Bare Land Research Articles

Supervised land cover classification of Nueva Ecija using random forest in google earth

Land cover classification is essential for environmental monitoring, urban planning, and sustainable land use management. This study presents a supervised land cover classification in Nueva Ecija, Philippines, utilizing Google Earth Engine (GEE) and the Random Forest (RF) algorithm, applied to Landsat 8 imagery. A total of 1,523 samples were collected representing five land cover types: built-up areas, agricultural lands, water bodies, forests, and barren land. The classification achieved an overall accuracy of 87.69% with a Kappa coefficient of 0.841. Future work should explore the integration of seasonal imagery and topographic indices for improved performance. This methodology provides crucial insights for resource management and supports regional policy development in Nueva Ecija.

Assessment of dyna clue model for prediction of spatio-temporal dynamics of urbanisation and green space in Jaipur district, Rajasthan (India)

Urbanisation in Jaipur, Rajasthan’s capital, has significantly impacted land use and land cover (LULC) over the past four decades. This study addresses the pressing issue of how rapid urban expansion affects green spaces and environmental balance. Utilizing satellite imagery and a support vector machine (SVM) algorithm, LULC maps for 1993, 2003, 2013, and 2023 were generated to analyze changes in land cover, including agriculture, barren land, built-up areas, vegetation, wasteland, fallow land, and water bodies. The Dyna-CLUE model, incorporating variables such as elevation, slope, aspect, and proximity to streams and roads, was employed to forecast LULC changes for 2033 and 2043. The findings reveal a marked increase in built-up areas at the expense of vegetative cover, driven by urbanization, infrastructural development, agricultural expansion, climate change, and policy decisions. With Kappa coefficients indicating high agreement between classified land cover and ground truth data (84–90% accuracy), the study projects continued urban growth and further decline in vegetation. This underscores the urgent need for sustainable land use planning and environmental conservation in Jaipur. Proposed strategies to address these issues include establishing ecological zones, enforcing green building regulations, promoting mixed-use development, and implementing a 15-min green space policy. These measures aim to enhance green space distribution and support Jaipur’s transition towards a greener urban environment.

The Process of Soil Carbon Sequestration in Different Ecological Zones of Qingtu Lake in the Arid–Semi-Arid Region of Western China

As a vital component of the global carbon pool, soils in arid and semi-arid regions play a significant role in carbon sequestration. In the context of global warming, increasing temperatures and moisture levels promote the transformation of barren land into wetlands, enhancing carbon sinks. However, the overdevelopment of oases and excessive extraction of groundwater lead to the opposite effect, reducing carbon sequestration. This study examines two soil types—meadow soil (MS) and swamp soil (SS)—from Qingtu Lake, an arid lake in western China. It analyzes the sources of soil inorganic carbon, the composition and origin of dissolved organic matter (DOM), and the relationships between microbes, soil organic carbon (SOC), soil inorganic carbon (SIC), mineral composition, and soil texture. The results indicate that inorganic carbon in the study area consists of both primary carbonate minerals and secondary pedogenic carbonates. The DOM primarily consists of two components, both identified as terrestrial humic substances. In meadow soils, bacterial activity drives the weathering of plagioclase, which releases Ca2+ necessary for the formation of pedogenic carbonates. Plagioclase also provides colonization sites for microbes and, along with microbial activity, participates in the soil carbon cycle. Within the soil community, bacteria appear to play a more critical role than fungi. In contrast, microbial contributions to the carbon cycle in swamp soils are weaker, with minerals predominantly interacting with organic carbon to form mineral-associated organic matter, thus promoting the soil carbon cycle. These findings have important implications for understanding soil carbon sinks under different micro-ecological conditions in arid and semi-arid regions. Through targeted human intervention, it is possible to enhance carbon sequestration in these areas, contributing to the mitigation of global climate change.

Correlation between Different Factors of Non-point Source Pollution in Yangtze River Basin

This research used the improved export coefficient model to estimate non-point source load in sub-watersheds and a self-organizing map model to identify the correlation relationship of factors, that can be affected by non-point source load in Yangtze River Basin. Research results indicate that total nitrogen load is 4.87 - 15.53 kg/ha; total phosphorus load is 0.18 - 0.51 kg/ha; Dong Ting Lake sub-basin has the lowest contribution value of total nitrogen and phosphorus; Tai Lake sub-basin has the highest contribution load of total nitrogen and total phosphorus; Jinsha River sub-basin reveals the highest value of total nitrogen and phosphorus contribution on grassy land and desert land; Tai Lake sub-basin has the highest total nitrogen and total phosphorus load on forest, agricultural land, and urban construction land. The correlation relationship of factors reveals a very close correlation with each other as precipitation, total nitrogen load, and total phosphorus load factors have correlation in the highest value, in which total nitrogen load and total phosphorus load factors have a close correlation with each other from the lowest value to highest value; wetland and barren land factors correlate with the lowest value and the highest value; agricultural land, forestland, and grassy land factors correlate with one another in the high value and the highest value. The forest cover rate factor, as well as the population factor, does not correlate with other factors.

Vegetative Index Intercalibration Between PlanetScope and Sentinel-2 Through a SkySat Classification in the Context of “Riserva San Massimo” Rice Farm in Northern Italy

Rice farming in Italy accounts for about 50% of the EU’s rice area and production. Precision agriculture has entered the scene to enhance sustainability, cut pollution, and ensure food security. Various studies have used remote sensing tools like satellites and drones for multispectral imaging. While Sentinel-2 is highly regarded for precision agriculture, it falls short for specific applications, like at the “Riserva San Massimo” (Gropello Cairoli, Lombardia, Northern Italy) rice farm, where irregularly shaped crops need higher resolution and frequent revisits to deal with cloud cover. A prior study that compared Sentinel-2 and the higher-resolution PlanetScope constellation for vegetative indices found a seasonal miscalibration in the Normalized Difference Vegetation Index (NDVI) and in the Normalized Difference Red Edge Index (NDRE). Dr. Agr. G.N. Rognoni, a seasoned agronomist working with this farm, stresses the importance of studying the radiometric intercalibration between the PlanetScope and Sentinel-2 vegetative indices to leverage the knowledge gained from Sentinel-2 for him to apply variable rate application (VRA). A high-resolution SkySat image, taken almost simultaneously with a pair of Sentinel-2 and PlanetScope images, offered a chance to examine if the irregular distribution of vegetation and barren land within rice fields might be a factor in the observed miscalibration. Using an unsupervised pixel-based image classification technique on SkySat imagery, it is feasible to split rice into two subclasses and intercalibrate them separately. The results indicated that combining histograms and agronomists’ expertise could confirm SkySat classification. Moreover, the uneven spatial distribution of rice does not affect the seasonal miscalibration object of past studies, which can be adjusted using the methods described here, even with images taken four days apart: the first method emphasizes accuracy using linear regression, histogram shifting, and histogram matching; whereas the second method is faster and utilizes only histogram matching.

Diversified Vegetation Cover Alleviates Microbial Resource Limitations within Soil Aggregates in Tailings.

Resource demand by soil microorganisms critically influences microbial metabolism and then influences ecosystem resilience and multifunctionality. The ecological remediation of abandoned tailings is a topic of broad interest, yet our understanding of microbial metabolic status in restored soils, particularly at the aggregate scale, remains limited. This study investigated microbial resources within soil aggregates from revegetated tailings and applied a vector model of ecoenzymatic stoichiometry to examine how different vegetation patterns (grassland, forest, or bare land control) impact microbial resource limitation. Five-year vegetation restoration significantly elevated carbon (C) and nitrogen (N) concentrations and their stoichiometric ratios in soil aggregates (approximately 2-fold), although these increases were not translated to in the microbial biomass and its stoichiometry. The activities of C- and phosphorus (P)-acquiring extracellular enzymes in these aggregates increased substantially postvegetation, with the most pronounced escalation in macroaggregates (>0.25 mm). The vector model results indicated soil microbial metabolism was colimited by C and P, most acutely in microaggregates (<0.25 mm). This colimitation was exacerbated by monotypic vegetation cover but mitigated under diversified vegetation cover. Soil nutrient stoichiometric ratios in vegetation restoration controlled microbial resource limitation, overshadowing the impact of heavy metals. Our findings underscore that optimizing resource allocation within soil aggregates through strategic revegetation can enhance microbial metabolism in tailings, which advocates for the implementation of diverse vegetation covers as a viable strategy to improve the ecological development of degraded landscapes.

Soil carbon sequestration potential of different land use systems: evidence from sub-humid southern plains and Aravalli hills of Rajasthan, India.

This study has assessed total soil organic carbon (TOC) fractions, carbon management index (CMI), and carbon sequestration economic value under diverse land use systems (LUSs) of sub-humid Southern plains of Rajasthan, India. The study dealt with six LUSs: barren land (BL), agricultural land (AL), agri-horticulture (AH), horticultural land (HL), grassland (GL), and natural forest land (FL) were selected for the study. FL contained the highest TOC (13.04 ± 0.74gkg-1), particulate organic carbon (POC) (2.2 ± 0.19gkg-1), mineral-associated organic carbon (MAC) (10.84 ± 0.54gkg-1), and CMI (230.36 ± 4.13), whereas BL had the lowest amount of TOC (3.53 ± 0.4gkg-1), POC (0.47 ± 0.06gkg-1), MAC (3.06 ± 0.32gkg-1), and CMI (54.60 ± 4.2). The impact of LUSs on soil carbon lability index (LI) was minimal in all LUSs except FL exhibited statistically insignificant variations in LI. TOC stock showed the highest decline in BL (71.04%), AL (55.29%), AH (44.38%), and HL (25.92%) uses compared with the FL system. Different LUSs result in varying amounts of carbon stocks, representing the relative carbon credit gain. The maximum carbon credit was achieved by FL, which was roughly US $49,303 and 245% higher than BL. These results indicate the reinstatement of BL and AL towards HL and AH systems, and effective recycling of residues could improve the TOC storage in the study region.

Accuracy Assessment of Land Use Land Cover Classification Using Machine Learning Classifiers in Google Earth Engine; A Case Study of Jammu District

Abstract. LULC (Land Use and Land Cover) involves classifying and describing different land types and their usage. Using satellite imagery for LULC mapping is increasing in remote sensing. This study focuses on Jammu district in India, situated between mountain ranges from north and south make it eco-sensitive zone. Expanding of human activity and loss of natural resources make it vulnerable if mismanaged. Study of LULC is important because of it and this study deals with efficiency and accuracy of various machine learning classifiers for LULC. This study uses machine learning classifiers - Random Forest (RF), Support Vector Machine (SVM), Gradient Boosted Trees (GTB), and Classification and Regression Trees (CART) - for the task, leveraging Google Earth Engine (GEE). Sentinel-2 satellite data from January 1 to March 31, 2023, with specific spectral bands (B4, B3, B2 &amp;amp; B8, B4, B3), were used for image preprocessing. Six classes: built-up, water, agricultural land, fallow land, forest, and barren land, each with 100 sample points, were used for classification. After training, the classifiers' accuracy was evaluated using Overall Accuracy and Kappa Coefficient. The results showed RF as the top performer with an overall accuracy of 99.36% and a Kappa coefficient of 99.11%, followed by SVM, GTB, and CART. This highlights the effectiveness of machine learning classifiers, especially RF and SVM, in accurately mapping LULC patterns in Jammu district, suggesting RF's potential as a reliable tool for remote sensing-based LULC mapping.

Study on the Value of Ecosystem Services and Land Use Change in the Area North of the Tianshan Mountains from 2000 to 2020

The northern Tianshans region in the arid zone of northwestern China plays a key role in promoting high-quality development of the ecological environment. In recent years, ecological environmental protection in this region has encountered major challenges due to the dual impacts of human activities and natural changes. In order to accurately assess the current status of the ecological environment in the northern Tianshans, this study analyzed the spatial and temporal changes in land use and ecological and environmental effects using land use data from 2000 to 2020 and explored the current status of land use, land use dynamic process, and ecosystem service value (ESV) in the region. Two-factor spatial autocorrelation analysis revealed the spatio-temporal characteristics of the value changes over the 20-year period as well as their spatial heterogeneity. The results show that: (1) land use changes are dominated by increases in cultivated land, forest land, watershed, and wetland, and decreases in grassland, glacier snow, and bare land. Of these changes, the expansion of cultivated land area is the most significant, showing a total increase of 1136.13 × 103 hm2. (2) The ESV increased and then decreased, reaching the highest value in 2005 and the lowest in 2020. The value of individual ecosystem services is dominated by regulating services, accounting for about 67% of the total value. (3) The overall regional balance of ecological environment quality and the contribution rate of the conversion from bare land to other land types is as high as 82.7986%, constituting the main factor in regional ecological environment improvement. The spatial distribution pattern exhibits the characteristic of “high in the northeast and low in the southwest”. (4) There is a positive correlation between the ESV, the Normalized Difference Vegetation Index (NDVI), and the Anthropogenic Impact Composite Index, with the NDVI being the main cause of spatial heterogeneity in the ESV. The research results provide a scientific basis for ecological protection, land management, and policy formulation in the northern foothills of the Tianshan Mountains and are of great significance for promoting regional sustainable development.

Identification of Spatial Patterns of Soil Erosion Based on the Combination of RUSLE and MCDA in the Ahferom District, Northern Ethiopia

Soil erosion is a widespread concern that is indeed considered to be a significant environmental issue, and it has particularly severe consequences in less developed countries like Ethiopia. An effective watershed management procedure for establishing priority is supported by the identification of erosion-susceptible areas. Therefore, the main objective of the study was to assess soil erosion dynamics and its spatial pattern using a novel methodological framework combining the RUSLE and MCDA. The study used data on land use and cover, topography, soil, and climatic data. The analytical hierarchy process (AHP) were used to identify soil erosion-susceptible areas and the factors were weighted using a pairwise comparison matrix, and weights were combined using weighted overlay in GIS. Our results indicated that the mean annual soil loss rate was 27.10 t ha−1 yr−1, while the total soil loss from the entire study area was 3.11 Mt. The highest soil loss was observed in bare land (30.54 t ha−1 yr−1) and farmland (23.65 t ha−1 yr−1), which were considered as the most susceptible land types to erosion. Likewise, 10.3% of the study area is very highly susceptible; 20.2% is highly susceptible, 24.2% of the area is moderately susceptible, 27.1% is low, and 18.2% has very low susceptibility. The district’s most significant erosion-susceptible areas are characterized by steep slopes that are composed of farmland and bare land. This suggests the majority of the area is susceptible to erosion, requiring interventions to reverse the alarming degradation level. The presented framework has a board application to estimate regional soil erosion and to identify spatial patterns of soil erosion.

Multi-Source Remote Sensing Data for Wetland Information Extraction: A Case Study of the Nanweng River National Wetland Reserve

Wetlands play a vital role in regulating the global carbon cycle, providing biodiversity, and reducing flood risks. These functions maintain ecological balance and ensure human well-being. Timely, accurate monitoring of wetlands is essential, not only for conservation efforts, but also for achieving Sustainable Development Goals (SDGs). In this study, we combined Sentinel-1/2 images, terrain data, and field observation data collected in 2020 to better understand wetland distribution. A total of 22 feature variables were extracted from multi-source data, including spectral bands, spectral indices (especially red edge indices), terrain features, and radar features. To avoid high correlations between variables and reduce data redundancy, we selected a subset of features based on recursive feature elimination (RFE) and Pearson correlation analysis methods. We adopted the random forest (RF) method to construct six wetland delineation schemes and incorporated multiple types of characteristic variables. These variables were based on remote sensing image pixels and objects. Combining red-edge features, terrain data, and radar data significantly improved the accuracy of land cover information extracted in low-mountain and hilly areas. Moreover, the accuracy of object-oriented schemes surpassed that of pixel-level methods when applied to wetland classification. Among the three pixel-based schemes, the addition of terrain and radar data increased the overall classification accuracy by 7.26%. In the object-based schemes, the inclusion of radar and terrain data improved classification accuracy by 4.34%. The object-based classification method achieved the best results for swamps, water bodies, and built-up land, with relative accuracies of 96.00%, 90.91%, and 96.67%, respectively. Even higher accuracies were observed in the pixel-based schemes for marshes, forests, and bare land, with relative accuracies of 98.67%, 97.53%, and 80.00%, respectively. This study’s methodology can provide valuable reference information for wetland data extraction research and can be applied to a wide range of future research studies.

Evaluation of Groundwater Potential Zones Using GIS‐Based Machine Learning Ensemble Models in the Gidabo Watershed, Ethiopia

AbstractThe main objective of this study is to map and evaluate groundwater potential zones (GWPZs) using advanced ensemble machine learning (ML) models, notably Random Forest (RF) and Support Vector Machine (SVM). GWPZs are identified by considering essential factors such as geology, drainage density, slope, land use/land cover (LULC), rainfall, soil, and lineament density. This is combined with datasets used for training and validating the RF and SVM models, which consisted of 75 potential sites (boreholes and springs), 22 non‐potential sites (bare lands and settlement areas), and 20 potential sites (water bodies). Each dataset is randomly partitioned into two sets: training (70%) and validation (30%). The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC‐ROC). The AUC of the RF model is 0.91, compared to 0.88 for the SVM model. Both models classified GWPZs effectively, but the RF model performed slightly better. The classified GWPZ map shows that high GWPZs are typically located within water bodies, natural springs, low‐lying regions, and forested areas. In contrast, low GWPZs are primarily found in shrubland and grassland areas. This study is vital for decision‐makers as it promotes sustainable groundwater use and ensures water security in the studied area.

Effects of Different Land Uses and Slope on Runoff and Soil Loss on the Loess Plateau of China

ABSTRACTThe Loess Plateau has one of the most serious areas of soil erosion in China; therefore, studying the effect of soil and water conservation measures on the erosion of loess soil slopes is important for land management and agricultural development. This study is based on 5 years of field runoff observations and rainfall data from 2015 to 2019. Correlation and regression analyses were used to study the runoff and sediment production of loess soil and its relationship with rainfall, slope and land use. The results show that &gt; 80% of the erosive rainfall occurs mainly in July to August in the area. Runoff and soil loss from the plots differed substantially, depending on land use, soil conservation measures and slope degree. Bare plots experienced the highest soil loss rate of over 27 t/ha with higher runoff depth, followed by cultivated plots (4.55 t/ha), grass plots (0.43 t/ha), shrub plots (0.35 t/ha) and forest plots (0.13 t/ha). Among the four soil and water conservation measures, the runoff and sediment reduction benefits of forest and shrub plots were the highest. The runoff reduction benefit fluctuated at approximately 0.8, and the benefit of sediment reduction was ≈1. The overall performance was forest &gt; shrubland &gt; grassland &gt; cultivated land &gt; bare land, and the benefits of sediment reduction were greater than those of runoff reduction. Forests had the highest soil retention capacity on slopes from 5° to 25°, and forests on gentle slopes had the best water storage capacity. Shrub plots had the best water storage capacity when they were on steep slopes. Rainfall erosion forces were significantly and positively correlated (p &lt; 0.01) with runoff depth and soil loss, making it the most significant rainfall indicator of sediment production. Under the same rainfall conditions, soil and water conservation measures are still required on bare slopes, land use is the main control factor for slope sediment production, and appropriate land use can greatly reduce the threat of slopes in terms of soil erosion. This study shows that soil and water conservation measures are imperative to prevent runoff and soil loss, especially for bare land without any measures; on steep slopes, a vegetation combination primarily featuring shrubs is expected to achieve greater benefits in reducing runoff and sediment. This study can provide a scientific basis for the management of vegetation measures and the implementation of soil and water conservation measures on loess soil slopes.

Land Use and Land Cover Change in East Java Indonesia from 1972 to 2021: Learning from Landsat

This study analyses land use and land cover (LULC) changes during the last five decades (1970–2021) in East Java Province, Indonesia. The changes are analysed by comparing four maps interpreted from Landsat images (MSS 1972, TM 1997, OLI 2013, and OLI 2021). The main research procedures consist of (1) data collection, (2) field survey, (3) image classification, and (4) LULC change interpretation. The classification uses the maximum likelihood algorithm, achieving an overall kappa accuracy of &gt; 75%. The classification produces eight classes, i.e., built-up land (BU), heterogeneous agricultural land (HAL), bare soil (BS), paddy fields (PF), open water (OW), vegetation (VG), shrubland (SH), and wetlands (WL). The analysis shows a significant shift in land use from 1972 to 2021, with HAL declining by 46%, SH by 91%, and BS by 88%. In contrast, PF has increased by 105%, VG (forest and plantation) by 64%, and built-up areas by a remarkable 366%. These changes show a significant shift from dryland agriculture, shrublands, and barren lands to irrigated regions, vegetated areas, and urban growth. Furthermore, there are increases in water bodies due to the construction of several reservoirs to support water availability. In coastal regions, the development of inland aquaculture leads to a proliferation of wetlands (salt evaporation ponds, fish ponds, and rice-fish integrated farming).

Landslide hazard mapping in Chefchaouen, Morocco: AHP-GIS integration

ABSTRACT Chefchaouen province, located in northern Morocco, is prone to landslides because of its geological features, climate change, and human activities. We investigated landslide dynamics in this mountainous province using a multi-criteria spatial approach and GIS, applying the Analytical Hierarchy Process (AHP). Our findings identified 1267.877 km2 as low-risk, 1695.334 km2 as moderate-risk, and 904.2071 km2 as high-risk, mainly in the northern and northwestern regions. Forests were the most susceptible at 13.83%, followed by agricultural and bare lands, with water bodies least affected. Contributing factors include deforestation, wildfires, and agricultural practices. Our results highlight the need for targeted risk mitigation and continuous land use monitoring. Strategies like slope stabilisation and reforestation can improve landslide prediction and prevention, enhancing community resilience in Chefchaouen. This study’s innovative approach and insights into landslide prediction and prevention provide valuable knowledge to the field.

Construction of Greenhouse for Horticultural Cultivation with Soil Media and Hydroponics

Theneral background greenhouse technology is increasingly recognized as an efficient solution for sustainable agricultural practices, especially in regions with limited fertile land. Specific background In Kedungpeluk Village, where the majority of residents work as fish farmers, there are also unused and arid lands. These underutilized spaces provide an opportunity for greenhouse-based horticultural cultivation using soil media and hydroponic technology. Knowledge gap however, limited knowledge and infrastructure have prevented local farmers from effectively utilizing these lands for sustainable crop production. Aims this community engagement project aims to build and utilize a greenhouse system to cultivate horticultural plants, focusing on optimizing both soil and hydroponic methods. Results the project successfully transformed barren land into productive greenhouses that facilitate the growth of tomatoes, chilies, eggplants, pak choy, and water spinach. The greenhouse structure, designed with transparent roofing, enhances sunlight exposure, promoting optimal plant growth. Both soil and hydroponic methods showed potential for increasing crop yields, with soil cultivation being more flexible but requiring extra care for pests, while hydroponics provided faster results with minimal weed and pest interference. Novelty this project introduces a dual-method approach combining traditional soil cultivation with modern hydroponic technology in a controlled greenhouse environment. Implications the findings highlight that greenhouse farming is a viable, modern agricultural solution for rural communities, offering increased productivity and quality crops, while also addressing the challenges of limited fertile land and changing climate conditions. This project serves as a model for sustainable agriculture in similar settings. Highlights: Dual-method approach: Combining soil and hydroponic techniques in a greenhouse. Efficient land use: Transforming unused arid land for productive horticulture. Controlled environment: Optimizing plant growth through sunlight, temperature, and humidity control. Keywords: Greenhouse, Horticulture, Soil Media, Hydroponics, Sustainable Agriculture

Grassland or Cropland? Land Use Dilemma and Ecological Solutions in Inner Mongolia

ABSTRACTInner Mongolia plays a critical role in both ecological conservation and food provision in China. However, some researchers have argued that focusing on and improving only one side of the equation necessarily threatens the functionality of the opposite side. To address this problem, we compared a “business‐as‐usual” scenario (BAU) with a “sustainable land use planning” scenario (SLU) constructed by simulating spatiotemporal changes in croplands and grasslands in Inner Mongolia from 2020 to 2030. Additionally, we analyzed the changes in ecosystem services and protein supply associated with changes in land use. We found that, in the BAU scenario, grasslands would decrease by 1.85% over the simulation period, while croplands would increase by 9.94%, with ecosystem services decreasing under both land uses. In contrast, land use changes over the same period in the SLU scenario are more significant, with increases of 11.33% and 2.78% in grassland and cropland, respectively, but, in this case, with ecosystem services increasing under both land uses. Moreover, protein supply increased under both scenarios, but SLU scenario can provide 33% more protein than the BAU scenario. The interconversion of cropland and grassland is the main type of land conversion in the study region, while cropland, grassland, and bare land show a triangular cycle of conversion. In addition, the implementation of scenario planning can realize multiple dividend for cultivation, livestock, and ecology in Inner Mongolia.

Land use dynamics and ecosystem service valuation in the Sanmenxia Reservoir wetland of the Yellow River

The Sanmenxia Reservoir wetland (SRW) serves as a critical ecological buffer in the middle reaches of the Yellow River. Ongoing population growth and changes in land use have placed significant pressure on the wetland’s ecosystems. However, existing research has yet to establish a spatiotemporal analysis method to assess the impact of land use change on the ecosystem service value (ESV). This gap hinders the precise regulation and sustainable development of land resources. To gain a comprehensive understanding of the land use dynamics and ecological functions of the SRW, this study introduced a refined ESV evaluation method. This method revised the equivalent factors from spatial, temporal, and hydrological perspectives, with an emphasis on the impact of sediment discharge. Furthermore, the Patch-level Land Use Simulation (PLUS) model was employed to project the land use structure in 2030, and the corresponding ESV was analyzed. The study revealed the following: (1) Cropland constituted the primary land type in the SRW. Over the last two decades, the build-up area exhibited the most significant single land use dynamics, with the transformation of bare land to cropland spanning the widest range and largest area. (2) From 2000 to 2020, the ESV increased by 0.64 billion CNY, with the water body contributing almost all the increase. (3) The projected ESV for 2030 is 2.94 billion CNY, indicating an increase in ecological functions in the near future. Policy makers should recognizethe impact of land use change on ESV and implement measures to rebalance land use structure. While ensuring the preservation of cropland and the protection of lives and property, it is advisable to consider expanding the water body area to facilitate comprehensive sustainable development within SRW.

Soil erodibility mapping using remote sensing and in situ soil data with random forest model in a mountainous catchment of Indian Himalayas.

Land degradation is accelerating in the Himalayan ecosystem, resulting in the loss of soil nutrients due to severe erosion. Soil erosion presents a significant environmental challenge, resulting in both on-site and off-site consequences, such as reduced soil productivity and siltation in reservoirs. Soil erodibility (K factor), an inherent soil property, determines the susceptibility of soils to erosion. Sampling across hilly and mountainous terrain pose challenges due to its complex landscape. Despite these challenges, it is essential to study K factor variations in different land use/land cover types to comprehend the threat of erosion. Digital soil mapping offers an opportunity to overcome this limitation by providing spatial predictions of soil properties. The objective of our study is to map the spatial distribution of soil erodibility using the Random Forest (RF) model, a machine learning method based on sampled in situ soil data and environmental covariates. We collected 556 surface soil samples from the mountainous catchment (Tehri dam catchment) using the stratified random sampling approach. The model performed satisfactorily in both training (r2 = 0.91; RMSE = 0.00185) and testing (r2 = 0.45; RMSE = 0.00318) phases. Subsequently, we generated a digital map with a resolution of 12.5m to depict the distribution of the K factor. Our analysis revealed that key environmental variables influencing the prediction of the K factor included geology, mean NDVI, and climatic factors. The average K factor value was estimated at 0.0304 and ranging from 0.0251 to 0.0400 t ha h ha-1MJ-1mm-1. A higher K factor was observed in the barren land (0.0344) primarily located in the higher and trans-Himalayan region of seasonally snow-covered areas. These areas typically feature young soils with weak soil formation and unstable soil aggregates. Subsequently cropland/cultivated soils (0.0307) exhibited higher K factor values due to the breakdown of soil aggregates by ploughing activities and exposing carbon to decomposition. The average K factor value of evergreen (0.0294) and deciduous (0.0295) forests were the lowest compared to other land use/land cover types indicating the role of forests in resisting soil erosion. By assessing and predicting soil erodibility, land planners and farmers can implement erosion control measures to protect soil health, prevent sedimentation in water bodies, and sustain agricultural productivity in the Himalayas.

Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application.

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District ofSri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available.Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations.