Bare Land Research Articles

Changes in Soil Organic Carbon Density and Its Response to Climate Change and Human Activities Before and After the Grain for Green Project on the Loess Plateau

Accurately assessing the changes in soil organic carbon storage （SOCS） before and after the Grain for Green Project （GFG） in the Loess Plateau （LP） and exploring the relationship between its spatial and temporal distribution and the influencing factors were important references for the development of regional recycling as well as the formulation of ecological protection policies. Based on the data of climate, human activities, and SOCD in the surface （0-20 cm） and deep （0-100 cm） soil before and after GFG in the LP from 2001 to 2020, we investigated the changes in SOCD at different spatial and temporal scales by using the methods of trend analysis, the kriging method, and variance partitioning analysis. The results showed that： ① Before and after the GFG, the surface SOCS of the whole region increased by 8 338.7×104 t； the deep SOCS increased by 1 160.02×104 t. ② In each bioclimatic subregion, the whole-region average SOCD of Ⅰ （Semi-Humid Forest Region）, Ⅱ （Semi-Humid Semi-Arid Forest and Grassland Region）, and Ⅲ （Semi-Arid Typical Grassland Region） showed a significant increasing trend, with a decreasing trend in Ⅳ （arid semi-arid desert grassland area） and Ⅴ （arid desert area）. ③ The average surface SOCS increase in different ecosystems was ranked as follows： cropland > grassland > woodland > shrubs > bare land and sparse vegetation. The deep soil increase was ranked as follows： grassland > cropland > woodland > shrubs > bare land and sparse vegetation. ④ Climate factors were the most important driving factors for changes in SOCD； the annual average temperature and precipitation were significantly positively correlated with changes in SOCD. The results of the study could provide data support for regional ecological management and land use policy formulation to promote high quality development of the ecological environment in the LP.

Assessing tropical cyclone impacts in coastal Bangladesh: A change detection analysis on cyclone Bulbul using geospatial analysis and remote sensing techniques

The coastal regions of Bangladesh are highly susceptible to the impacts of tropical cyclones, which frequently pose substantial challenges for disaster management. This research aimed to analyze the immediate consequences of significant catastrophic disasters imposed on coastal regions using geospatial techniques. Cloud-free satellite images and a support vector machine learning algorithm were utilized to examine the outcomes of Land Use and Land Cover (LULC) in the pre- and post-cyclone phase for assessing disaster impact. This study found that after the cyclone Bulbul, 45.08 km2 of land features were changed. It also showed agriculture, bare land, dense vegetation, and settlement areas have decreased by about 4.75 km2, 5.55 km2, 4.56 km2, and 7.72 km2, respectively. Instead, the body of water grew at a terrible rate of 22.49 km2 in areas turned into water body areas. Cyclone made a considerable impact by increasing the waterbody in this study area by flooding, mainly the southern portion, due to the changing process of land characteristics, which increases (waterbody) and reduces (agricultural, dense vegetation, barren land, settlement areas). The accuracy of the analysis was supported by the kappa coefficient. The results underscore the vulnerability of the coastal region and emphasize the need for effective post-cyclone recovery strategies. The study hamper valuable insights into the dynamic changes in the landscape following a cyclone, shedding light on the socio-economic and environmental implications. Further research is recommended to explore the long-term impacts of cyclones on coastal ecosystems and communities, considering the changing climate patterns.

Inventorization of Spring Water Resources in Mid Hills of Himachal Pradesh, India

The study on spring inventory was conducted in the mid hills of Himachal Pradesh, India where 43 springs were inventoried in the Beas catchment area. Hydrogeological characteristics, such as geology, slope, aspect, elevation, rock and soil distribution, landuse, geomorphology, and spring discharge were utilized to access the condition of springs in the study area. The study showed that 40.12% of the springs have seen a drop in water flow and compared to the previous times, the water flow has increased in 4.85% of the springs. A total of 56% of spring is located in the forest, 32% in Agriculture land, 3% in grassland, 6% in Shrubland, 2% in built up area, 1% in barren land. 65% are depression springs, 28% are fracture springs, and 7% are contact springs. 2.32% of the spring sources had discharge rate <0.05 Lmin-1. 79.06% of the springs have average discharge rate of 1-10 Lmin-1. Rest of the 11.60% of the springs have discharge rate ranged from 10-25 Lmin-1. Most of the springs are fed by annual precipitation and those located near the Beas valley are supported by river water course.

Assessment of growth, and ion uptake of plant species, Conocarpus erectus and Dodonaea viscosa, on industrial solid waste

Present study assessed the growth of two plant species and ion uptake by them grown on different proportion of industrial solid waste and garden soil. The industrial waste having high concentration of chemicals were used with garden soil at different proportion i.e. 0% (T0), 5% (T1), 10% (T2), 15% (T3) and 20% (T4). Two species namely Conocarpus erectus (alien plant) and Dodonaea viscosa (indigenous) were used as test plants in pot study. Different parameters including growth, physiology, and anatomy of plants and concentration of cations (Na+, K+, Ca2+, and Mg2+) in the plant shoot and root were measured at different time duration (initial, 1st, 2nd, 3rd and 4th month). The key objective of the study was to use these plants to establish their plantations on the barren lands where industrial solid wastes were being disposed of. C. erectus showed better growth than D. viscosa, as well as more uptake of ions. A significant increase in plant growth was observed in fourth month in T1, where plant height reached 24.5% and 46% for C. erectus and D. viscosa, respectively. At harvest, in C. erectus, no significant difference in the fresh (65–78 g) and dry weight (24–30 g) of the shoot was observed across treatments compared to the control. In D. viscosa, at the time of harvest, the fresh and dry weights of the root and shoot showed a strong, significantly decreasing pattern across T1, T2, and T3, leading to the death of the plant at T3 and T4. Further, optimum ratio of waste soil to garden soil was found as 10:90 and 20:80 to establish the plantations of D. viscosa and C. erectus, respectively in areas where such solid waste from industries are disposed. Findings can be used for the restoration of such solid waste for the sustainable management of industrial areas and their associated ecosystems.

Analysis of Land Use and Land Cover Changes and Trends in Fantalle Range Land, East Shewa Zone, Oromia Regional State, Ethiopia

This study was conducted in Fantalle Range lands in East Shewa zone of, Oromia Region, Ethiopia, to assess land use land cover changes, trends, drivers and their socioeconomics. Household surveys were conducted through simple random sampling to collect qualitative data. Qualitative data are used to investigate the causes and effects of land use and land cover changes. SPSS software (version 20) was used for data analysis, and descriptive research methods were adopted. Additionally, map processing was done using ERDAS Imagine (version 9.1) and ArcGIS (version 10.1). The land use land cover classification activity was started by obtaining Landsat images of 1972, 1990, 2000 and 2020 at different intervals from the Earth Explorer (USGS) from the Landsat 4, Landsat 5, Landsat 7 and Landsat 8, respectively. Land use land cover change (LULCC) maps are generated based on year classification. Range land, agricultural land, woody vegetation, bare land and settlement are the five main LULCC categories generated from satellite data. The findings show that in the presence of LULCC, agricultural land, settlements and bare land expand significantly, while range land and woodland show a decreasing trend. The classification results of the 1972 image show that rangeland/grazing land accounts for the largest proportion of the land in this area, accounting for 31.6%. In addition, due to various factors, the number of livestock owned in pastoral areas is also decreasing. The main cause of changes in livestock types is drought, which can cause different impacts, such as feed and water shortages and health problems. Therefore, intervention in land use manipulation is needed to maintain ecosystems and natural resources. Furthermore, rangeland policies should be developed to maintain pastoral and pastoral systems.

Capturing Snowmelt Runoff Onset Date under Different Land Cover Types Using Synthetic Aperture Radar: Case Study of Sierra Nevada Mountains, USA

Snow plays a crucial role in the global water and energy cycles, and its melting process can have a series of impacts on hydrological or climatic systems. Accurately capturing the timing of snowmelt runoff is essential for the utilization of snow resources and the early warning of snow-related disasters. A synthetic aperture radar (SAR) offers an effective means for capturing snowmelt runoff onset dates (RODs) over large areas, but its accuracy under different land cover types remains unclear. This study focuses on the Sierra Nevada Mountains and surrounding areas in the western United States. Using a total of 3117 Sentinel-1 images from 2017 to 2023, we extracted the annual ROD based on the Google Earth Engine (GEE) platform. The satellite extraction results were validated using the ROD derived from the snow water equivalent (SWE) data from 125 stations within the study area. The mean absolute errors (MAEs) for the four land cover types—tree cover, shrubland, grassland, and bare land—are 24, 18, 18, and 16 d, respectively. It indicates that vegetation significantly influences the accuracy of the ROD captured from Sentinel-1 data. Furthermore, we analyze the variation trends in the ROD from 2017 to 2023. The average ROD captured by the stations shows an advancing trend under different land cover types, while that derived from Sentinel-1 data only exhibits an advancing trend in bare land areas. It indicates that vegetation leads to a delayed trend in the ROD captured by using Sentinel-1 data, opposite to the results from the stations. Meanwhile, the variation trends of the average ROD captured by both methods are not significant (p > 0.05) due to the impact of the extreme snowfall in 2023. Finally, we analyze the influence of the SWE on RODs under different land cover types. A significant correlation (p < 0.05) is observed between the SWE and ROD captured from both stations and Sentinel-1 data. An increase in the SWE causes a delay in the ROD, with a greater delay rate in vegetated areas. These findings will provide vital reference for the accurate acquisition of the ROD and water resources management in the study area.

Deep learning-based modeling of land use/land cover changes impact on land surface temperature in Greater Amman Municipality, Jordan (1980–2030)

Modeling the impacts of Land Use/Land Cover changes (LULCC) on Land Surface Temperature (LST) is crucial in understanding and managing urban heat islands, climate change, energy consumption, human health, and ecosystem dynamics. This study aimed to model past, present, and future LULCC on Land Surface Temperatures in the Greater Amman Municipality (GAM) in Jordan between 1980 and 2030. A set of maps for land cover, LST, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and topography was integrated into the Cellular Automata-Artificial Neural Network (CA-ANN) and the Long-Short-Term Model (LSTM) models to predict the LULC and LST for 2030. The results showed an expansion of urban areas in GAM from 54.13 km2 (6.6%) in 1980 to 374.1 km2 (45.3%) in 2023. However, agricultural areas decreased from 152.13 km2 (18.5%) in 1980 to 140.38 km2 (17%) in 2023, while barren lands decreased from 54.44 km2 (6.6%) in 1980 to 34.71 km2 (4.22%) in 2023. Forested areas declined from 4.58 km2 (0.56%) in 1980 to 4.35 km2 (0.53%) in 2023. Rangelands/ sparsely vegetated areas declined from 557 km2 (67.7%) in 1980 to 270.71 km2 (32.9%) in 2023. The results of modeling LST showed an increase in average LST for all land cover types, with the most significant increases evident within urban areas and Rangelands/Sparsely vegetated areas. The slightest increase in LST was within forested areas as the average LST increased from 28.42 °C in 1980 to 34.16 °C in 2023. The forecasts for the future showed a continuous increase in LST values in all land cover types. These findings highlight the impact of land surface dynamics and their impact on increasing land surface temperature, which urges the adoption of more sustainable planning policies for more livable and thermally comfortable cities.

A biodegradable multifunctional pectin–montmorillonite fertilizer coating: Controlled-release, water-retention and soil-cementation

Coated fertilizers have been widely used to improve fertility in barren land. However, improving soil structure and water-retention capacity is also essential for arid and semi-arid areas with sandy soils to promote crop growth. Most currently available coated fertilizers rarely meet these requirements, limiting their application scope. Therefore, this study “tailored” pectin–montmorillonite (PM) multifunctional coatings for arid areas, featuring intercalation reactions and nanoscale entanglement between pectin and montmorillonite via hydrogen bonding and electrostatic and van der Waals forces. Notably, PM coatings have demonstrated an effective “relay” model of action. First, the PM-50 coating could act as a “shield” to protect urea pills, increasing the mechanical strength (82.12 %). Second, this coating prolonged the release longevity of urea (<0.5 h to 15 days). Further, the remaining coating performed a water-retention function. Subsequently, the degraded coating improved the soil properties. Thus, this coating facilitated the growth of wheat seedlings in a simulated arid environment. Moreover, the cytotoxicity test, life cycle assessment, and soil biodegradation experiment showed that the PM coating exhibited minimal environmental impact. Overall, the “relay” model of PM coating overcomes the application limitations of traditional coated fertilizers and provides a sustainable strategy for developing coating materials in soil degradation areas.

Temporal variation characteristics in the association between climate and vegetation in Northwest China

Northwest China has undergone notable alterations in climate and vegetation growth in recent decades. Nevertheless, uncertainties persist concerning the response of different vegetation types to climate change and the underlying mechanisms. This study utilized the Normalized Difference Vegetation Index (NDVI) and three sets of meteorological data to investigate the interannual variations in the association between vegetation and climate (specifically precipitation and temperature) from 1982 to 2015. Several conclusions were drawn. (1) RNDVI-GP (relationship between Growing Season NDVI and precipitation) decreased significantly across all vegetation, while RNDVI-GT (relationship between Growing Season NDVI and temperature) showed an insignificant increase. (2) Trends of RNDVI-GP and RNDVI-GT exhibited great variations across various types of vegetation, with forests displaying notable downward trends in both indices. The grassland exhibited a declining trend in RNDVI-GP but an insignificant increase in RNDVI-GT, while no significant temporal changes in RNDVI-GP or RNDVI-GT were observed in the barren land. (3) The fluctuations in RNDVI-GP and RNDVI-GT closely aligned with variations in drought conditions. Specifically, in regions characterized by VPD (vapor pressure deficit) trends less than 0.02 hpa/yr, which are predominantly grasslands, a rise in SWV (soil water volume) tended to cause a reduction in RNDVI-GP but an increase in RNDVI-GT. However, a more negative trend in SWV was associated with a more negative trend in both RNDVI-GP and RNDVI-GT when the VPD trend exceeded 0.02 hPa/yr, primarily in forests. Our results underscore the variability in the relationship between climate change and vegetation across different vegetation types, as well as the role of drought in modulating these associations.

The Impact of Meteorological Drought at Different Time Scales from 1986 to 2020 on Vegetation Changes in the Shendong Mining Area

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought.

Geospatial AI solution to monitor and mitigate increasing adverse ecological and hydrological impacts of climate change in Uttarakhand Himalaya (India).

Though climate change and its adverse ecological and geohydrological impacts are being experienced across the world in all types of ecosystems but as far as the Himalaya mountain ecosystem is concerned, the rate of climate change and subsequent impacts have reached an alarming stage due to anthropogenic and technogenic intervention on natural process and now need most effective and less time taking management strategy. Addressing this burning environmental problem, a geospatial artificial intelligence (GeoAI) technique-based case study is presented here from one of the most densely populated and urbanized regions of Himalaya mountain, viz Uttarakhand Himalaya, which is also called central Himalaya. The results of the study suggest that due to quite a high rate of climate change, the climatic zones shifting towards higher altitudes at the average rate of 5.6 2m/year, causing several adverse ecological impacts in terms of decreasing quality dense temperate forest cover (0.05%/year), snow cover (0.02%/year), water bodies (0.01%/year), agricultural land (0.31%/year), and horticultural land (0.01%/year). Conversion of these eco-friendly land use land cover into barren land, fallow land, and built-up land causes geohydrological consequences of climate change in terms of decreasing rainy days (1%/year), drying perennial springs (0.20%/year), perennial streams (0.11%/year), decreasing spring and stream discharge during non-monsoon season, increased extreme rainfall events (6-8%/year), and subsequent surface runoff during monsoon season. Further, the study advocates that the degraded geohydrological process has resulted in an increased frequency of disaster events (floods, cloudbursts, landslides. etc.) with a 3% (12 events) annual rate, causing great loss of environment, infrastructure, lives, and economy each year. Therefore, it has been very urgent to mitigate climate change and increase geohydrological disaster events through an integrated approach. Keep in view this, the present study proposed an integrated watershed management plan which is equally useful to be implemented across the Himalaya region and other similar ecosystems across the world.

Spatiotemporal analysis of sand and dust emission point sources detected from satellite imagery in Syria, Jordan, and Iraq

This study utilized MODIS true color satellite imagery to analyse blowing sand and dust events dynamics in the Middle East from 2010 to 2021, focusing on Syria, Iraq, and Jordan. A total of 4923 dust point sources were detected, with a significant concentration (~90 %) located within the Tigris-Euphrates Basin (Nearest Neighbor Ratio = 0.41, р < 0.001). Land cover analysis revealed that bare land, comprising most of the study area, was the predominant source of dust emissions. Wetlands, though only constituting about 1 % of the area, showed the highest frequency of dust sources per unit area, highlighting their role as critical dust emission hotspots. The study emphasizes the impact of drought and anthropogenic factors, such as poor land management, on blowing dust intensity. It suggests the necessity of strategic land management practices, including re-vegetation of arid areas, reducing soil exposure, and implementing wind erosion control measures. To effectively address the transboundary nature of dust emissions, the findings underscore the importance of fostering regional cooperation through mechanisms such as shared environmental monitoring and data exchange platforms, joint management of cross-border natural resources, and collaborative policy making.

Monitoring of vegetation changes based on RS monitoring in Changbai Mountain Nature Reserve

High vegetation cover helps maintain the ecosystem's stability and reduces the risk of soil erosion and natural disasters. Vegetation restoration is essential for the ecological value of Changbai Mountain Nature Reserve. In this study, the interannual changes in vegetation cover in Changbai Mountain Nature Reserve, China, from 2013 to 2022 were studied in depth based on Landsat remote sensing data and land use transfer matrix. The study includes interannual dynamic changes in vegetation types, quantitative analysis of interannual changes in surface vegetation types, and the land use transfer matrix analysis. In addition, the drivers of dynamic changes in vegetation types were analysed. The research indicates a notable advancement in ecological restoration within Changbai Mountain Nature Reserve between 2013 and 2022. There was a substantial reduction in the extent of barren land, which predominantly transitioned into grass and woodland areas. In particular, the year 2022 exhibited a significant increase in grassland area when compared to 2013, with a remarkable expansion of 27.18 square kilometers, primarily concentrated in the central-eastern sector of the study area. The findings of this investigation serve as a valuable scientific foundation for guiding ecological preservation and management decisions within Changbai Mountain Nature Reserve, contributing to a deeper comprehension of the evolving patterns in vegetation cover.

Effects of disturbances on the spatiotemporal patterns and dynamics of coastal wetland vegetation

The coastal wetlands represented by mangroves are vulnerable to disturbances. To clarify the effects of disturbances quantitatively on mangroves, spatiotemporal changes in land use and land cover (LULC) were compared between the inside and outside of mangroves during 1988 and 2020 in Bangladesh by remote sensing. The mangrove region, which had diverse surface greenness due to natural and anthropogenic disturbances, were surveyed. Changes in greenness were estimated by five vegetation indices (VIs). To increase in the accuracy of land use classification, the five VIs were combined under a random forest (RF) classifier. The five VIs were: normalized difference vegetation index (NDVI), green NDVI (GNDVI), soil-adjusted VI (SAVI), green–red VI (GRVI) and enhanced VI (EVI), using data obtained from Landsat TM (30 m resolution) and Sentinel-2A (10 m) via Google Earth Engine. A Moran’s I analysis showed a random pattern of training samples of LULC classes for integrated RF classification. The classification accuracy was evaluated using the reference point data with multiple comparisons of VIs and κ coefficient. The area was classified into eight LULC classes. Although the respective VIs depicted unique characteristics, the accuracies of them were less than 89 %. The integrated classifier improved the accuracy of 96–97 % with κ of 0.96 in 1988 and 0.97 in 2020. The VI-integrated RF was the most effective classifier for LULC, particularly for separating mangroves from homestead vegetation. The changes for 32 years showed that the dense mangrove occupied 42 % of surveyed area in 1988 but declined ca 622 km2 in 2020, whereas sparse mangrove and aquaculture increased by 487 km2 and 464 km2, respectively, out of the total area of 8,360 km2. The changes in LULC classes were mostly from dense mangroves to sparse mangroves, sparse mangroves to dense mangroves and from cropland to bare land and aquaculture. Inaccessible mangrove degradation should result from natural disturbances, i.e., tropical cyclones, while areas near forest boundaries were distorted by anthropogenic disturbances such as aquaculture development. The restoration of habitat quality, mangrove afforestation and sustainable aquaculture practices were effective indicators for improving the mangrove ecosystems. Since natural and anthropogenic disturbances induce substantial loss of mangrove vegetation, the VI-integrated RF classifier should be applied for monitoring LULC and for developing the sustainable management strategies.

Balancing Environmental and Human Needs: Geographic Information System-Based Analytical Hierarchy Process Land Suitability Planning for Emerging Urban Areas in Bni Bouayach Amid Urban Transformation

Urbanization in Bni Bouayach, Morocco, threatens vital irrigated areas and agricultural land, raising concerns about environmental sustainability. This study employs a GIS-based Analytical Hierarchy Process (GIS-AHP) framework to assess land suitability for sustainable development. It addresses knowledge gaps in urban planning as follows: (i) Evaluating land suitability for sustainable development: this analysis identifies areas appropriate for urban expansion while minimizing environmental impact. (ii) Balancing environmental and human needs: the framework integrates ten criteria encompassing accessibility, economic, social, geomorphological, and environmental factors. This comprehensive approach results in a Land Suitability Map with five categories: prohibited/unfit, extremely unsuitable, moderately unsuitable, adequately suitable, and highly suitable. Notably, 39.5% of the area falls within the adequately suitable or highly suitable categories, primarily consisting of accessible bare lands and pastures. These findings provide valuable insights for policymakers to guide Bni Bouayach towards sustainable urban development, ensuring balanced growth that respects both environmental preservation and resident needs.

Urban Sprawl’s Impact on Land Use in Kenya: A Systematic Review of Literature

Urban growth is essential in bringing in new opportunities and a larger labour force, but when it is unconventional and unregulated, it tends to raise new threats and problems in the community and the environment. This urban sprawl phenomenon and its impact on land use need to be explored deeply for comprehensive planning and development. Therefore, it is important to analyse existing evidence related to urban sprawl impacts on land use. In this study, existing knowledge on the impact of urban sprawl on land use in Kenya is described through a systematic literature review. The specific objectives of the review were to assess the general characteristics of studies on urban sprawl impact on land use in Kenya, synthesise the findings of these studies, and highlight the gaps in the studies. A search was conducted on the Google Scholar engine, leading to 4317 results from which 21 articles were selected through a systematic screening process. The review established that the majority of the studies are recent, having been published in the last 10 years, have utilised the quantitative approach, and have been conducted in diverse urban centres in Kenya. The findings of the studies revealed that diminishing agricultural land is the most prominent consequence of urban sprawl. The research found that urban sprawl is taking much of the available agricultural land due to increased demand for housing and the creation of new non-farm economic activities. This phenomenon has threatened to cause food shortages, reduced agricultural exports, hence reduced foreign exchange, and lost job opportunities in the agricultural sector. Another impact of urban sprawl is the decline of conservation areas, including bare land, forests, bushlands, and wetlands. Replacement of land under natural cover with built cover has dire consequences for human life and the ecosystem, including increased cases of floods and soil erosion. The results of this study provide insights that can be used to formulate policies related to urban planning, agriculture, and conservation.

Spatio-temporal analysis land use land cover changes in South Kashmir region of North-western Himalayas using Landsat data

This paper presents a comprehensive analysis of the spatial patterns and temporal dynamics of land use and land cover changes in South Kashmir from 2000 to 2022 leveraging remote sensing technologies revealing significant transformations in various land cover classes. The study used maximum likelihood classification, a supervised classification method, to analyze Landsat satellite imagery and identify ten major land use categories. The findings demonstrate notable increases in, barren land by 18.78 km2 (0.35%), built-up areas by 72.28 km2 (1.33%), forests by 274.76 km2 (5.05%), grasslands by 68.06 km2 (1.25%), scrubland by 307.82 km2 (5.66%). horticulture experienced a significant rise of 419.17 km2 (7.70%), Conversely, several land use classes reported decline, agriculture by 757.21 km2 (13.91%), exposed rockmass by 258.58 km2 (4.75%), glaciers and snow by 136.83 km2 (2.51%), and water bodies contracted by 8.23 km2 (0.15%). The primary drivers of land use change in the region are identified as climate change, population growth, and economic factors. Climate change has altered precipitation patterns affecting agricultural productivity and leading to the retreat of glaciers. Population growth and economic reasons, including the rise of horticulture and changes in land use policies, have also played a significant role in shaping the landscape dynamics of South Kashmir. These changes underscore the dynamic nature of land use in South Kashmir, with significant implications for regional planning and environmental management. The study underscores the cost-effectiveness and efficacy of geospatial technologies in conducting spatiotemporal analyses and formulating evidence-based policies for the sustainable management of natural resources.

LULC changes in the region of the proposed Pwalugu hydropower project using GIS and remote sensing technique

&lt;p&gt;Proper understanding of LULC changes is considered an indispensable element for modeling. It is also central for planning and management activities as well as understanding the earth as a system. This study examined LULC changes in the region of the proposed Pwalugu hydropower project using remote sensing (RS) and geographic information systems (GIS) techniques. Data from the United States Geological Survey's Landsat satellite, specifically the Landsat Thematic Mapper (TM), the Enhanced Thematic Mapper (ETM), and the Operational Land Imager (OLI), were used. The Landsat 5 thematic mapper (TM) sensor data was processed for the year 1990; the Landsat 7 SLC data was processed for the year 2000; and the 2020 data was collected from Operation Land Image (OLI). Landsat images were extracted based on the years 1990, 2000, and 2020, which were used to develop three land cover maps. The region of the proposed Pwalugu hydropower project was divided into the following five primary LULC classes: settlements and barren lands; croplands; water bodies; grassland; and other areas. Within the three periods (1990–2000, 2000–2020, and 1990–2020), grassland has increased from 9%, 20%, and 40%, respectively. On the other hand, the change in the remaining four (4) classes varied. The findings suggest that population growth, changes in climate, and deforestation during this thirty-year period have been responsible for the variations in the LULC classes. The variations in the LULC changes could have a significant influence on the hydrological processes in the form of evapotranspiration, interception, and infiltration. This study will therefore assist in establishing patterns and will enable Ghana's resource managers to forecast realistic change scenarios that would be helpful for the management of the proposed Pwalugu hydropower project.&lt;/p&gt;

Pre- and Post-COVID-19 Impact on the Tourism Industry in Kashmir and Ladakh

Generally, Tourism includes all kinds of different activities which are associated with tourism industries like hotels and restaurants, tourist guides, transporters, shopkeeper etc on same way, it also include large number of small tourism related activities in different States and UTs because of their geographical areas like Ladakh and Kashmir region their mostly tourism activities are Houseboat, Shakara Safari, Gondola safari, horse riding, Trekking in mountain, Camel Safari, Jeep Safari etc. this type of tourism activities are mostly found in Ladakh and Kashmir region. Because of Barren Land and low agriculture productivity in these regions, mostly Ladakh 90% and Kashmir 60% depends on tourism industry. Tourism generates jobs income, employments; promote long-term growth, benefiting local economies in both the regions. Tourism is huge business in Ladakh and Kashmir regions. Tourism, the country's largest service industry, boosts the state's GDP, foreign exchange, employment, and tax revenue. The Ladakh and Kashmir regions are a popular tourist destination due to its successful development. Suddenly, the Covid-19 outbreak hit Ladakh and Kashmir regions just as tourist season was starting, dampening spirits. Travel and tourism have spread a novel corona virus that is highly infectious. This paper also discusses about the lockdown and has affected the tourism activities in these regions. The serious damaging effects of covid-19 are in 2020, resulting in reduced visitor numbers and travel restrictions, private employments, workers, etc in both the regions of Ladakh and Kashmir. Poorer people (tourist stakeholders) were also severely harmed; the findings show the covid-19 pandemic early effects on tourism and cost.

Classification of major species in the sericite-Artemisia desert grassland using hyperspectral images and spectral feature identification.

The species composition of and changes in grassland communities are important indices for inferring the number, quality and community succession of grasslands, and accurate monitoring is the foundation for evaluating, protecting, and utilizing grassland resources. Remote sensing technology provides a reliable and powerful approach for measuring regional terrain information, and the identification of grassland species by remote sensing will improve the quality and effectiveness of grassland monitoring. Ground hyperspectral images of a sericite-Artemisia desert grassland in different seasons were obtained with a Soc710 VP imaging spectrometer. First-order differential processing was used to calculate the characteristic parameters. Analysis of variance was used to extract the main species, namely, Seriphidium transiliense (Poljak), Ceratocarpus arenarius L., Petrosimonia sibirica (Pall), bare land and the spectral characteristic parameters and vegetation indices in different seasons. On this basis, Fisher discriminant analysis was used to divide the samples into a training set and a test set at a ratio of 7:3. The spectral characteristic parameters and vegetation indices were used to identify the three main plants and bare land. The selection of parameters with significant differences (P < 0.05) between the recognition objects effectively distinguished different land features, and the identification parameters also differed due to differences in growth period and species. The overall accuracy of the recognition model established by the vegetation index decreased in the following order: June (98.87%) > September (91.53%) > April (90.37%). The overall accuracy of the recognition model established by the feature parameters decreased in the following order: September (89.77%) > June (88.48%) > April (85.98%). The recognition models based on vegetation indices in different months are superior to those based on feature parameters, with overall accuracies ranging from 1.76% to 9.40% higher. Based on hyperspectral image data, the use of vegetation indices as identification parameters can enable the identification of the main plants in sericite-Artemisia desert grassland, providing a basis for further quantitative classification of the species in community images.