Land Use Cover Change Analysis Research Articles

Assessing the impact of land use land cover changes on soil moisture and vegetation cover in Southern Punjab, Pakistan using multi-temporal satellite data

ABSTRACT Change in land use land cover (LULC) is driven by human activities and drives changes that limit the accessibility of services and products for livestock and humans. This study aimed to develop the spatio-temporal changes in LULC, vegetation cover, and moisture index using multi-temporal satellite data in Southern Punjab, Pakistan. Analysis of satellite data was accomplished using ArcGIS 10.4 software. Based on ground-truthing, the supervised classification technique (maximum likelihood algorithm) was used to achieve the LULC classification. The LULC change analysis revealed that vegetation area converted to the build-up area by 7.17 % and bare soil converted to urban areas by 1.68 % from 2000 to 2021. Average NDVI values were calculated at 0.23, 0.17, 0.19, and 0.14 for 2000, 2007, 2014, and 2021, respectively. In the study area, average NDMI values were observed at 0.28, 0.25, 0.2, and 0.15 for 2000, 2007, 2014, and 2021, respectively. Based on our study, the general trend in Southern Punjab is a decrease in vegetation cover, moisture index, and forest area due to an increase in build-up areas. Assessments of LULC and NDMI changes, as well as estimates of the effect on the environment, are necessary for many policy decisions and planning.

Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

Gelephu, located in the Himalayan region, has undergone significant development activities due to its suitable topography and geographic location. This has led to rapid urbanization in recent years. Assessing land use land cover (LULC) dynamics and Normalized Difference Vegetation Index (NDVI) can provide important information about urbanization trends and changes in vegetation health, respectively. The use of Geographic Information Systems (GIS) and Remote Sensing (RS) techniques based on various satellite products offers a unique opportunity to analyze these changes at a local scale. Exploring Bhutan’s mandate to maintain 60% forest cover and analyzing LULC transitions and vegetation changes using Sentinel-2 satellite imagery at 10 m resolution can provide important insights into potential future impacts. To examine these, we first performed LULC mapping for Gelephu for 2016 and 2023 using a Random Forest (RF) classifier and identified LULC changes. Second, the study assessed the dynamics of vegetation change within the study area by analysing the NDVI for the same period. Furthermore, the study also characterized the resulting LULC change for Gelephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentinel-2 satellite data, which was then used for pre-and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Gelephu and Gelephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Gelephu between 2016 and 2023. The results serve as a basis for strategy adaption required to examine the environmental protection and sustainable development management, and the policy interventions to minimize and balance the ecosystem, taking into account urban landscape.

Implications of uncontrolled water withdrawal and climate change on the water supply and demand gap in the Lake Tana sub-basin

This study evaluates the implications of multiple climatic and non-climatic factors on the water resource of the Lake Tana sub-basin, Ethiopia. The study focuses on three drivers: land use change, irrigation expansion, and climate change (CC), and their impact on the current and future water availability across the sub-basin. The study uses a random forest (RF) machine learning classifier in the Google Earth Engine (GEE) platform to detect land use land cover change (LULC) and for mapping the actual irrigated-area. The Climate Hazards Group InfraRed Precipitation and temperature data were used with station data to evaluate the implication of CC on the water availability of Lake Tana sub-basin. The supply-demand relationship was done for Gumara catchment within Tana sub-basin as an experimental site. Based on the LULC analysis, plantation, cropland, bare lands, built-up, and wetland showed an increasing trend, while forest, bush land, and grassland showed a decreasing trend. The increasing rate of crop land in the expense of natural forest, shrub land, grassland may cause runoff/flood, high soil erosion, and lower rate of groundwater recharge. The actual irrigated area mapping analysis shows a widespread irrigation near the Lake Shore and upstream parts of the sub-basin. The climate change analysis shows an increasing trend of potential evapotranspiration during the irrigation period attributed to increase in maximum temperature. The rising of potential evapotranspiration during the irrigation period creates a water shortage in the study and future periods as the crops need more water for growth. The estimated stream flow for Gumara catchment shows an increasing trend for the future period. The supply-demand relationship of the catchment shows an uneven distribution of water in the Gumara catchment both in the current and future periods resulting in unbalance abstraction of irrigation water. The study reveals that climate change, land cover change, and uncontrolled expansion of irrigated land area are likely to increase the unmet demand by increasing irrigation water demand, and this can cause conflict of interest between the users. Therefore, sustainable water resource management practices such as land management practices, and adaptive management of irrigation should be applicable to prevent or reduce the occurrence of conflict over the water demand.

An assessment of the geomorphodiversity and land use/cover change (LUCC) effects associated with landslides in Meghalaya, India

AbstractThe Shillong Plateau in the Eastern Himalayas is exposed to high seismicity, neo‐tectonic activity and orographic precipitation that aggravated landslides in the region by affecting hillslope processes. Landscape development is influenced by landslide phenomenon and depends upon geomorphic processes and geomorphodiversity (GmD). It is linked intimately with geomorphology, hydrology and soil type. This study uses a remote sensing and GIS‐based approach to assess the spatial linkage between GmD and landslide distribution in Meghalaya (India). GmD index (GmDI) is developed using geological, morphometric and geomorphological parameters and analysed with landslide distribution. The landslide distribution considering land use/cover change (LUCC) is also examined. The result shows that the region with high‐relief, valley and depression zone, mainly along the Dauki fault, have a high to a very high GmDI. About 61% of landslide localities are in zones of high GmDI, concentrated in the central and eastern parts of Meghalaya. Density analysis of the very high class (class 5) of GmD confirms the same. Most of these landslides occur in dense and light‐vegetated landmasses. However, the LUCC analysis reveals a significant increase in the built area on steeper slopes (average expansion ratio of 2.30 for slopes >15°) between 2017 and 2022, indicating a rising threat of landslides in urban areas of Meghalaya. The study presents the importance of GmD in active tectonic regions with particular reference to landslide potential and recent LUCC. It can aid decision‐makers in planning sustainable developments and risk mitigation strategies for landslide hazards and geoconservation.

Spatiotemporal Patterns in Land Use/Land Cover Observed by Fusion of Multi-Source Fine-Resolution Data in West Africa

Land Use/Land Cover (LULC) change is a major global concern and a topic of scientific debate. In West Africa, the key trend among the changes of the past few years is the loss of natural vegetation related to changes in different LULC categories, e.g., water bodies, wetland, and bare soil. However, not all detected changes in these LULC categories are relevant for LULC change management intervention in a resource-constrained continent, as a massive change in the dominant LULC types may be due to errors in the LULC maps. Previous LULC change analysis detected large discrepancies in the existing LULC maps in Africa. Here, we applied an open and synergistic framework to update and improve the existing LULC maps for West Africa at five-year intervals from 1990 to 2020—updating them to a finer spatial resolution of 30 m. Next, we detected spatial–temporal patterns in past and present LULC changes with the intensity analysis framework, focusing on the following periods: 1990–2000, 2000–2010, and 2010–2020. A faster annual rate of overall transition was detected in 1990–2000 and 2010–2020 than in 2000–2010. We observed consistent increases in shrubland and grassland in all of the periods, which confirms the observed re-greening of rangeland in West Africa. By contrast, forestland areas experienced consistent decreases over the entire period, indicating deforestation and degradation. We observed a net loss for cropland in the drought period and net gains in the subsequent periods. The settlement category also gained actively in all periods. Net losses of wetland and bare land categories were also observed in all of the periods. We observed net gains in water bodies in the 1990–2000 period and net losses in the 2010–2020 period. We highlighted the active forestland losses as systematic and issued a clarion call for an intervention. The simultaneous active gross loss and gain intensity of cropland raises food security concerns and should act as an early warning sign to policy makers that the food security of marginal geographic locations is under threat, despite the massive expansion of cropland observed in this study area. Instead of focusing on the dynamics of all the LULC categories that may be irrelevant, the intensity analysis framework was vital in identifying the settlement category relevant for LULC change management intervention in West Africa, as well as a cost-effective LULC change management approach.

A multi-layer perceptron–Markov chain based LULC change analysis and prediction using remote sensing data in Prayagraj district, India

Land use land cover (LULC) mapping and temporal observations are indispensable drivers for sustainable development. This research showed the growth trends and land use transition for the Prayagraj district in the last three decades. Supervised classification of Landsat images was performed on 5-year temporal intervals using a maximum likelihood classifier. All satellite images were organized into six major LULC feature classes viz agriculture/open land, barren land, built-up, forest, sand, and water. The overall accuracy of LULC classification was achieved by more than 89% in all seven temporal points. Furthermore, the accuracy of the classified maps was estimated through area-based error matrix. The Land Change Modeler tool of TerrSet 2020 software was used to analyze the transition of classes and to incorporate the multi-layer perceptron-Markov chain (MLP-MC) technique. The transition potentials were included in MLP-MC with the help of sensitive explanatory variables and significant transitions of classes. Furthermore, these transition potentials and the Markov chain transition matrix were used to predict the future LULC dynamics and vulnerability. The change analysis revealed that a significant portion of the agriculture/open land gradually decreased and got converted to built-up land. The results depicted that agriculture/open land was reduced by 8.03% in the last three decades while the built-up region was grown by 199.61%. Forest area was continuously decreasing while the sand area increased due to river meandering. Overall, more than 75% of accuracy was achieved in MLP. The prediction model was first validated with observed data, and then the LULC scenario of 2035 and 2050 was simulated. LULC of 2050 showed that the built-up area would likely reach 13.90% of district area whereas the forest area would remain only 0.79%. The prediction model has given the output in the form of future LULC map along with projected potential transition maps. This would be useful for sustainable urban planning to deal with the alarming rate of built-up growthand agriculture/open landshrinkage.

Integration of LULC change/prediction and hydrological modeler for assessment of the effect of LULC Change on peak discharge in Sari Watershed, Sumbawa Island, Indonesia

Flood is the main hazard that frequently occurs in Sari Watershed, Sumbawa Island, West Nusa Tenggara Province, Indonesia. The Land Use Land Cover (LULC) change, especially from the forest into non-forest coverage, may have contributed to the increased surface runoff and flooding frequency downstream. Because of the contradicting effects that these processes have on the risk of flooding, it is difficult to predict what the long-term impact of LULC changes will be on flooding in Sari Watershed. This study evaluated the effect of LULC change and prediction on peak discharge in the Sari watershed from 1990 to 2030. LULC maps of 1990, 1995, 2000, 2005, 2010, 2015, and 2020 were obtained by Landsat images classification, whereas 2025 and 2030 were from prediction. LULC change and prediction analysis used the ANN (Artificial Neural network)-CA (Cellular Automata) Markov model. Peak discharge for each year was calculated using the HEC-HMS model. The relationship between LULC change and peak discharge was analyzed with correlation analysis. The result showed that the trend of peak discharge dynamic was consistent with the tendencies of LULC change, particularly from forest coverage to non-forest coverage. Based on correlation analysis, the peak discharge dynamic correlated with the shift in LULC during the analysis period. The study result may provide a reference for flood risk reduction and LULC planning in Sari Watershed.

Understanding the Linkage between Urban Growth and Land Surface Temperature—A Case Study of Bangalore City, India

Planning for a sustainable future involves understanding the past and present problems associated with urban centers. Rapid urbanization has caused significant adverse impacts on the environment and natural resources. In cities, one such impact is the unsettling urban growth, resulting in the urban heat island (UHI) effect, which causes considerable positive feedback in the climate system. It can be assessed by investigating the relationships between urban Land Use/Land Cover (LULC) changes and changes in land surface temperature. This study links the urban transformations in Bangalore, India, between 2001 and 2021, with the city’s changing average land surface temperatures. LULC classification was performed on Landsat satellite images for the years 2001, 2011, and 2021, using the support vector machine (SVM) classification algorithm. LULC change analysis revealed an increase in the built-up area coinciding with a decreasing trend of water bodies, vegetation, and the area under the others (wasteland/open land/barren land) category. The results show that built-up increased from 462.49 km2 to 867.73 km2, vegetation decreased from 799.4 km2 to 485.72 km2, and waterbody declined from 34.28 km2 to 24.69 km2 in 20 years. The impact of urbanization was evident in Bangalore’s land temperature changes between 2001 and 2021, showing the average temperature increased by 0.34 °C per year between the highest UHI events, contrary to 0.14 °C per year in non-urbanized areas. It is hoped that the results of this study can help the urban planners of Bangalore city identify critical areas where improvement in urban dwelling could be planned sustainably according to the global smart cities concept, an offshoot concept of the Sustainable Development Goal (SDG)-11.

Conflict of Land Use Types over Geomorphological Space of Anambra State, Southeastern Nigeria: The Analyses and Predictions

Geomorphological constraints have led to the scarcity of lands in most urban and newly urbanizing areas by reducing land that would have been available for urban expansion. This study aims at looking at the conflict among various types of land related activities over geomorphological space of Anambra State. The data used for this study include primary and secondary data. A mixed-method approach was applied in the analysis and presentation of data, and ArcGIS 10.2 Geographic Information System (GIS) software was also used for the processing of the satellite imageries to determine the rate of land use cover change (LUCC) in the State. Conclusively, people for long are not living in the active flood plains of Idemili-Niger area and other agriculturally endowed lands, but presently, due to increase in population and pressure on land, people have now started buying and sand filling lands in these areas without considering the danger their actions may cause if there is soil failure. Mixed land use type is visibly observed, especially urban related land use like commercial and other socio-cultural land uses. The LUCC analysis shows that there is a reduction in vegetated land and disturbed vegetation, while settlements increased within a period of thirty (30) years. Presently, Anambra State is almost attaining equilibrium in all land use types in the State and if nothing is done to check the trend, there may no longer be available space for vegetation or farmland in the State in the next thirty (30) years.

Land use/land cover dynamics study and prediction in jaipur city using CA markov model integrated with road network

Land Use Land Cover (LULC) dynamics analysis is critical and should be done regularly. It draws attention to LULC developments that can be addressed before they become unmanageable disasters or circumstances. For the years 2000, 2010, and 2020, LULC change analysis was carried out in Jaipur City, Rajasthan, India. The LULC maps were created using Landsat data through a visual interpretation technique at a scale of 1:50,000. These maps were classified into vegetation, agriculture, built-up areas, barren land, and water bodies. LULC was predicted by extrapolating the current LULC change pattern. Using a Cellular Automata-Markov Chain Model (CA Markov) integrated with road network, the current LULC change trend was extrapolated and utilized to estimate the LULC map for the years 2020, 2030, 2040, and 2050. The strategy was validated by estimating LULC change for 2020 and comparing it to the actual LULC map for that year. The urban area contributed to 4. 75% in 2000 of the total area in Jaipur city. The percentage of area under urban class has increased to 9.68% in 2010 and 12.96% in 2020. The prediction based on 2000–2010 and 2010–2020 has shown an unprecedented decadal growth in the built-up area till 2050. The prediction based on the 2000–2010 period has shown a rise of 92.04% during 2020–2030, 77.13% during 2030–2040 and, 64.34% during 2040–2050. The prediction based on the 2010–2020 period has shown a rise of 102.42% during 2020–2030, 73.56% during 2030–2040 and, 54.47% during 2040–2050. This study is, therefore, calls for policy interventions to manage population and urban growth.

Land Use/Land Cover (LULC) Dynamics in a Semi-Arid Watershed in Eastern Rajasthan, India Using Geospatial Tools

Land use/land cover (LULC) change analysis has become a unique approach in determining the extent of degradation of natural resources within a given period of time. Remote sensing and GIS techniques have proved to be efficient tools for mapping and analyzing LULC changes over the last few decades. LULC change analysis has been carried out in Ruparel watershed which is situated in Alwar district, Eastern Rajasthan, India, based on visual image interpretation and change detection analysis of multi-temporal satellite data pertaining to IRS-P6 LISS III data of 2004 (Path-Row 95:52), IRS-P6 LISS III of 2014 (Path-Row 95:52) and IRS-R2A LISS III data of 2021. Visual image interpretation led to the delineation of 13 LULC classes using ArcGIS 10.5 software and include categories such as cultivated land, fallow land dense forest, open forest, degraded forest, open scrub, gullied/ravenous land, settlement/built-up land, River/waterbody, dry waterbody/dry river, plantation, barren/rocky/stony waste, and stone quarry. Results of the analysis depict significant LULC changes that have taken place in the area from 2004 to 2021. LULC categories such as cultivated land and settlement/built-up land have reported major changes in terms of their increase with 56.42 km2 (4.63%) and 31.9 km2 (2.63%) respectively primarily because of an increase in population. Likewise, the dense forest has reported a decrease of 33.78 km2 (2.78%) in its area and has been converted into degraded forest i.e., 32.04 km2 (2.64%) and open forest 2.85 km2 (0.24%) due to increased human exploitation of forest resources and mining activities taking place within the forested area. The study area needs the immediate attention of policymakers and stakeholders as the study area being part of the National Capital Region (NCR) will see excessive in-migration of the population in coming years which will further deplete the precious resources in the area.

基于Google Earth Engine平台与复杂网络的黄河流域土地利用/覆被变化分析

PDF HTML阅读 XML下载 导出引用 引用提醒 基于Google Earth Engine平台与复杂网络的黄河流域土地利用/覆被变化分析 DOI: 10.5846/stxb202011132938 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学面上基金项目（42071144，41771118）；国家重点研发计划重点专项（2016YFC0501601）；中央高校基本科研业务费专项资金（GK202003060） Land use/cover change in the Yellow River Basin based on Google Earth Engine and complex network Author: Affiliation: Fund Project: the National Natural Science Foundation of China (42071144, 41771118); the National Key Research and Development Program of China (2016YFC0501601); and the Fundamental Research Funds for the Central Universities, Shaanxi Normal University (GK202003060). 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:黄河流域在中国社会经济发展和生态安全方面的地位十分重要，过去几十年的自然演变和人类活动对其造成了深远影响，系统科学地认识黄河流域的环境格局变化是实现黄河流域高质量发展的重要前提。基于Google Earth Engine平台和复杂网络分析方法，解译并分析了1986年至2018年黄河流域的连续年度土地利用/覆被变化（LUCC）。基于1000个独立验证点进行的评估显示解译的覆盖黄河流域33年的年度土地利用/覆被数据集的7个一级类的总体准确率为82.6%，15个二级类的总体准确率为74.7%。分析结果显示黄河流域的土地系统在整个研究期间呈现出复杂的时空变化，主要的LUCC模式包括：不变或很小的变化、伴随耕地流失的城市扩张、草地恢复、果园和梯田扩张和森林增加。基于复杂网络方法对黄河流域土地系统的分析表明，土地利用/覆被转移网络中的高覆盖草地、低植被覆盖地表和落叶常绿混交林与其他地类的转移较频繁，地类节点的中介中心性和度值较高，是黄河流域土地系统中的关键地类。另外与人类活动关系密切的果园和梯田、谷物耕地和城市及建设用地节点组成了较为活跃的网络社区结构，它们都有较高的结构多样性、接近中心性，这些地类之间的转移是土地系统中重要的地类转移类型。时间序列的转移网络分析表明黄河流域的土地系统在1993-1998年、2001-2007年、2011-2014年存在三个不稳定时期，这三个不稳定时期的出现可能分别由大量的农业土地开发、退耕还林还草工程和果园梯田的大量增加导致。对黄河流域的长时间土地利用/覆被动态的系统性分析提供了关于环境政策和社会经济活动对土地系统造成何种影响的见解，表明黄河流域正朝着实现农民生计安全，区域经济增长和生态环境保护的可持续发展的目标迈进，并为LUCC研究提供了新的数据处理方式和新的系统科学视角，有助于实现区域的高质量可持续发展和理解人与自然系统之间的相互作用关系。 Abstract:The Yellow River Basin plays an important role in China's economic and social development and ecological security. The natural evolution and human activities in past decades had a profound impact on the Basin. A systematic and scientific understanding of the environmental changes in the Yellow River Basin is an important premise to achieve the high-quality development of the Basin. Using landsat imagery (17080 scenes) and the classification and regression trees algorithm, mapped and analyzed the annual continuous land use/cover change (LUCC) of the Yellow River Basin based on the Google Earth Engine platform and complex network analysis methods, and provided a set of 90 m resolution continuous annual land use/cover maps from 1986 to 2018. The assessment based on 1000 independent validation points collected in Google Earth indicated that these maps achieved an overall accuracy of 82.6% for 7 first-degree classes and 74.7% for 15 second-degree classes. The analysis of the land system showed that the Yellow River Basin presented complex temporal and spatial changes during the study period. The main LUCC patterns included no change or little change, urban expansion with croplands loss, grasslands restoration, orchard and terrace expansion, and forests growth. In the land use/cover transfer network, high coverage grasslands, low-vegetated lands, and the mixed forests were the key land class nodes in the land system of the Yellow River Basin, with high degrees and frequent transfers with other classes. In addition, the nodes of orchard and terrace, croplands, and urban and built-up all had high diversity and closeness and form one relatively active network community structure. The transfers between them were more important land transfer types in the land system. Time series transfer network analysis showed that the land system of the Yellow River Basin had three unstable periods in 1993-1998, 2001-2007, and 2011-2014 with lower average shortest-path length and higher network density and network transitivity. The possible reasons for these three periods are the reclamation of a large amount of agricultural land, the implementation of the Grain for Green program, and the large increase in orchard and terrace, respectively. Based on the data and analysis results, we can further understand the role of the land system in the mutual feedback between society and the environment. The results of LUCC analysis revealed the land use/cover dynamics caused by frequent human activities in the Yellow River Basin, provided insights on the impact of environmental policies and socio-economic activities on the land system, which showed that the Yellow River is moving towards achieving the sustainable development goals of livelihood security, economic growth, and ecological protection. Research provided new data processing methods and new system science perspectives for LUCC analysis, which can help to achieve high-quality regional development and to understand the interaction between human and natural systems. 参考文献 相似文献 引证文献

Assessing the Impact of Land Use and Land Cover Change on Air Quality in East Baton Rouge—Louisiana Using Earth Observation Techniques

There has been significant research in recent decades on Land use Land cover (LULC) changes and their influence on biodiversity but little to no research on its impact on air quality. This research seeks to demonstrate how geospatial technologies such as geographic information system (GIS) and remote sensing can be used to assess the effects of LULC changes on particulate matter emissions and their impact on air quality in the East Baton Rouge area. In pursuit of these objectives, this study uses LANDSAT imageries from the past 30 years specifically Landsat Thematic Mapper (TM C2L2) and Landsat 8 Operational Land Imager/Thermal Infrared (OLI/TIRS C2L2) covering 1991, 2001, 2011 and 2021 were collected, processed, and analyzed for the LULC change analysis using QGIS software. Additionally, Sentinel 5P and the Air quality index from the U.S. Environmental Protection Agency (EPA) were used to assess the air quality trend over the years to establish the correlation between LULC and air quality. Results showed an increasing trend in air quality over the past 3 decades with concentrations of CO, NO2, and PM2.5 abruptly falling however, urbanization and the population expanded throughout the time. The paper concludes by outlining a policy recommendation in the form of encouraging Louisiana residents to use alternative renewable energies rather than the over-dependence on coal-fired electric generating plants that have an impact on the environment.

Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis

Peatlands in Indonesia are subject to subsidence in recent years, resulting in significant soil organic carbon loss. Their degradation is responsible for several environmental issues; however, understanding the causes of peatland subsidence is of prime concern for implementing mitigation measures. Here, we employed time-series Small BAseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) using ALOS PALSAR-2 images to assess the relationship between subsidence rates and land use/land cover (LULC) change (including drainage periods) derived from decadal Landsat data (1972–2019). Overall, the study area subsided with a mean rate of −2.646 ± 1.839 cm/year in 2018–2019. The subsidence rates slowed over time, with significant subsidence decreases in peatlands after being drained for 9 years. We found that the long-time persistence of vegetated areas leads to subsidence deceleration. The relatively lower subsidence rates are in areas that changed to rubber/mixed plantations. Further, the potential of subsidence prediction was assessed using Random Forest (RF) regression based on LULC change, distance from peat edge, and elevation. With an R2 of 0.532 (RMSE = 0.594 cm/year), this machine learning method potentially enlarges the spatial coverage of InSAR method for the higher frequency SAR data (such as Sentinel-1) that mainly have limited coverage due to decorrelation in vegetated areas. According to feature importance in the RF model, the contribution of LULC change (including drainage period) to the subsidence model is comparable with distance from peat edge and elevation. Other uncertainties are from unexplained factors related to drainage and peat condition, which need to be accounted for as well. This work shows the significance of decadal LULC change analysis to supplement InSAR measurement in tropical peatland subsidence monitoring programs.

Analysis of the Future Land Use Land Cover Changes in the Gaborone Dam Catchment Using CA-Markov Model: Implications on Water Resources

Land use/land cover (LULC) changes have been observed in the Gaborone dam catchment since the 1980s. A comprehensive analysis of future LULC changes is therefore necessary for the purposes of future land use and water resource planning and management. Recent advances in geospatial modelling techniques and the availability of remotely sensed data have become central to the monitoring and assessment of both past and future environmental changes. This study employed the cellular automata and Markov chain (CA-Markov) model combinations to simulate future LULC changes in the Gaborone dam catchment. Classified Landsat images from 1984, 1995, 2005 and 2015 were used to simulate the likely LULCs in 2015 and 2035. Model validation compared the simulated and observed LULCs of 2015 and showed a high level of agreement with Kappa variation estimates of Kno (0.82), Kloc (0.82) and Kstandard (0.76). Simulation results indicated a projected increase of 26.09%, 65.65% and 55.78% in cropland, built-up and bare land categories between 2015 and 2035, respectively. Reductions of 16.03%, 28.76% and 21.89% in areal coverage are expected for shrubland, tree savanna and water body categories, respectively. An increase in built-up and cropland areas is anticipated in order to meet the population’s demand for residential, industry and food production, which should be taken into consideration in future plans for the sustainability of the catchment. In addition, this may lead to water quality and quantity (both surface and groundwater) deterioration in the catchment. Moreover, water body reductions may contribute to water shortages and exacerbate droughts in an already water-stressed catchment. The loss of vegetal cover and an increase in built-up areas may result in increased runoff incidents, leading to flash floods. The output of the study provides useful information for land use planners and water resource managers to make better decisions in improving future land use policies and formulating catchment management strategies within the framework of sustainable land use planning and water resource management.

Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

Monitoring Urban Expansion And Land Use/Land Cover Changes In Banadir, Somalia Using Google Earth Engine (GEE)

Land Use Land Cover (LULC) changes in the Banadir region are rapidly changing because of the increasing interaction of human activates with the environment as the population increases. However, there is no published evidence on this phenomenon. This study used multi-temporal Landsat images (1989, 2003, and 2018) to extract and evaluate LULC changes in Banadir Somalia. Based on Google Earth Engine (GEE) platform, two Machine Learning (ML) algorithms were used and compared, using supervised classification approaches via Support Vector Machine (SVM) and the Random Forest (RF) classifiers. This study shows that the average classification accuracy of SVM is showing the highest overall accuracy of 96.7% (Kappa = 0.946), which is 1.7% higher than that achieved by RF. The analysis of LULC changes in Banadir for the last three decades revealed that the urban area increased from 43.1 km2 in 1989 to 87.7 km2 in 2018, which means that the urban expanded or increased by two folds. Vegetation land cover dropped down from 276.5 km2 in 1989 to 96.42 km2 in 2018, which is a two-thirds decrease in vegetation in the last 30 years whereas bare surface area increased from 93.74 km2 in 1989 to 229.05 km2 in 2018, which is 1.96% increase per year. This study concludes that Banadir has experienced a dramatic increase in the urban and bare surface and a decrease in vegetation for the past 30 years.

Spatial and transient modelling of land use/land cover (LULC) dynamics in a Sahelian landscape under semi-arid climate in northern Burkina Faso

Sahelian landscapes have gone through rapid changes over the past decades under high pressure from an ever-growing population, climate hazards and land degradation. Understanding drivers of change and forecasting LULC dynamics in time and space are critical for sustainable land management. This research aimed at analyzing LULC patterns and forecasting future changes in the landscape at the watershed level in semi-arid climate in northern Burkina Faso. Three LULC categories (natural vegetation, degradation soils, and cultivated areas) were considered. Seasonal vegetation dynamics were assessed through the monitoring of radiometric indices over the years 2015, 2016 and 2017. The post-harvest period (October-November) was found to be optimal for single-date LULC mapping. Furthermore, LULC maps for 1986, 1999, 2009 and 2017 were produced through a supervised classification of Landsat satellite images. For land change modelling and prediction, a Multilayer Perceptron (MLP) neural network was calibrated between 1999 and 2009 and validated against 2017 reference map, then used to simulate prediction maps for 2030 and 2050, in conjunction with population growth forecasts. Analysis of LULC changes revealed degradation soils were the major contributor to cultivated areas expansion between 1986 and 2017. Land use maps predicted for 2030 and 2050 indicated an expected increase in cultivated areas of 10.4 % and 22.7 % respectively. These results highlight the strength of the human-environment relationship in the watershed and call for careful planning and conservation actions for a sustainable balance between sustainable agricultural production and natural resources conservation.

Land use and Land Cover Change Analysis, Causes and Effects on Agricultural Productivity, Case of Anlemo District in Hadiya zone, Ethiopia

Change in land use land cover (LULC) is one of the constraints which can influence the development of sustainable agricultural in the study area. The change in LULC can negatively affect the possible use of land and completely lead to soil and vegetation degradation that have an influences on crop productivity. LULC change analysis was conducted in Anlemo District in Hadiya zone, Ethiopia for the period of 2000-2018, using Remote Sensing satellite image and Geographic Information System with field verifications. This was to look at LULC change, its causes and influences on crop productivity in Anlemo District. In this study, LULC maps of 2000, 2010 and 2018, and change maps of 2000-2010 and 2010- 2018 were produced. Results from LULC change analysis revealed an increase in agricultural land from 36.6% in 2000 to 55.764% in 2018. The increase of agricultural land was mainly at the cost of vegetation and grazing land cover change. Vegetation cover decreased from 21.81% in 2000 to 14.601% in 2018. Shrub land area was 38.81% in 2000 that decreased to 19.933% in 2018 and wetland which was 0.817% in 2000 increased to 1.057% in 2018. The study also made known that the main reasons of LULC changes were mainly, expansion of agricultural land and clearing vegetation. Therefore, these urges to initiate mechanized farming system that permits small land holder farmers to obtain more crop products from their small plot of land and to use alternative sources for fuel to reduce the complete reliance of rural community on forest products.

Landscape transformation of an urban wetland in Kashmir Himalaya, India using high-resolution remote sensing data, geospatial modeling, and ground observations over the last 5 decades (1965-2018).

Wetlands are among the most vulnerable and dynamic ecosystems of the world. Any change in the anthropogenic footprint or climate affects the health of these pristine ecologically and socioeconomically important ecosystems. In the present study, land use land cover changes (LULCC) and fragmentation of natural landscape changes in an urban wetland, Khushalsar, located in the heart of the Srinagar City, were assessed using high-resolution satellite data, geospatial modeling approach, and ground observations over the last ~ 5 decades (1965 and 2018). The spatiotemporal changes in LULC of the wetland were assessed for 3 time periods that include 1965-1980, 1980-2018, and 1965-2018. Additionally, landscape fragmentation tool (LFT) was used to quantify fragmentation of land cover. The analysis of LULCC indicated that built-up areas in the vicinity of the wetland increased by 510% between 1965 and 2018. The aquatic vegetation and marshy lands increased by 150% and 33% respectively. The area under agriculture, plantation, open water, and barren lands decreased mostly taken over by built-up areas. Within the wetland, the area under open water spread reduced by 75% while the aquatic vegetation increased by 150% from 1965 to 2018. The built-up areas including roads also showed a substantial increase. The LFT analysis revealed four categories of landscapes i.e., patch, edge, perforated, and core areas. Since the natural land cover types were taken over by land use predominantly built-up areas, the core natural areas and perforated landscapes in the Khushalsar shrunk by 34% and 94% respectively indicating fragmentation of natural environment. The lack of sewage treatment facility, reckless unplanned urbanization within and in the vicinity of the wetland, is responsible for the degradation of the Khushalsar wetland.