Patterns Of Land Cover Change Research Articles

Land cover changes in rural communities of Zimbabwe pre and post land reform era; a case of Shurugwi South constituency

ABSTRACT The study assessed land cover changes in rural communities of Zimbabwe pre and post land reform era (1980 - 2020). A mixed methods research design was adopted with the use of both qualitative and quantitative research methods. Remote sensing, questionnaires, interviews and observations were used for data collection. Microsoft Excel and ArcMap 10.5 software were adopted for data analysis. Results indicated that clearance of land for agriculture especially crop production, wood abstraction for domestic use and selling, establishment of settlements were the major drivers of change in Shurugwi South. Between 2000 and 2020, results showed that water bodies, shrub land and dense woodlands declined whilst grassland and cultivated/bare land increased. Findings indicated that the rates of increase in cultivated/bare lands and grassland were faster between 2000 and 2020. More so, generally water bodies and shrub land decreased during the 2000–2020 period. These changes indicated that land reform program and associated land reallocation in 2000 influenced the rates and pattern of land cover changes. The changes in land ownership and use rights following the land reforms affected land use patterns between 2000 and 2020. Naturally vegetated land is depleting hence the need for land resource conservation.

Responses of streamflow to forest expansion in a typical subhumid watershed under future climate conditions

Water availability in the subhumid region is highly vulnerable to frequent droughts. Water scarcity in this region has become a limiting factor for ecosystem health, human livelihood, and regional economic development. A notable pattern of land cover change in the subhumid region of the United States is the increasing forest area due to afforestation/reforestation and woody plant encroachment (WPE). Given the distinct hydrological processes and runoff generation between forests and grasslands, it is important to evaluate the impacts of forest expansion on water resources, especially under future climate conditions. In this study, we focused on a typical subhumid watershed in the United States – the Little River Watershed (LRW). Utilizing SWAT + simulations, we projected streamflow dynamics at the end of the 21st century in two climate scenarios (RCP45 and RCP85) and eleven forest expansion scenarios. In comparison to the period of 2000–2019, future climate change during 2080–2099 will increase streamflow in the Little River by 5.1% in the RCP45 but reduce streamflow significantly by 30.1% in the RCP85. Additionally, our simulations revealed a linear decline in streamflow with increasing forest coverage. If all grasslands in LRW were converted into forests, it would lead to an additional 41% reduction in streamflow. Of significant concern is Lake Thunderbird, the primary reservoir supplying drinking water to the Oklahoma City metropolitan area. Our simulation showed that if all grasslands were replaced by forests, Lake Thunderbird during 2080–2099 would experience an average of 8.6 years in the RCP45 and 9.4 years in the RCP85 with water inflow amount lower than that during the extreme drought event in 2011/2012. These findings hold crucial implications for the formulation of policies related to afforestation/reforestation and WPE management in subhumid regions, which is essential to ensuring the sustainability of water resources.

Spatio-Temporal Patterns of Land Use and Cover Change in the Lancang–Mekong River Basin during 2000–2020

Exploring the mechanisms that drive land use and cover change (LUCC) is essential for informing the formulation and implementation of effective policies aimed at optimizing land use patterns. In this study, we examined the spatial and temporal patterns of LUCC within the Lancang–Mekong River Basin (LMRB) using Globeland30 data for the years 2000, 2010, and 2020. Firstly, we analyzed the quantitative characteristics of LUCC within the LMRB in terms of the value of change and rate of change. Additionally, we investigated the converting characteristics of LUCC within the LMRB by employing land use transition matrices and land use transition probability matrices. Furthermore, we depicted the spatial distribution of LUCC within the LMRB through land use mapping and statistical analysis. The results indicate a substantial decline in forests, coupled with a notable expansion in cultivated land. Given the vital role of forests as carbon sinks, reforestation can enhance ecological services and address challenges related to climate change. Converting cultivated land to forests is an effective human intervention promoting forest transition. This study applies binary logistic models to explore the mechanisms that influence the conversion from cultivated land to forests. The results reveal that slopes ranging from 5° to 15° have the lowest probability of conversion, whereas distances between the cultivated land and the nearest tourist attraction ranging from 9 km to 18 km have the highest probability. Moreover, the conversion process is positively associated with traffic conditions and significantly influenced by human interventions. Within the study area, China, Laos, and Myanmar show a tendency to convert cultivated land into natural LULC types, while Cambodia, Thailand, and Vietnam tend to encroach on cultivated land and expand artificial surfaces. Promoting ecological restoration in the LMRB requires cooperation among these countries.

The comparison of land cover change and erosion-deposition patterns during the Early Bronze Age-I (ca. 5100-4700 cal. BP) in Eastern and Southwestern Anatolia

Land use in archaeological societies has important consequences for ecosystemic changes in the long-term, which directly affect the sustainability of the economy and socio-political organization. In the Near East; socio-political organization, mode of production, and land use patterns diversified spatio-temporally during the Early Bronze Age-I (ca. 5100–4700 cal. BP) when socially complex entities emerged across the region. During this transformative phase, Anatolia witnessed polities of varying levels of organization and modes of subsistence, all of which translate into diverse land use patterns. This research focuses on the results of agent-based modeling to simulate the long-term impacts of land use/land cover change at two major Early Bronze Age sites; Arslantepe (ca. 5100–4700 cal. BP) in Eastern Anatolia and Hacılar Büyük Höyük (ca. 5100–4900 cal. BP) in Southwest Anatolia. Arslantepe and Hacılar Büyük Höyük are situated in different environments (i.e., climate and vegetation), and show different levels of social organization, and economies. Comparing the land use patterns of these two contemporary but otherwise different polities presents important results in terms of our understanding of how land use patterns diversify and what their ecological results have been in the long term.

Multi-Scenario Land Use/Cover Change and Its Impact on Carbon Storage Based on the Coupled GMOP-PLUS-InVEST Model in the Hexi Corridor, China

Understanding the relationship between land use and carbon storage is vital for achieving sustainable development goals. However, our understanding of how carbon storage develops under land policy planning is still incomplete. In this study, a comprehensive framework that integrates Gray Multi-objective Optimization Programming (GMOP), the Patch-generating Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models is introduced to evaluate land use dynamics and ecosystem services. Two scenarios have been established to estimate Land Use and Land Cover Change (LUCC) patterns in the Hexi Corridor by 2035: the business-as-usual (BAU) scenario, developed based on historical trends, and the ecological conservation scenario (ECS), optimized with multiple policy objectives. The results show the following: (1) From 2000 to 2020, the predominant land use type in the Hexi Corridor was unutilized land, with LUCC mainly involving the transformation of unutilized land to grass land. (2) Carbon storage in the Hexi Corridor increased by approximately 9.05 × 106 t from 2000 to 2020 due to LUCC, characterized by higher levels in the south and lower levels in the north. (3) The areas of grass land and arable land are expected to continue increasing until 2035, while the extent of unutilized land is projected to decrease. The ECS is poised to create a balance between ecological protection and economic development. (4) By 2035, both the BAU scenario and ECS estimate an increase in the carbon storage of the Hexi Corridor, with the ECS expected to result in the most significant gains. These research findings provide valuable insights for administrators and researchers, guiding more rational land use planning and ecological restoration policies to achieve carbon peaking and neutrality.

Forecast of Land Cover Change as a Sustainable Flood Prevention Strategy using Cellular Automata Model in Lampoko Watershed, South Sulawesi, Indonesia

The Lampoko watershed is the biggest watershed in South Sulawesi's Barru Regency. Flooding is a common occurrence in this area, particularly tidal flooding. To get around this, this research uses a geographic information system to estimate land cover and reveal how an organization will grow in the future. This forecast is meant to help plan preventive measures in organizational regions where flooding is anticipated. The CA-Markov model was used to analyze satellite imagery data (Landsat 8 OLI TIRS Year 2015-2021). The findings revealed an increasing pattern of land cover change in several land cover classes that were hubs of communal activity including villages, fisheries (ponds) and agricultural (rice fields). Balusu, Lampoko and Ajakkang are areas that need to be prepared for since they are part of regions with a high likelihood of flooding in the future.

Dynamics of Land Cover, Development Level, and Regional Typology of Central Java Province Based on Sustainable Development Index

The high population of Central Java Province has become a factor triggering the dynamics of land cover change and regional development. Uncontrolled urban expansion and development threatens sustainability. This study aims to identify the types and patterns of land cover change, the level of regional development, the index of sustainable development in environmental, social, and economic dimensions, and the typology of regencies/cities based on the index of sustainable development. This research was conducted in Central Java Province from January to April 2023 using land cover data, village potential data, provincial statistics, and administrative maps. Land-cover changes were identified using overlay analysis in QGIS. The level of regional development was determined using the scalogram. The index of sustainable development was constructed using factor analysis, while the regional typology was identified using cluster analysis. The results showed a dynamic change in forest cover, with a significant decrease from approximately 1 million hectares to 550,000 ha from 2014 to 2019. Agricultural land and built-up areas increased by 312,000 ha and 164,000 ha, respectively. A scalogram analysis indicated an improvement in the regional development index. Sustainability analysis reveals that the sustainability index in the economic dimension is lower than that in the social and environmental dimensions. The cluster analysis indexes that approximately 60.31% of the areas had a low sustainability index. All urban areas have a high sustainability index in the economic and social dimensions, otherwise in the environmental dimension.

Identification of land cover change and spatial distribution based on slope variation in java island

The phenomenon of land cover change in Java Island is an important issue today because of its impact on the environment and human life. Rapid population growth in Java is one of the causes of land conversion. However, industrialization, urbanization and agricultural intensification are also major factors in land cover change. Java Island has land slope characteristics that vary from 0° to 30°. The large amount of land conversion that occurs in areas with steep slopes causes many landslides and floods in Java. The methodology used in this research is spatial analysis to identify land cover change in Java Island in 2000-2018 based on its land slope. Meanwhile, to determine the pattern of land cover change in Java Island, identification was carried out by dividing it into 3 periods with an interval of 6 years. Each land cover class that changes is determined by the land slope threshold with average central tendency statistical analysis. Each area of change that occurs in a class within its land slope class is averaged to get a reference value for its land slope threshold. Land cover change in Java Island was visualized in 3D based on land slope using Rstudio. The results showed that the phenomenon of land cover change on the island of Java is dominant on a gentle slope, which is around 0°-3°. Forest and wetland agriculture experienced the largest decrease in area. While the built-up area class experienced the largest area increase of 404300 Ha or 35.37%. Overall accuracy of this research is 73% of 400 sample points randomly scattered on the island of Java.

Identification of Land Cover Changes and its Impact on Biodiversity in Kalimantan Island

The land cover change that happens on Kalimantan Island has the biggest contribution to forest area reduction. High population density, clean water crisis, and land carrying capacity degradation in Java Island are among the reasons for the Indonesian National Capital City relocation from DKI Jakarta to East Kalimantan. New capital city development is inseparable from land acquisition activities which can escalate land conversion. The land cover change that is already occurring, coupled with the National Capital City (IKN) development plan, are threats to biodiversity in Kalimantan. To minimize the negative impacts, it is necessary to plan for flora and fauna conservation programs in Kalimantan. This study aims to identify land cover change patterns on biodiversity on Kalimantan and analyze suitable areas for conservation sites based on ecosystem services index, topography, and accessibility. In this study, biodiversity is represented by the ecosystem services index, while the land cover change pattern on biodiversity is represented by changes in the ecosystem services index for biodiversity. Analysis of suitable areas for conservation is determined based on several criteria, (1) very high and high class of ecosystem service indexes, (2) located at more than 9 km from arterial and collector roads, and (3) considering the adjacent grid area. The ecosystem services index in high, and moderate classes has decreased since 1990 – 2020, and land cover changes that dominate this change occur in the conversion of forest class to dryland agriculture, plantations, and other vegetation with lower ecosystem service values.

Exploring the nexus between land cover change dynamics and spatial heterogeneity of demographic trajectories in rapidly growing ecosystems of south Asian cities

Rapid urbanization is a global trend that significantly alters Land Use and Land Cover Change (LULCC) patterns. Dhaka, a megacity, and Chittagong, the country’s second-largest city, have experienced substantial shifts in LULCC patterns over the past three decades. This study aims to examine the LULCC in Dhaka and Chittagong districts of Bangladesh from 1991 to 2021, focusing on identifying patterns and quantifying the effects of population growth and urbanization. Changes in LULCC patterns were identified by analyzing Landsat satellite imagery using remote sensing and geospatial techniques. The LULCC classes were classified using the Random Forest model and an inconsistency diagram, demonstrating high precision with an overall accuracy rate exceeding 85 %. The study’s findings reveal significant transformations in LULCC patterns across Dhaka and Chittagong districts from 1991 to 2021. A notable increase in built-up areas was observed in both districts, with Dhaka division witnessing a 129 % increase in built-up areas while Chittagong district saw a 515 % increase. This was due to transforming water bodies and barren land into built-up areas. However, the study also highlighted a promising increase in vegetation cover in both districts, indicating successful ecological restoration efforts. Chittagong and Dhaka saw increments of 597.95 km2 and 252.15 km2 in vegetation cover, respectively. Despite these positive changes, the study identified decreased surface water bodies in both regions. The results show a significant positive correlation between population growth/density and the built-up class, while a significant negative correlation with the barren land was observed in both districts. The findings could potentially guide policymakers in making informed decisions regarding land use, land cover, and urban sprawl management from an ecological restoration perspective. This could contribute to achieving sustainable development goals by promoting resilience ecological restoration practices.

Assessing Land Cover Change Projections and Its Suitability with Urban Spatial Pattern Plan Using Google Earth Engine and Cellular Automata in the National Activity Centres on Sumatera Island

The national activity centres (Pusat Kegiatan Nasional – PKN) areas, especially in Sumatera Island, are closely related to the land cover change phenomenon. This study aimed to identify the land cover change projection and assess its suitability with the spatial pattern plan in the “Rencana Tata Ruang Wilayah” (RTRW) which focuses on the study area including Medan, Pekanbaru, and Padang. The integration of remote sensing data, geographic information systems (GIS), and spatial analysis techniques are used to identify and assess the patterns of land cover change over the specific period. The analysis of land cover change patterns is performed using Landsat-7 and Sentinel-2A imagery data which processed in Google Earth Engine (GEE). Furthermore, Cellular Automata and Artificial Neural Network (CA-ANN) modelling is used to identify land cover prediction using transition rules like main road as driving factor then followed by overlay analysis to assess its suitability with the spatial pattern plan. The result shows the different level of each land cover classification in sub-district level. The proportion of the land cover projection suitability in Medan at 2042 was 84,92%; in Pekanbaru at 2040 was only 58,51%; while in Padang at 2030 was 77%. This findings can be able to serve as a suggestion for stakeholders in formulating effective strategies for spatial planning evaluation and managing land cover uses, especially in several national activity centers in Sumatera.

DYNAMIQUE DOCCUPATION DES TERRES ET TRANSFORMATIONS DES ECOSYSTEMES AUTOUR DES USINES DEGRENAGE DE COTON DE LA SOFITEX DES VILLES DE BOBO DIOULASSO ET DE BANFORA AU BURKINA FASO

The study concerns the analysis of the dynamics of land occupation around the factories Bobo 1, Bobo 2 and Banfora 1, Banfora 2 at Burkina Faso. The types of land use in these areas were analyzed in order to understand the changes resulting from the industrial activities, urbanization and other factors. The tools used are Envi 5.3 for the processing of satellite images, the Excel table for the processing and production of graphics and ArcGIS 10.5 for data mapping. The observed are the conversion of agricultural land into industrial areas, urban expansion and the modification of the natural ecosystem. The areas occupied by residential areas increased on average by 615.76 hectares/year between 1986 and 2021 around the Banfora factories and by 1885.41 hectares/year between 1976 and 2021, for the Bobo-Dioulasso units. The comparison between the Bobo 1, 2 and Banfora 1, 2 factories revealed similar but also distinct patterns of land cover change. The study contributed to a better understanding of the dynamics of land use change and provided information to guide urban planning policies, industrial development strategies and environmental preservation initiatives in these two cities.

Index-Based Change Detection, Spatial Zoning and Environmental Criticality of Urban Land Cover: A Spatiotemporal Study on Gazipur Sadar Upazila of Bangladesh

Urbanization has become a comprehensive phenomenon worldwide as well as in Bangladesh. This study has tried to analyze the urban land cover change pattern of Gazipur Sadar Upazila between 2001 and 2021. This study also has made an investigation to classify the urban land cover of Gazipur Sadar Upazila based on categorical spatial zoning and analyze how urbanization affects nearby green spaces to monitor environmental criticality. Multispectral Landsat satellite imageries, population census data, air quality index, and open-ended questionnaire data were used as the operational data. Spectral indices such as NDVI, and IBI, were performed from remotely sensed satellite data. NDVI was used to extract green cover and IBI was used to extract the built-up area. Spatial zoning was done based on existing literature, expert interviews, and RS analysis. Environmental criticality was analyzed by correlating population density and air quality data with the extracted green cover. Between 2001 and 2021, the built-up area expanded by around 77.7 km2 and green cover by about 74.79 km2. Although the green cover flourished, it decreased in Tongi and Konabari. Besides, the south-western region of Gazipur experiences rapid growth in built-up areas. Three classified zones e.g., industrial and manufacturing zone, forested zone, and neutral zone were extracted from criteria-based spatial zoning. The results of green space per capita analysis showed that from 2001 to 2021, green space in the study area decreased by 10-713 m2. Environmental criticality analysis illustrated that in 2001, two regions (Tongi and Konabari) were classified as highly critical, and by 2021, this number had escalated to five (Tongi, Konabari, Kasimpur, Basan, Gaccha). Furthermore, the study uncovered that the criticality in urbanized areas is growing rapidly.

Monitoring of wetland cover changes in protected areas to trade-offs between ecological conservation and food security: A case study from the Dongting Lake, China

The United Nations' Sustainable Development Goals (SDGs) for 2030 underscore the urgency of “eradicating hunger and ensuring food security” (SDG 2: Zero Hunger) and the critical need to “cease and reverse land degradation and biodiversity decline” (SDG 15: Life on Land). The Dongting Lake area in China, serving as a significant wetland nature reserve and grain production zone, is increasingly confronted with complex, interdependent trade-offs between agricultural cultivation and the ecological and environmental preservation of wetlands (SDG 2 and 15). To analyze the trade-off between conservation and food security, it is crucial to rely on long-term, continuous monitoring of land cover changes in the lake area. However, the absence of reliable optical remote sensing has posed a significant challenge due to the prevalence of cloudy and rainy conditions. Our study begins by integrating long-term Landsat and MODIS data using the GF-SG fusion algorithm to generate 8-day NDVI data with a spatial resolution of 30 m. This analysis focuses on the areas outside the embankment of Dongting Lake (referred to as O-DTL) from 2003 to 2020. We then employ a pixel-connected sample migration method to facilitate automated sampling of land cover types and a random forest classification algorithm to analyze long-term dynamic changes. Finally, by combining meteorological and social data, we examine the temporal patterns of land cover changes in the study area and identify the underlying factors influencing them. The results indicate a trade-off between the fluctuation of cropland and forest area from 2003 to 2020. Water body areas have exhibited a significant decrease, while impervious surface areas have consistently increased annually. Impervious surface areas have experienced a substantial increase of approximately 70%, with almost 90% of this expansion resulting from the conversion of cropland. Under the premise of maintaining a stable area for food cultivation, the land consumed by urban expansion eventually encroached on the wetland water bodies of the reserve. The reduction in wetland water area in the O-DTL can be primarily attributed to agricultural cultivation, while urban expansion and urbanization have indirectly contributed to the decrease. This study provides a case in which prioritizing food security (SDG 2) takes precedence over ecological conservation (SDG 15), highlighting its significant implications for guiding subsequent development in the Dongting Lake area. This finding is crucial for minimizing unintended consequences and maximizing co-benefits between sustainable development goals.

Contrasting patterns of land-cover change between early and late successional forests in a tropical montane cloud forest area managed under the milpa system

Land cover dynamics (LCD) in areas affected by different management types are hard to predict. Such is the case of tropical montane cloud forest (TMCF) areas in Mexico. The millenary itinerant milpa involves local cyclical deforestation and natural reforestation dynamics with long-lasting fallow periods. Conversely, induced grasslands for cattle, non-traditional maize cultivation with short fallow periods, and sedentary agriculture involve persistent deforestation rates. Also, human density changes could alter LCD. Ignoring such factors may lead to equivocal conclusions. We analyzed LCD using remote sensing analyses with LANDSAT images during two periods (1993–2005, 2005–2014) in a TMCF area in Sierra Norte, Oaxaca, affected by milpas, sedentary agriculture, and grasslands. We divided the study area into six indigenous sub-regions with different human population growth rates (HPG). We identified five land-cover types: mature forests (MF) dominated by TMCF species, early successional pine forests (PF), shrubby fallows (SH), croplands (milpas and more sedentary maize croplands together), and grasslands. We show that: (a) The 1993–2005 period was more variable than the 2005–2014 period, 27 % vs. 21 % of the total area displayed transitions into another class. (b) MF area dominated (277,128 ha), increased from 1993 (64.0 %) to 2014 (67.6 %), and was not statistically associated with HPG. (c) PFs decreased from 27 % in 1993 to 15 % in 2014, and their rate of change was negatively associated with HPG. (d) The total forest area (MF + PF) decreased by 9.3 %. (f) Grasslands, SHs, and croplands fluctuated between periods and sub-regions, but none exceeded 7 % of the total area. (g) Grasslands showed the greatest increase rate, mainly at the expense of forestlands. (h) The grassland and SH increments were positively associated with HPG. The heterogeneity in LCD among periods and sub-regions reveals the relevance of considering temporal and spatial scales within the same region. The persistence of MF and the PF decrease can be ascribed to the disruption of the successional dynamics related to the decline of the traditional milpa enforced by federal and state policies at the beginning of the 21st century, the sedentarization of agriculture, local conservation areas, increases of induced pastures, and human emigration. Although Sierra Norte is an essential reservoir of TMCFs, the decline of the total forest area, particularly secondary forests, is of concern given their roles in resilience and as carbon, culture, and biodiversity reservoirs. Humid mid-elevation areas of Sierra Norte are one of the few examples of mature forest persistence and secondary forest decline.

Uncertain Spatial Pattern of Future Land Use and Land Cover Change and Its Impacts on Terrestrial Carbon Cycle Over the Arctic–Boreal Region of North America

AbstractLand use and land cover change (LULCC) represents a key process of human‐Earth system interaction and has profound impacts on terrestrial ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regional LULCC projections by integrated assessment models, such as the Global Change Analysis Model (GCAM). The uncertainty associated with the different spatial downscaling methods and its impacts on the subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on their impacts on the carbon cycle over the Arctic‐Boreal Vulnerability Experiment (ABoVE) domain, where extensive LULCC is expected. Specifically, we used the Future Land Use Simulation model (FLUS) and the Demeter model to generate 0.25° gridded LULCC data (i.e., LULCCFLUS and LULCCDemeter, respectively) with the same input of regional LULCC projections from GCAM, under both the low (i.e., SSP126) and high (i.e., SSP585) greenhouse gas emission scenarios. The two sets of downscaled LULCC were used to drive the Community Land Model version 5 and prognostically simulate the terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial‐temporal differences between LULCCFLUS and LULCCDemeter, and the spatial distributions of the needleleaf evergreen boreal tree, broadleaf deciduous boreal tree, broadleaf deciduous boreal shrub, and C3 arctic grass are particularly different under both SSP126 and SSP585. Additionally, the spatiotemporal differences are larger under SSP126 than SSP585, due to more intensive LULCC under SSP126 than SSP585 from GCAM projection. The differences in LULCC further lead to large discrepancies in the spatial patterns of projected gross primary productivity, ecosystem respiration, and net ecosystem exchange, which represent more than 79% of the contributions of future LULCC in 2100. Additionally, the difference in carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by the spatial downscaling process in future LULCC projections and carbon cycle simulations.

Habitat quality assessment and multi-scenario prediction of the Gansu-Qinghai section of the Yellow River Basin based on the FLUS-InVEST model

Research on the impact of land use change on regional habitat quality, in various future scenarios, can effectively aid planning and decision-making for sustainable development at a regional level. The study conducted its research in the Gansu-Qinghai Yellow River section and used ArcGIS and a land use transfer matrix to analyze the spatiotemporal pattern of land use and land cover changes. The study assessed the changes in habitat quality in the Gansu-Qinghai Yellow River region between 1990 and 2020, using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, by evaluating the gains and losses. Simultaneously, 15 elements of the natural economy were chosen and examined for their temporal and spatial impact on habitat quality using the random forest model and spatially weighted regression model. To forecast land use changes in the Gansu-Qinghai Yellow River section for 2030, the Future Land Use Simulation Model (FLUS) model was utilized and a series of four scenarios (cultivated land protection scenario, ecological protection scenario, natural development scenario, and rapid development scenario) were employed. The research results indicate that over 70% of the Gansu-Qinghai Yellow River is occupied by grasslands, and only a small portion of the area, about 0.22%, is developed for construction purposes. The quality of the habitat in the Gansu-Qinghai Yellow River had a minor drop between 1990 and 2020, followed by an improvement. Habitat quality changes are primarily attributed to improvements, with variations across different areas, i.e., enhanced in the east and reduced in the central and western parts. The habitat quality of the Gansu-Qinghai Yellow River has improved in all four scenarios compared to 2020, as evidenced by the decrease in low-value habitats and increase in high-value areas. The ecological protection scenario has the highest average habitat quality value. These research results can be used to support policy development and ecological restoration initiatives in the Gansu-Qinghai Yellow River.

Assessment of Land Cover Status and Change in the World and "the Belt and Road" Region from 2016 to 2020.

The assessment of land cover and changes will help to understand the temporal and spatial pattern of land cover in the world and the Belt and Road (B&R) region, and provide reference information for global sustainable development and the Belt and Road construction. In this paper, the 1 km global land cover classification maps of 2016 and 2020 with a high accuracy of 88% are mapped using the Moderate Resolution Imaging Spectroradiometer (MODIS) time series surface reflectance products. Based on the maps, the land cover status of the world and the Belt and Road region, the land cover change from 2016 to 2020, and the mutual transformation characteristics between various types, are analyzed. The research results indicate that from 2016 to 2020, the global change rates of cropland, forest, grassland, and impervious surface are 0.25%, 0.22%, 0.08% and 3.41%, respectively. In the Belt and Road region, the change rates of cropland, forest, grassland, and impervious surface are 0.42%, 0.60%, -0.55% and 2.98% respectively. The assessment results will help to clarify the spatial pattern of land cover change in the five years from 2016 to 2020, so as to provide valuable scientific information for the global realization of sustainable development goals and the construction of the B&R.

Analysis of the spatial and temporal patterns in land use land cover in Belize city from 1991 to 2021

Urban theorists and planning researchers ignore small cities with ports. Rectifying this neglect, we focus on Belize City. We assess the spatial and temporal patterns of land use and cover changes, utilizing remote sensing analysis and multiyear Landsat satellite images. Focusing on the contemporary period (1991-present), we analyze the impacts of the rapid development of the Port of Belize and its consequent effect on the expansion of the urban built environment of Belize City. In addition to analyzing land use land cover changes, we calculated key indices such as NDVI, NDBI, and LST in Belize City. Importantly, local-level clustering of differences in NDBI and NDVI values is analyzed using LISA analysis. Our results show that the urbanized area has increased in the past 30 years, with corresponding reductions in the area under forest and water. Our analyses reveal three distinctive periods of urban changes: 1991–2001, 2001–2011, and 2011–2021. From 1991 to 2001 most of the urban expansion occurred along the Belize River. Significant urban expansion from 2001 to 2011 is related to a private company managing and developing the port, accelerating construction around the port. Finally, from 2011 to 2021, the process of urban sprawl slowed, with only sporadic new construction in the suburbs, due to the master planning at the port. Nevertheless, masterplan implementation delays, numerous appeals, and weak enforcement of regulations have not curbed unplanned urbanization.

Land Cover Change Using Multi-Time Landsat Imagery with Geographic Information Techniques (Sig) in Iuphhk-Ha Forest Area, Buru Regency, Maluku Province

Development of land cover change in IUPHHK-HA PT. Gema Hutani Lestari is very important to know, so that future patterns of land cover change can be predicted so that negative land cover changes can be prevented or reduced, so that land cover changes that occur can be analyzed. This study aims to access land cover change in the IUPHHK-HA PT. Gema Hutani Lestari between 2013, 2016 and 2018. The methodology used by this study is guided classification with maximum and visual likelihood estimates, spatial data analysis, field checks and using overlays for landsat 8 data in 2013, 2016 and 2018. This study shows that land cover in IUPHHK-HA in Buru Regency, Maluku Province in 2013-2016 changed from forest to open land (69.26%) and from forest to allang-allang (29.03%) dominated by Main Swamp Forest (39.27%), Primary Dry Forest 27.08% and Secondary Swamp Forest ± 17.12%. While the smallest land cover is 0.01% pasture. The largest land cover change occurred in the Secondary Dryland Forest which was reduced ± 693,444 Ha. The most significant change when compared to the situation in 2005 was the Bush Intervention Dryland Agriculture which increased ± 190.69%. Deforestation that occurred was ± 1,122.21 ha (± 160.32 ha / year) and degradation was ± 100.19 ha (± 14.31 ha / year).