditions.

The headline result is stark. When both species were pooled, baboons travelled an average of 3.01 plus or minus 0.07 kilometres per day in the dry season, against 1.06 plus or minus 0.03 kilometres in the wet season, a difference that proved statistically significant under a Mann-Whitney U test. The explanation, the study argues, lies in water. During the wet months, ephemeral water sources are replenished and vegetation flourishes, meaning baboons rarely need to venture far for a drink or a meal. When the rains stop, those sources dry up, forcing the troops to expand their search radius and, in doing so, cover three times the ground they would under favourable conditions.

The two species, however, did not respond identically. Olive baboons consistently outwalked their yellow cousins in both seasons, averaging 3.27 kilometres per day in the dry season and 1.17 kilometres in the wet, while yellow baboons logged 2.76 and 0.95 kilometres respectively. Although the overall difference between the species did not reach statistical significance in the Kruskal-Wallis test, the pattern was consistent. The most plausible driver, the study suggests, is group size: olive baboons live in larger troops than yellow baboons at Swagaswaga, and larger groups exhaust food patches faster and face greater within-group feeding competition, which compels them to travel farther and more frequently between resource patches.

Group dynamics themselves shifted with the seasons in ways that illuminate the fundamental trade-offs of social living. Average troop sizes were significantly larger in the wet season, at 16 individuals, than in the dry season, when they dropped to 9. Olive baboons formed the biggest groups, at 18 in the wet season and 11 in the dry, compared with 14 and 7 for yellow baboons. Importantly, the study notes that these fluctuations were not driven by births or deaths but by fission and fusion, the splitting and merging of groups that is common among primates and governed by both social and ecological pressures. When resources are plentiful, the costs of crowding are low and the benefits of group living, including dilution of predation risk and improved vigilance, tip the balance in favour of large aggregations. When water and food grow scarce, smaller groups become energetically advantageous.

This logic follows a well-established framework in behavioural ecology: as time spent travelling increases, a threshold is eventually reached at which the energy cost of moving becomes so high that small groups outcompete large ones. In semi-arid environments like Swagaswaga, where prolonged dry seasons can sharply curtail food and water availability, an increase in group size directly expands the area a troop must cover to meet its collective needs. Individual baboons in larger groups must therefore travel farther and burn more energy than they would in smaller aggregations, a cost that the dry season amplifies to a breaking point. The observed seasonal fissioning is the animals’ solution to that arithmetic.

Perhaps the most consequential finding for conservation is what happens at the reserve’s edge. Although baboons spent roughly 85 percent of their observation time inside Swagaswaga, both species regularly moved beyond the borders, where human settlements and cultivated farms offer alternative food sources. Olive baboons, with their larger troops and longer daily ranges, spent more time outside the reserve than yellow baboons, and both species ventured out most often during the dry season, presumably driven by the same resource shortages that inflated their travel distances. Baboons are omnivorous and opportunistic, and the study notes they will eat cultivated crops and even domesticated animals such as goats, sheep and poultry, behaviour that places them in direct conflict with farmers.

These crop-raiding excursions have tangible costs on both sides. Human-wildlife conflict around Swagaswaga typically centres on baboons raiding farms just beyond the reserve boundary, and prolonged conflict threatens both local livelihoods and the long-term tolerance that conservation depends on. The study argues that quantifying how much time baboons spend outside the protected area, and under which seasonal conditions, gives wildlife managers a practical tool: interventions can be timed and targeted for the dry season, when the risk of encounters peaks. Because baboons are widely regarded as agricultural pests and are classified as a species of least concern, they rarely attract conservation funding, yet their ecology makes them a bellwether for how semi-arid protected areas function.

The broader implications stretch into a warming future. Climate change is expected to make rainfall in semi-arid regions more erratic, potentially prolonging dry seasons and intensifying water shortages, which would push baboons and other wildlife into even longer daily movements and more frequent excursions beyond reserve boundaries. Understanding the precise relationship between seasonality, ranging and group structure, as this GPS-based study does, is therefore a first step toward predicting and managing those pressures. The research calls for further work on human-baboon conflict around Swagaswaga to inform management strategies, and its data are held at the University of Dodoma for future study. For now, the image it leaves is vivid: on the hottest, driest days, when the ephemeral pools have cracked and the acacias stand bare, baboon troops tighten their ranks, shrink their numbers and set out across the savannah on journeys three times longer than any they make in the season of plenty.

Subject of Research: Effects of seasonal variation on daily travel distances and group sizes of olive and yellow baboons in a semi-arid Tanzanian game reserve

Article Title: Effects of seasonality on baboons’ (Papio cynocephalus and Papio anubis) daily movement patterns and group sizes in semi-arid environment at Swagaswaga game reserve, Tanzania

Article References: Effects of seasonality on baboons’ (Papio cynocephalus and Papio anubis) daily movement patterns and group sizes in semi-arid environment at Swagaswaga game reserve, Tanzania. (n.d.). https://doi.org/10.1007/s44353-025-00058-8

Image Credits: AI Generated

DOI: 10.1007/s44353-025-00058-8

Keywords: baboons, seasonality, Swagaswaga Game Reserve, Tanzania, olive baboon, yellow baboon, daily travel distance, group size, semi-arid, human-wildlife conflict, GPS tracking, primatology

The study, conducted across parts of Anantapur and Kurnool districts in Andhra Pradesh, focused on a landscape dominated by Inceptisols and Vertisols under a punishing semi-arid climate, where summer temperatures climb to around 37 degrees Celsius and most of the roughly 762 millimeters of annual rainfall arrives in just three monsoon months. Using a classified land use and land cover map at 1:50,000 scale, the researchers selected 32 representative sites spanning six major land uses: forest, agriculture, horticulture, plantation, wasteland, and fallow land. The set included seven fallow sites, seven horticultural plots, seven agricultural fields, five forest sites, three wasteland plots, and three plantations, a design intended to capture the full spatial variability of soil properties and moisture conditions across the region.

What sets the study apart technically is its measurement approach. Rather than relying on traditional gas chromatography or chamber methods that require collecting and transporting gas samples to a laboratory, the team deployed a Vaisala CARBOCAP GMP343 carbon dioxide probe, a diffusion-type sensor that uses single-beam, dual-wavelength non-dispersive infrared technology to measure CO₂ directly in the soil. The probe, fitted with a collar area of 22.8 square centimeters and a volume of 200 cubic centimeters, was inserted approximately five centimeters into the surface soil after scraping away loose material to ensure good contact. Because the diffusion adapter allows soil gases to reach the sensor membrane naturally, measurements were taken without disturbing the soil atmosphere, an advantage when working in shrink-swell Vertisols that crack and shift dramatically as they dry.

Field campaigns were timed to capture the seasonal contrast, with measurements taken in late January and early February as representative of the dry season and again in mid-August during the wet season. At each site, the probe recorded CO₂ concentrations every 15 seconds over 900-second windows, repeated across five time segments throughout the daylight hours. The researchers calculated fluxes by fitting linear regressions to the concentration-time curves, applying the regression only where the relationship was strongly linear with a coefficient of determination of at least 0.90, and discarding readings showing curvature, irregular fluctuation, or sensor drift. The slope of carbon dioxide accumulation was then converted to a flux in milligrams of carbon per square meter per hour using chamber geometry and an ideal gas law correction, a calculation the team validated following established closed-chamber methodology for non-dispersive infrared analyzers.

The results revealed a striking hierarchy of emissions across land uses. In the dry season, forest soils released carbon dioxide at 70.7 milligrams of carbon per square meter per hour, while fallow land emitted a mere 2.40 milligrams. The wet season amplified the contrast, with forests reaching 247.1 milligrams and fallow soils just 8.30 milligrams. Across both seasons, the overall ordering ran from forest to agriculture to horticulture to plantation to wasteland to fallow. The researchers attribute the forest advantage to a continuous supply of labile carbon from litterfall and root systems, deeper rooting that sustains microbial activity through dry spells, and canopy microclimates that buffer temperature swings and retain moisture. Fallow and wasteland soils, stripped of vegetation and receiving minimal carbon inputs, simply lack the substrate to fuel vigorous respiration.

Statistical analysis pinpointed the environmental levers behind these differences. Soil moisture showed the strongest correlation with CO₂ efflux, with a Pearson coefficient of 0.65, followed by soil temperature at 0.48 and organic carbon content at 0.416, all significant at the one percent probability level. Carbonate content and soil pH were negatively correlated with fluxes, at coefficients of minus 0.369 and minus 0.396 respectively, reflecting how calcium carbonate formation sequesters carbon and how alkaline conditions, common outside the more acidic forest soils, suppress microbial proliferation. Principal component analysis reinforced the picture, grouping CO₂ efflux, organic carbon, moisture, and temperature in the same directional space, while pH and carbonates pointed the opposite way. During the dry season, moisture levels hovered near or below the wilting point for these sandy loam and sandy clay loam soils, effectively shutting down microbial metabolism, whereas wet season moisture rose into the range between wilting point and field capacity where biological activity thrives.

The modeling component of the study compared four machine learning algorithms trained on soil properties to predict efflux: Partial Least Squares Regression, Random Forest, Gradient Boosting Regression, and k-Nearest Neighbors. The dataset was split with 75 percent of samples used for calibration and 25 percent held out for independent validation. Random Forest dominated, achieving a coefficient of determination of 0.97 with a root mean squared error of 3.36, meaning it explained 97 percent of the variance in observed emissions with minimal error. Gradient Boosting followed with an R² of 0.83 and an RMSE of 5.94, k-Nearest Neighbors managed 0.70 with an RMSE of 9.03, and Partial Least Squares Regression trailed at 0.51 with an RMSE of 11.86. The gap illustrates a core principle of modern environmental modeling: soil respiration is governed by complex, nonlinear interactions that tree-based ensemble methods capture naturally, while linear approaches leave most of the signal on the table.

Variable importance analysis from the Random Forest model, based on the increase in node purity metric, ranked soil moisture as the most influential predictor, followed by soil temperature and organic carbon. Variables such as pH, carbonate content, silt, and sand made moderate contributions, while bulk density, electrical conductivity, and clay content ranked lowest, suggesting their effects on emissions are indirect or weak in this particular dataset. The dominance of moisture and temperature aligns with decades of soil respiration research showing that water availability controls microbial activity while temperature regulates metabolic rates, but the study adds a crucial semi-arid dimension: in landscapes where rainfall is concentrated into a few months, the wet season effectively becomes the carbon emission season, and any warming-driven change in monsoon timing could reshape the regional carbon budget.

The authors are candid about the limitations. All measurements were taken during daytime hours because of logistical constraints in farmers’ fields, potentially missing nighttime fluxes, and the two-season sampling window excludes year-round variability. Soil biological parameters such as microbial biomass carbon were not measured, leaving a mechanistic layer unexplored. Nonetheless, the practical implications are substantial. The finding that higher soil efflux in forests reflects vigorous carbon cycling rather than net carbon loss is important, since forests continue to accumulate carbon through photosynthesis at rates exceeding their respiratory losses, while degraded fallow and wasteland soils offer little sequestration and modest emissions. For land managers in semi-arid regions, the message is twofold: maintaining vegetation cover and organic carbon inputs keeps soils biologically active and carbon-rich, and data-driven tools like Random Forest can now flag where carbon losses are likely to accelerate. As climate change pushes these already fragile ecosystems toward harder edges of heat and drought, continuous year-round monitoring paired with machine learning prediction may become an essential instrument for keeping semi-arid soils on the right side of the carbon ledger.

Subject of Research: Soil CO₂ efflux variability across land uses in a semi-arid Indian region and its machine learning prediction

Article Title: Evaluating soil CO₂ efflux variability across diverse land uses in semi-arid region of Southern India and its prediction through machine learning

Article References: Evaluating soil CO₂ efflux variability across diverse land uses in semi-arid region of Southern India and its prediction through machine learning. (n.d.). https://doi.org/10.1186/s44329-026-00050-0

Image Credits: AI Generated

DOI: 10.1186/s44329-026-00050-0

Keywords: soil CO₂ efflux, land use, machine learning, Random Forest, semi-arid, soil carbon, soil moisture, seasonal variation, Andhra Pradesh, soil respiration, India, carbon cycle