LULC Dynamics Research Articles

Building resilience: Insights into ecosystem-based integrated environmental risk reduction approaches for urban environments in coastal Bangladesh

The intricate relationship between environmental quality, ecosystem services, and human well-being underscores the importance of understanding and addressing environmental complexities. Considering these issues, this research aims to propose ecosystem-based approaches for mitigating environmental risks within the Khulna City Corporation (KCC) area, while also elucidating the connection between ecosystem services and these risks. By doing so, it indirectly addresses disaster risk reduction approaches, acknowledging the relationship between environmental degradation and increased disaster occurrences. Leveraging multi-spectral Landsat images from 2001 (Landsat-5), 2011 (Landsat-5), and 2021 (Landsat-8), the study evaluates LULC dynamics and quantifies ecosystem service values (ESVs) using the Benefit Transfer Method. Additionally, seven key indicators, including land use-based carbon emissions, elevation, LST, NDVI, NDWI, NDSI, and NDBI, were analyzed to monitor spatiotemporal changes. The weighted overlay method was employed to integrate raster data and delineate environmental risk zones driven by deteriorating ESVs in KCC. Notably, the study reveals a significant increase in highly environmental risk areas from 7 % to 34 % and a decrease in medium-risk areas from 92 % to 60 % of the total area between 2001 and 2021 due to the decline in ecosystem services resulting from unplanned rapid urbanization. Furthermore, a moderate negative correlation (R2 values of 0.56, 0.67, and 0.69) between ESVs and the environmental risk index underscores the linkage between deteriorating ecosystem services and the expansion of high-risk zones. Furthermore, this study advocates policies such as drought-resistant tree plantations, community involvement, post-disaster resource provision, funding allocation, and stakeholder knowledge dissemination to enhance the resilience of KCC.

Assessment of forest cover change, key drivers of change and perception of locals in Birendranagar Municipality, Surkhet District, Nepal

Land use and land cover change has an immense impact on the global environment and ecosystem and is mostly connected with human activities such as conversion of forest into agricultural land and settlements. Forests are changing globally in quantity and quality, and in both increasing and decreasing trends. For the Birendra Nagar Municipality, this study evaluated LULC dynamics, the conversion of forest cover from 2000 to 2020, as well as community perceptions on forest cover change, its drivers. Landsat images of 2000 and 2020 were used for LULC dynamics and for forest cover change detection Using software GIS 10.8. Questionnaire survey, key informant survey, field observation and secondary data were acquired from journal articles, reports, District Forest office, books, and various other sources to find out drivers of forest cover change and perception of locals. It was found that 1146 ha of forest area, 280.53 ha of water bodies, 752.62 ha of barren land were lost between 2000 and 2020. However, 1147.74 ha of settlement area and 1032.40 ha of agricultural land was gained.Net forest cover decreased overall by 4.67%, however, some areas within the municipality experienced an increase in forest cover namely Ward 11 and ward 16 of Municipality shows Highest positive and Negative Forest cover change in terms of Area. Expansion of agriculture land and settlements with the encroachment of forest area, haphazard infrastructure development, illegal timber harvesting and low regeneration capacity of land responsible for the reduction of forest, along with Community forestry approach, plantations programs, promotion of agroforestry practices, fuel transition were responsible drivers for improvements of forest cover. Water cycle disruption, Increases Natural hazard, Temperature rises, shortage of wood for fuel, extinction of indigenous species were the most perceived negative impacts of the forest cover reduction whereas income generation Activities, support biodiversity, easy to collect fuel wool and fodder and others including purifying air quality, increasing aesthetic beauty were the most perceived positive impacts of the forest cover improvement in the study area. Most of the locals have a holistic understanding of forest cover change. Strengthening forest protection, improving soil and water conservation structures, awareness creation, enrichment planting, Promote Alternate Energy Resources and Equitable benefit-sharing mechanism are possible solutions perceived by locals.

Analysis of Land Use/Cover Characteristics and Dynamic of Changes in Huai River Basin from 2000 to 2020

Globally, land use is considered as one of the most closely related link between human being and nature. But land use/cover change (LULC) is regarded as the main cause for global environmental change with possible serious impact on human livelihoods and many parts of social and physical environmental aspects. Huai River Basin (HRB) is among the global river-land ecosystems that has been intensively altered by land use change. Unfortunately, the increasing land use change in HRB is not well scientifically addressed and still, related existing problems are not solved putting the basin ecological status and its basic grain production agricultural capacity at risks. This study adopted data image processing software and various mathematical statistical models to analyze the current LULC characteristics and dynamics of change in the study basin. From the analysis that adopted combined scientific techniques of CA-Markov, InVEST models with ArcGIS and IDRISI software, the results indicate that in 20 years of study, area of cultivated land in HRB declined by -4.34%, grassland declined by -21.28%, forest land declined by -3.22% and unused land declined by -18.84% while area of construction land increased by 26.38% and water area increased by 11.62%. Cultivated land was the major land use type in the basin while unused land was the minor land use type. Land use projection trend of HRB in 2035 predicts decline of areas coverage on cultivated land by -6.92%, on forestland by -9.20% and on grassland by -12.29%. But the basin is expected to have area increase on construction land by 30.07%, on water area by 15.03% and on unused land by 18.45%. Pictorial summary for the analysis of land use/cover characteristics and dynamic of changes in HRB is shown below.

LULC dynamics and the effects of urban green spaces in cooling and mitigating micro-climate change and urban heat island effects: a case study in Addis Ababa City, Ethiopia

ABSTRACT Land surface temperature (LST) increase and urban heat islands (UHI) are significant issues in urban development. Urban green spaces are crucial in combating micro-climate change. This study assesses their effects on cooling and mitigating micro-climate change in Addis Ababa using remotely sensed data from Landsat 5 TM (1990), Landsat 7 ETM+ (2005), and Landsat 8 OLI/TIRS (2021). LST was retrieved using mono-window algorithms for Landsat 5 and Landsat 7, and the split-window algorithm for Landsat 8. Regression and correlation analyses of LST and Normalized Difference Vegetation Index (NDVI) were conducted using SPSS V23, STATA, and R Studio. Results showed a decrease in urban green space (UGS) from 120.4 km² in 1990 to 76.26 km² in 2021. Multiple linear regression analysis indicated that built-up areas and green vegetation accounted for 92.2% of LST variations. The cooling efficiency (CE) and threshold value (TVoE) of green space were calculated as 4.5 ± 0.5 hectares. This suggests that allocating green spaces of this size effectively reduces heat effects. Strengthening public participation in urban greening is crucial to mitigate micro-climate change, sustain urban development, and improve quality of life. Built-up areas and vegetation significantly impact LST more than surface water (NDWI), essential for urban green and land-use planning to minimize urbanization impacts.

Assessing the impact of anthropogenic activities on land use and land cover changes in the semi-arid and arid regions of Algeria.

Land use and land cover are critical factors that influence the environment and human societies. The dynamics of LULC have been constantly changing over the years, and these changes can be analyzed at different spatial and temporal scales to evaluate their impact on the natural environment. This study employs multitemporal satellite data to investigate the spatial and temporal transformations that occurred in Sidi Bel Abbes province, situated in the northwestern region of Algeria, spanning from the early 1990s to 2020. Notably, this province is marked by semi-arid and arid climates and hosts a wide range of areas susceptible to gravitational hazards, especially concerning alterations in land use and forest fires. The interactive supervised classification tool utilized multiple machine learning algorithms including Random Forest, Support Vector Machine, Classification and Regression Tree, and Naïve Bayes to produce land cover maps with six main classes: forest, shrub, agricultural, pasture, water, and built-up. The findings showed that the LULC in the research area is undergoing continuous change, particularly in the forest and agricultural lands. The forest area has decreased significantly from 10.80% in 1990 to 5.25% in 2020, mainly due to repeated fires. Agricultural land has also undergone fluctuations, with a decrease between 1990 and 2000, followed by a fast increase and near stabilization in 2020. At the same time, pasture lands and built-up areas grew steadily, increasing by 11% and 13% respectively. This research highlights the significant impact of anthropogenic activities on LULC changes in the study area and can provide valuable insights for promoting sustainable land use policies.

Remote sensing and GIS-based study of land use/cover dynamics, driving factors, and implications in southern Ethiopia, with special reference to the Legabora watershed

This paper investigates the trends, drivers, and consequences of LULC changes in Legabora watershed, Ethiopia, by utilizing remote sensing and geographic systems. Landsat Maltispectiral scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images of years 1976, 1991, 2001, and 2022, respectively, were used to study the dynamics of LULC. Essential image pre-processing steps were carefully carried out to correct distortions caused by sensor limitations. Eight main LULC categories were identified based on supervised image categorization methods and the maximum likelihood classification algorithm.The findings of change detection and cross-tabulation matrix demonstrate that there has been a significant increase in the area of cropland 345.1 ha/year, settlement 5.9 ha/year, forest 38.2 ha/year, and degraded lands 2.56 ha/year, respectively, over the period between 1976 and 2022. In contrast, considerable decreases were observed in grasslands (−248 ha/year) and shrublands (−144 ha/year), whereas other LULC categories augmented. The results revealed that the overall accuracy rates stood at 88.3 %, 88.4 %, and 85.6 % for 1976, 1991, and 2022, respectively. The overall kappa coefficient demonstrated values of 0.86 %, 0.86 %, and 0.83 % for the same period. Surveyed respondents perceived population growth, settlement, agricultural expansion, and infrastructure development as the most noticeable drivers of these LULC changes. In contrast, deforestation, land degradation, lack of livestock fodder, and biodiversity loss were identified as the main consequences of LULC changes. The factors and implications addressed in this study may be helpful tool for the formulation and implementation of evidence-based land use policies and strategies within in the study area and elsewhere.

Socioeconomic Drivers of Land Use and Land Cover Change in Western Ethiopia

A variety of socioeconomic and environmental drivers have contributed to changes in LULC around the world in recent years. This study examines the socioeconomic drivers that accelerated LULC in western Ethiopia. The data were generated from terrestrial satellite images primary and secondary sources. Primary data sources include household surveys, field observations, group discussions, interviews, key informants, and interpreting remote sensing data. Secondary data were reviewed mainly from relevant literature both published and unpublished materials. Landsat images were classified using the supervised classification technique and maximum likelihood classifier using arc GIS 10.3 to create LULC maps of the study area. Accuracy score and kappa coefficient were used to confirm the accuracy of the classified LULC, and agricultural land, settlement, bare land, forest land, and water body were the main LULC classes in the district. Forest cover in three decades (1990–2020) in the study area decreased from 12.1% in 1990 to 2.6% in 2020. The data were also analyzed using a descriptive model, Pearson correlation, and binary logistic regression. The independent variables (age and gender) show a Pearson’s positive correlation with the drivers of LULC dynamics; that is, as these independent variables increase, the drivers of LULC dynamics also increase, whereas educational status and land holding size show a negative correlation. This shows that the drivers of the anthropogenic forces of LULC dynamics decreased as the number of educated populations and the size of land holdings increased, and vice versa. Then, the binary logistic regression model examined the relationship between the dependent and the major socioeconomic (independent) variables. Logistic regression was performed to determine how independent variables and the drivers of LULC (natural forces or anthropogenic forces) change and the model was statistically significant (x2 = 23.971, df = 5, P < 0.001). The model explained 13.9% (Nagelkerke R2) of the variance in the drivers of LULC dynamics and correctly classified 66.1% of the cases. The study found that age, gender, and educational status largely determine the drivers of LULC dynamics and have the greatest chance of determining the anthropogenic forces. Therefore, relevant stakeholders should take integrated measures to reduce the drivers of LULC dynamics through landscape restoration.

Studying the Impact of Urban Management Strategies and Spatiotemporal Dynamics of LULC on Land Surface Temperature and SUHI Formation in Jeddah, Saudi Arabia

This study focuses on examining the historical trends of the surface urban heat island (SUHI) in relation to land use/cover (LULC) changes in Jeddah City from 2013 to 2022 using Landsat data. To achieve this, various parameters were analyzed, including LULC changes, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and LST. During this timeframe, Jeddah City underwent a comprehensive development initiative aimed at demolishing informal and illegal settlements and improving the overall quality of life, aligning with the Kingdom’s 2030 vision. This provides an interesting opportunity to investigate the effect of these urban transformations on the variation, presence, and intensity of SUHIs. Based on the results, a significant positive correlation between the density of built-up and LST was found, while a negative relationship was noticed between NDVI and LST for all time points. Furthermore, a remarkable observation in 2022 was the complete absence of the SUHI phenomenon following the removal of old informal settlements from the city. The outcomes of this study highlight the impact of urban development and hold the potential to uncover environmental imbalances and contribute to the development of strategies to mitigate the negative effects of LULC changes, thereby restraining the rise in LST.

Changes in Land-Cover/Land-Use Pattern in the Fortore River Basin (Southern Italy) and Morphodynamic Implications

In Southern Italy, studies dealing with the analysis of multidecadal land-use/land-cover (LULC) changes at the basin scale are scarce. This is an important gap, considering the deep interrelationships between LULC, soil erosion, and river and coastal dynamics. This study provides a contribution in filling this gap by analyzing the LULC patterns and changes in an area of southern Italy, i.e., the Fortore River basin, which occurred between 1960 and 2018. To this end, we conducted a GIS-aided comparison and analysis of LULC data from 1960, 1990, and 2018, respectively. The LULC changes were analyzed at both the basin and the physiographic unit scale. The results showed that most of the LULC changes occurred between the 1960s and 1990s, while from the 1990s onward, great stability in LULC was evident in the basin. The obtained data were mostly coherent with national-, regional-, and basin-scale trends, although some scale-dependent discrepancies were noted. The river and shoreline dynamics fully reflected the duration and the amount of phases of the changes in LULC stability at the basin scale.

LULC dynamics and application of nature based solution in high erosion prone areas of Malappuram District

Kerala State is highly vulnerable to natural disasters, mainly soil erosion due to changing climatic dynamics in the steep slope. In 2018 and 2019 flood, some districts in Kerala State were affected by significant floods due to extreme and prolonged rainfall, leads to large and small landslides. Malappuram is one of the districts that got affected in 2018 and 2019 flood. Disaster risks are augmented by a critical factor that has been silently rising in the State now, which is change in the land use pattern and practices. Hence, the Land Use and Land Cover Dynamics study was conducted in the selected watersheds (Kakkarathode – Pulikkal and Palathingal) of Malappuram district, and spotted major landslides in the area. The LULC dynamics were carried out in the different time periods like 2013, 2018 and 2020. LISS IV (5.8 m resolution) satellite images were used for the analysis and field visit, to identify the related changes. Accuracy of the classification was evaluated using error matrices and kappa statistics. The overall accuracies for 2013, 2018 and 2020 were 84.93%, 86.21% and 87.5% respectively and the corresponding Kappa values were 0.82, 0.84 and 0.85 which indicates the high accuracy of the classification. The flood has mainly affected Plantation, Paddy and Mixed Plantation which had been decreased during 2018-20 and has resulted in the emergence of more Barren land and Waste Land. LULC helps in identifying the changes in the erosion prone areas. Moreover, erosion hazardous area and its prioritization in applying the soil management and conservation practices can be effectively done using LULC change assessment. Nature based solutions such as planting trees and grasses (like shrubs, vetiver grass etc.), construction of ponds, creation of green walls and assemblage of vegetations can be adopted in the region of high-risk hazardous area depending on the categorized zone.

Patterns and controls of ecosystem service values under different land-use change scenarios in a mining-dominated basin of northern China

Exploring future land use/cover change (LULC) and ecosystem service values (ESV) dynamics is crucial for sustainable development from regional to global scales. However, ESV dynamics is significantly affected by economic development and policy changes, and future projections of ESV are generally characterized by large uncertainties due to complexity in LULC dynamics. Mining activities are supposed to exert significant effects on ESV dynamics, while the patterns and controls of ESV in mining-dominated basins are still less understood, especially under different land-use scenarios. Here, based on multi-source remote sensing data and land-use data in the Fenhe River Basin, we integrated the SD model, Markov model and PLUS model to simulate LULC in the Fenhe River Basin under four scenarios, i.e., the economic development priority (EDP), natural development priority (ND), cultivated land protection priority (CLP), and ecological protection priority (EPP). Based on ESV equivalent coefficient method and geographical detector, we further determined the patterns and controls of ESV. Results showed that construction land and cultivated land were mainly concentrated in the central parts of the basin, and grassland and woodland were concentrated in the edge area of the basin. Over the past two decades, construction land and woodland have experienced significant expansion, while the areas of grasslands and cultivated lands have declined due to mining activities. The high-value and sub-high-value areas of ESV were concentrated in woodland, grassland, reservoir and concentrated water area of mainstream and tributary. The low-value area was mainly located in the gathering area of construction land. The total ESV showed a downward trend with time, and the highest decline in the total ESV under the four land use scenarios was the EDP, while the lowest drop was the EPP. The highest contribution rate of ESV was woodland, which was equivalent to the contribution rate of cultivated land, grassland and water area, totally accounting for 58.54% of the whole ESV. The geographical detector method indicated that distance to the residential area, soil erodibility, distance to the river, soil type and annual average temperature were the main driving factors affecting the spatial patterns of ESV. Our results highlighted the fundamental role of mining activity in affecting LULC characterized by grassland and cultivated land shrinkage, and suggested that mining areas rehabilitation should be given priority in terms of further improving ESV in mining-dominated basins.

Prediction of Changes in Land Use/Land Cover and Hydrological Response in the Upper Ciliwung Watershed

As a result of rapid urbanization, in 2021, it was recorded that the forest area in the Upper Ciliwung Watershed was only a quarter of the total area. Therefore, the Upper Ciliwung Watershed cannot optimally perform its hydrological functions. One of the reasons for conducting this research is to provide consideration in calculating the adequacy of the forest area in the Upper Ciliwung watershed. In addition, this research was also conducted to estimate the discharge and volume of water, the trend of land use change in the Upper Ciliwung watershed in the last few decades, what might happen in the future if land use change continues, and how much influence it will have in maintaining forest areas that exist in minimizing the risk of future disasters occurring due to the high rate of development. This study combines remote sensing technology and hydrological modelling to understand changes in hydrological characteristics when LULC transformed from 2001 to 2051. The spatial-temporal dynamics of LULC and its hydrological response were estimated using an integrated approach that combines remote sensing, Cellular Automata-Markov Chain based on Multi-Layer Perceptron with an HEC-HMS modelling system. Multitemporal satellite imagery is used to understand LULC changes and projections for 2031 and 2051 in the Upper Ciliwung watershed. The analysis shows that there has been a very significant increase in the developed area over the years. In contrast, other types of LULC have decreased. In 2001-2051, the forest area experienced the worst decline, 22,423,493 m2. Meanwhile, the developed area experienced a significant increase of 49,771,952 m2. This changing pattern has a negative impact on hydrological characteristics. Hydrological modelling shows that the volume and discharge are projected to increase drastically to reach 258.61 x 106 m3 and 26.3 m3/s, respectively, in 2051.

Dynamics and Drivers of Land Use and Land Cover Change in the Upper Awash Basin, Central Rift Valley of Ethiopia.

This study analyzed the patterns and drivers of LULC dynamics in relation to the expansion of large-scale irrigated agriculture in the Central Rift Valley of Ethiopia from 1972 to 2016. Aerial photographs (1972), Landsat images (1980, 2000) and SPOT5 satellite images (2016) were analyzed using GIS tools to reveal LULC changes, and documentation, key informant interviews and focus group discussions were used to ascertain the biophysical and socioeconomic implications and drivers of these dynamics. The study revealed that cultivated and rural settlement land, and urban built-up areas had expanded at the expense of forestland, woodland, shrubland and grassland. While an increase in the production of cash crops had brought some benefits to smallholder farmers, such as access to irrigation and modern agricultural inputs and technologies, the unregulated conversion of natural vegetation to cultivated land resulted in a loss of biodiversity, deforestation, and reduction of pasture and firewood. We identified that significant LULC changes in the study area were caused by intersecting biophysical, economic, institutional, technological and demographic factors, which reinforced each other with varying magnitudes at different moments in time. These changes were underpinned by one key driver, that is, government agricultural policies that promoted investment in commercial agriculture for national and export markets. The study shows that understanding the complex interaction between the contributing factors and drivers of LULC change is crucial to inform decision-making and policies, in particular, by directing responses towards the underlying drivers of change rather than only the proximate causes.

Towards Land Consumption Neutrality and Natural Capital Enhancement at Urban Landscape Scale

Among the UNCCD SDGs 2030, there is the recognition that land consumption can strongly affect the provision of ecosystem services. From the perspective of land degradation neutrality, urban level is the right scale when planning actions against land consumption. The aims of this research are: (1) to assess land consumption at urban landscape scale and its effects on natural capital flow provision; and (2) to identify sustainable strategic planning choices for land consumption mitigation and natural capital enhancement. We propose and test an approach based on multi-temporal landscape spatial analysis (land use/land cover map, land consumption map, and landscape metrics) and ecosystem services’ flow assessment for the identification of areas at risk of loss of natural capital flow. The results have shown that from 2006 to 2019, land consumption has increased with a consequent decrease of natural capital flow. LULC dynamics has been analyzed in terms of landscape risk to lose natural capital flow, highlighting that the management of Galatone urban landscape is still far from land consumption neutrality. Landscape metrics have allowed the analysis of the aggregation among land consumption areas. The mitigation of land consumption should be based on the identification of suitable nature-based solutions towards the balance between past land consumption and future land recovery.

Effects of climate change and anthropogenic activity on the vegetation greening in the Liaohe River Basin of northeastern China

Elucidating the response mechanism of variation in vegetation trend to determinant is of great value to environmental resource management, particularly significant in the ecologically fragile area. The Liaohe River Basin (LRB) is a key part of eco-security in China, which has experienced apparent climatic variations and intensified human activities in recent decades. Yet, it still remains not clear about drivers in shaping the spatio-temporal patterns of vegetation growth. Here, the normalized difference vegetation index (NDVI) was utilized to investigate the spatio-temporal variation of vegetation coverage from 2000 to 2019. Then, we incorporated partial derivatives analysis to conduct attribution analyses of vegetation greening in light of the meteorological data. The prime findings are as follows: (1) The vegetation coverage in the LRB presented a growing state in the recent 20 years at a rate of 0.0031/a, with significant spatial and temporal heterogeneity due to its slope; (2) The attribution results showed that the average contribution of precipitation, temperature, and solar radiation to the NDVI changes in the LRB was 0.00205/a, 0.00008/a, and −0.00028/a, respectively. (3) The climatic change played the most dominant role in influencing vegetation activities as a result of the relative contributions of 59.68% of NDVI changes (40.32% contributed by anthropogenic activities); (4) LULC dynamics were characterized by an increase in forest land and large-scale ecological afforestation projects, which increase vegetation coverage. Conversely, urbanization adversely affected vegetation variations. Understanding the findings of this study is expected to offer further scientific support and practical implications for monitoring the local vegetation status.

Mapping LULC Dynamics and Its Potential Implication on Forest Cover in Malam Jabba Region with Landsat Time Series Imagery and Random Forest Classification

Pakistan has an annual deforestation rate of 4.6% which is the second highest in Asia. It has been described by the Food and Agriculture Organization (FAO) that the deforestation rate increased from 1.8–2.2% within two decades (1980–2000 and 2000–2010). KPK (Khyber Pakhtunkhwa), Pakistan’s northwestern province, holds 31% of the country’s total forest resources, the majority of which are natural forests. The Malam Jabba region, known for its agro-forestry practices, has undergone significant changes in its agricultural, forestry, and urban development. Agricultural and built-up land increased by 77.6% in the last four decades, and significant changes in land cover especially loss in forest, woodland, and agricultural land were observed due to flood disasters since 1980. For assessing and interpreting land-cover dynamics, particularly for changes in natural resources such as evergreen forest cover, remote sensing images are valuable assets. This study proposes a framework to assess the changes in vegetation cover in the Malam Jabba region during the past four decades with Landsat time series data. The random forest classifier (RF) was used to analyze the forest, woodland, and other land cover changes over the past four decades. Landsat MMS, TM, ETM+, and OLI satellite images were used as inputs for the random forest (RF) classifier. The vegetation cover change for each period was calculated from the pixels using vegetation indices such as NDVI, SAVI, and VCI. The results show that Malam Jabba’s total forest land area in 1980 was about 236 km2 and shrank to 152 km2 by 2020. The overall loss rate of evergreen forests was 35.3 percent. The mean forest cover loss rate occurred at 2.1 km2/year from 1980 to 2020. The area of woodland forest decreased by 87 km2 (25.43 percent) between 1980 and 2020. Other landcover increased by 121% and covered a total area of 178 km2. The overall accuracy was about 94% and the value of the kappa coefficient was 0.92 for the change in forest and woodland cover. In conclusion, this study can be beneficial to researchers and decision makers who are enthusiastic about using remote sensing for monitoring and planning the development of LULC at the regional and global scales.

Spatiotemporal green water dynamics and their responses to variations of climatic and underlying surface factors: A case study in the Sanjiang Plain, China

Study regionThe Sanjiang Plain (SJP), located at the confluence reaches of the Heilong, Songhua, and Wusuli Rivers in Northeast China. Study focusThis study aimed to quantify the effects of varying climate and land-use/land-cover (LULC) dynamics on green water (GW) over the SJP during two distinctive periods (i.e., pre-2000 and post-2000), when synergetic effects of increased precipitation and temperature and rapid development of agriculture occurred. This assessment used the distributed eco-hydrological model ESSI-3. Multivariable and multi-objective calibration approaches (i.e., discharge, evapotranspiration, and terrestrial water storage anomaly) were used to ensure the high accuracies of the model outputs. New hydrological insights for the regionThis research concluded that GW flow and GW storage in the SJP evidently increased after 2000 compared with before. Across the SJP, GW flow and GW storage responded differently to climate changes and LULC dynamics during pre-2000 and post-2000 period. Our results demonstrated that GW storage changes were predominately affected by climatic changes, especially variations in precipitation, whose contribution accounted for more than 56% after 2000. However, GW flow changes were mainly governed by LULC changes, especially the influence of cropland transformation, whose contribution increased over 20% in the post-2000 period. This study posed a deep insight regarding impacts of regional climatic and LULC changes on GW dynamics, which may benefit decision-making for planting structure optimization and adjustment over the region.

Exploring Multiscale Influence of Urban Growth on Landscape Patterns of Two Emerging Urban Centers in the Western Himalaya

The Western Himalaya are experiencing and epitomizing growing urbanization trends due to rapid population and tourism rise across the Indian Himalayan region. The pace and process of urban development in these regions are largely unplanned and unregulated; consequently, the altered landscape composition and configuration are influencing key ecological processes and functions supporting human wellbeing. Existing urbanization research addressing this issue has mainly focused on large urban centers, underrepresenting the potential role of medium-sized cities in sustainable landscape planning. Thus, this study attempted to quantify land use/land cover and landscape pattern dynamics in response to urban growth and expansion in and around two emerging urban centers—Dharamsala and Pithoragarh, Western Himalaya, over the past two decades. The study was split into three temporal periods, and intensity analysis was used to characterize transformational patterns in the city and outer zone of each landscape. The results indicate that, during the T2 and T3 period, the overall LULC dynamics was highest in Dharamsala and Pithoragarh, respectively. The urban development in Dharamsala occurred at the expense of cropland followed by vegetation and forest, while, in Pithoragarh, it occurred at the expense of cropland followed by vegetation loss dominated. Furthermore, the landscape pattern results highlighted the aggregation and homogenization at the city level, with a higher degree of disaggregation, fragmentation, and heterogeneity in outer zone. This paper highlights the importance of transformational patterns based on intensity analysis and landscape patterns to sustainable landscape development and planning. In addition, considering the past to present urban development trajectories, this study purposes a framework for sustainable landscape development in Himalaya for urban planners and policymakers.

Cellular Automata-Based Artificial Neural Network Model for Assessing Past, Present, and Future Land Use/Land Cover Dynamics

Land use and land cover change (LULCC) is among the most apparent natural landscape processes impacted by anthropogenic activities, particularly in fast-growing regions. In India, at present, due to the impacts of anthropogenic climate change, supplemented by the fast pace of developmental activities, the areas providing the highest agricultural yields are facing the threat of either extinction or change in land use. This study assesses the LULCC in the fastest-changing landscape region of the Indian state of Bihar, District Muzaffarpur. This district is known for its litchi cultivation, which, over the last few years, has been observed to be increasing in acreage at the behest of a decrease in natural vegetation. In this study, we aim to assess the past, present and future changes in LULC of the Muzaffarpur district using support vector classification and CA-ANN (cellular automata-artificial neural network) algorithms. For assessing the present and past LULC of the study area, we used Landsat Satellite data for 1990, 2000, 2010, and 2020. It was observed that between 1990 and 2020, the area under vegetation, wetlands, water body, and fallow land decreased by 44.28%, 34.82%, 25.56%, and 5.63%, respectively. At the same time, the area under built-up, litchi plantation, and cropland increased by 1451.30%, 181.91%, and 5.66%, respectively. Extensive ground truthing was carried out to assess the accuracy of the LULC for 2020, whereas historical google earth images were used for 1990, 2000, and 2010, through the use of overall accuracy and kappa coefficient indices. The kappa coefficients for the final LULC for the years 1990, 2000, 2010, and 2020 were 0.79, 0.75, 0.87, and 0.85, respectively. For forecasting the future LULC, first, the LULC of 1990 and 2010 were used to predict the landscape for 2020 using the CA-ANN model. After calibrating and validating the CA-ANN outputs, LULC for 2030 and 2050 were generated. The generated future LULC scenarios were validated using kappa index statistics by comparing the forecast outcomes with the original LULC data for 2020. It was observed that in both 2030 and 2050, built-up and vegetation would be the major transitioning LULC. In 2030 and 2050, built-up will increase by 13.15% and 108.69%, respectively, compared to its area in 2020; whereas vegetation is expected to decrease by 14.30% in 2030 and 32.84% in 2050 compared to its area in 2020. Overall, this study depicted a decline in the natural landscape and a sudden increase in the built-up and cash-crop area. If such trends continue, the future scenario of LULC will also demonstrate the same pattern. This study will help formulate better land use management policy in the study area, and the overall state of Bihar, which is considered to be the poorest state of India and the most vulnerable to natural calamities. It also demonstrates the ability of the CA-ANN model to forecast future events and comprehend spatiotemporal LULC dynamics.

Multi-Scenario Simulation of Land-Use Change and Delineation of Urban Growth Boundaries in County Area: A Case Study of Xinxing County, Guangdong Province

Delineating urban growth boundaries (UGBs) by combining the land-use/land-cover (LULC) change simulation method has become common in recent studies. However, few of the existing studies have integrated multi-source big data to analyze the driving factors of LULC dynamics in the simulation. Moreover, most of previous studies mainly focused on the UGBs delineation in macroscale areas rather than small-scale areas, such as the county area. In this study, taking Xinxing County of Guangdong Province as the study area, we coupled a system dynamics (SD) model and a patch-generating land-use simulation (PLUS) model to propose a framework for the LULC change simulation and UGBs delineation in the county area. Multi-source big data such as points of interest (POIs), night-time light (NTL) data and Tencent user density (TUD) were integrated to analyze the driving forces of LULC change. The validation results indicate that the coupled model received high accuracy both in the land-use demand projection and LULC distribution simulation. The combination of multi-source big data can effectively describe the influence of human socio-economic factors on the expansion of urban land and industrial land. The UGBs delineation results have similar spatial patterns with the LULC change simulation results, which indicates that the proposed UGBs delineation method can effectively transform the LULC simulation results into available UGBs for the county area. It has been proven that the proposed framework in this study is effective for the LULC change simulation and UGBs delineation in the county area, which can provide insight on territorial spatial planning in the county area.