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Deforestation and alterations in land use are key factors contributing to rises in both local and global temperatures. However, the effect of these alterations on land surface temperature (LST) remains understudied in many areas that have experienced significant changes in land use. To address this gap, a spatial and temporal evaluation of land use and land cover (LULC) was performed to identify potential changes in LST using satellite imagery and statistical analysis. This study focused on the central and northern zones of Sinaloa, Mexico, an agriculturally important region where cultivated land has expanded in recent years, covering the period from 1993 to 2017. The results demonstrated that the study area exhibited an increase in LST over time, which was strongly linked to the expansion of agricultural land. The least-squares method also demonstrated warming trends in both the winter and summer seasons. An increasing rate of 0.1672 °C/year was found in winter, while a LST value of 0.1176 °C/year was found in summer. Warming areas were identified in the foothill regions and an increase in LST in mountain ranges was observed, where a loss of low deciduous forest cover was detected., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Changes in land use and land cover (LULC) have significant implications for biodiversity, ecosystem functioning, and deforestation. Modeling LULC changes is crucial to understanding anthropogenic impacts on environmental conservation and ecosystem services. Although previous studies have focused on predicting future changes, there is a growing need to determine past scenarios using new assessment tools. This study proposes a methodology for LULC past scenario generation based on transition analysis. Aiming to hindcast LULC scenario in 1970 based on the transition analysis of the past 35 years (from 1985 to 2020), two machine learning algorithms, multilayer perceptron (MLP) and similarity weighted (SimWeight), were employed to determine the driver variables most related to conversions in LULC and to simulate the past. The study focused on the Aristida spp. grasslands in the Uruguayan savannas, where native grasslands have been extensively converted to agricultural areas. Land use and land cover data from the MapBiomas project were integrated with spatial variables such as altimetry, slope, pedology, and linear distances from rivers, roads, urban areas, agriculture, forest, forestry, and native grasslands. The accuracy of the predicted maps was assessed through stratified random sampling of reference images from the Multispectral Scanner (MSS) sensor. The results demonstrate a reduction of approximately 659 934 ha of native grasslands in the study area between 1985 and 2020, directly proportional to the increase in cultivable areas. The MLP algorithm exhibited moderate performance, with notable errors in classifying agriculture and grassland areas. In contrast, the SimWeight algorithm displayed better accuracy, particularly in distinguishing grassland and agriculture classes. The modeled map using SimWeight accurately represented the transitions between grassland and agriculture with a high level of agreement. By modeling the 1970s scenario using the SimWeight model, it was estimated that the Aristida spp. grasslands experienced a substantial reduction in grassland coverage, ranging from 9982.31 to 10 022.32 km 2 between 1970 and 2020. This represents a range of 60.8%-61.07% of the total grassland area in 1970. These findings provide valuable insights into the driving factors behind land use change in the Aristida spp. grasslands and offer useful information for land management, conservation, and sustainable development in the region. The study's main contribution lies in the hindcasting of past LULC scenarios, utilizing a tool used primarily for forecasting future scenarios. Integr Environ Assess Manag 2024;20:1140-1155. © 2023 SETAC., (© 2023 SETAC.)

Analysis of land use and land cover (LULC) change and its drivers and impacts in the biodiversity hotspot of Bale Mountain's socio-ecological system is crucial for formulating plausible policies and strategies that can enhance sustainable development. The study aimed to analyze spatio-temporal LULC changes and their trends, extents, drives, and impacts over the last 48 years in the Bale Mountain social-ecological system. Landsat imagery data from the years 1973, 1986, 1996, 2014, and 2021 together with qualitative data were used. LULC classification scheme employed a supervised classification method with the application of the maximum likelihood algorithm technique. In the period between 1973 and 2021, agriculture, bare land, and settlement showed areal increment by 153.13%, 295.57%, and 49.03% with the corresponding increased annual rate of 1.93%, 2.86%, and 0.83%, respectively. On the contrary, forest, wood land, bushland, grass land, and water body decreased by 29.97%, 1.36%, 28.16%, 8.63%, and 84.36% during the study period, respectively. During the period, major LULC change dynamics were also observed; the majority of woodland was converted to agriculture (757.8 km 2 ) and grassland (531.3 km 2 ); and forests were converted to other LULC classes, namely woodland (766.5 km 2 ), agriculture (706.1 km 2 ), grassland (34.6 km 2 ), bushland (31.9 km 2 ), settlement (20.5 km 2 ), and bare land (14.3 km 2 ). LULC changes were caused by the expansion of agriculture, settlement, overgrazing, infrastructure development, and fire that were driven by population growth and climate change, and supplemented by inadequate policy and institutional factors. Social and environmental importance and values of land uses and land covers in the study area necessitate further assessment of potential natural resources' user groups and valuation of ecosystem services in the study area. Hence, we suggest the identification of potential natural resource-based user groups, and assessment of the influence of LULC changes on ecosystem services in Bale Mountains Eco Region (BMER) for the sustainable use and managements of land resources., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The conversion of natural ecosystems to agricultural land and urban areas plays a threat to the protected areas and the natural ecosystems conservation. The aim of this paper is to provide an analysis of the agricultural expansion and its impact on the landscape spatial and temporal patterns in a buffer zone of a protected area located in the transition zone between the Atlantic Forest and Cerrado, in the State of São Paulo, Brazil. The land use and land cover were mapped between 1971 and 2008 and landscape metrics were calculated to provide a spatiotemporal analysis of the forest structure and the expansion of the croplands. The results showed that the landscape patterns were affected by the economic cycles. The predominant crop surrounding the protected area is sugar cane, which increased by 39% during this period, followed by citrus. This landscape change is connected to the Brazilian oil crisis in 1973. The rapid expansion of sugar cane was largely driven by Brazil's biofuel program, the "Proálcool" (pro-alcohol), a project in 1975 that mixed ethanol with gas for automotive fuel. The forest loss occurred mainly between 1971 and 1988, decreasing the forest cover from 17% in 1971 to 12.7% in 2008. Most of the forest patches are smaller than 50 ha and has low connectivity. Throughout the years, the fragments in the buffer zone have become smaller and with an elongated shape, and the park has become isolated. This forest fragmentation process and the predominance of monoculture lands in the buffer zone threaten the protected areas, and can represent a barrier for these areas to provide the effective biodiversity conservation. The measures proposed are necessary to ensure the capability of this ecosystem to sustain its original biodiversity., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

While the urban runoff are increasingly being studied as a source of fecal indicator bacteria (FIB), less is known about the occurrence of FIB in watershed with mixed land use and ongoing land use and land cover (LULC) change. In this study, Escherichia coli (EC) and fecal streptococcus (FS) were monitored from 2012 to 2013 in agricultural, mixed and urban LULC and analyzed according to the most probable number (MPN). Pearson correlation was used to determine the relationship between FIB and environmental parameters (physicochemical and hydrometeorological). Multiple linear regressions (MLR) were used to identify the significant parameters that affect the FIB concentrations and to predict the response of FIB in LULC change. Overall, the FIB concentrations were higher in urban LULC (EC=3.33-7.39; FS=3.30-7.36log10MPN/100mL) possibly because of runoff from commercial market and 100% impervious cover (IC). Also, during early-summer season; this reflects a greater persistence and growth rate of FIB in a warmer environment. During intra-event, however, the FIB concentrations varied according to site condition. Anthropogenic activities and IC influenced the correlation between the FIB concentrations and environmental parameters. Stormwater temperature (TEMP), turbidity, and TSS positively correlated with the FIB concentrations (p>0.01), since IC increased, implying an accumulation of bacterial sources in urban activities. TEMP, BOD5, turbidity, TSS, and antecedent dry days (ADD) were the most significant explanatory variables for FIB as determined in MLR, possibly because they promoted the FIB growth and survival. The model confirmed the FIB concentrations: EC (R(2)=0.71-0.85; NSE=0.72-0.86) and FS (R(2)=0.65-0.83; NSE=0.66-0.84) are predicted to increase due to urbanization. Therefore, these findings will help in stormwater monitoring strategies, designing the best management practice for FIB removal and as input data for stormwater models., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Urban expansion in Ethiopia, particularly informal construction, has led to complex and dynamic changes in livelihoods and wetlands. This study examined the effects of urban expansion on the Cheleleka Wetland and the livelihood of the peripheral community in Hawassa. A mixed research design was used to quantitatively analyze Landsat imagery (1991, 2001, 2011, 2021) and socioeconomic data. Key informant interviews and focus group discussion data were narrated qualitatively. The built-up area substantially increased from 718.11 ha in 1991 to 4820.71 ha in 2021. However, agricultural and wetland areas decreased from 8807.58 ha and 8177.04 ha in 1991 to 6382.44 ha and 7030.26 ha in 2021, respectively. Water and forest areas fluctuated. Built-up areas had a significant advantage over other land use and cover classes, with agricultural, wetland, and forest areas being converted into built-up areas throughout the study period. As a result, the area of Lake Hawassa expanded. Currently, the Cheleleka Wetland has been negatively impaired by overgrazing, cutting of grass, animal dung dumps, eucalyptus plantations, agricultural expansion, and urban expansion. Urban expansion has exacerbated social and economic divisions, resulting in the concentration of poor-quality neighborhoods in the peripheral community. To protect the wetlands, alternative livelihoods and participatory land-use planning are crucial. [ABSTRACT FROM AUTHOR]

Precision is required for agricultural advancements to be sustainable. Traditional farming lacks effective monitoring, resulting in resource waste and environmental problems. Farmland mapping is important for agricultural management and land-use planning. The use of deep learning techniques in farmland mapping is increasing rapidly. Excellent results have been generated from deep learning approaches in a number of applications, such as image processing and prediction. Agricultural agencies are now considering different applications of deep learning including land mapping, crop classification, and monitoring of paddy fields. This paper shall explore different deep learning models that are commonly used for image processing specifically in land mapping. The three deep learning models convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) were evaluated to find out which among the deep learning models is best for land mapping. It compares the classification accuracy of the models on image processing and it can be concluded that CNN algorithm normally makes better results when compared to other deep learning models. This study offers guideline and suggestions to researchers who are interested in contributing to the field of precision agriculture with the used of deep learning techniques. [ABSTRACT FROM AUTHOR]

[Objective] Detecting the spatial and temporal variation characteristics of soil erosion in river basins and carry out sustainable river basin management. [Methods] This study quantitatively analyzes the temporal and spatial characteristics of soil erosion in Guishui River Basin by RUSLE model based on the rainfall, soil, digital elevation model (DEM) and land uses datasets of Guishui River Basin from 1995 to 2018, meanwhile, the soil stability in the basin was evaluated. [Results] (1) The areas of forestland and grassland showed a decreasing trend from 1995 to 2018, respectively. In 2018, the areas of forestland and grassland were 4.41×104 and 0.84×104 hm2, respectively, which were 13.52% and 10.61% lower than those in 1995. The farmland area increased from 3.53×104 hm2 in 1995 to 4.07×104 hm2 in 2018. The area of building land has gradually increased, from 0.59×104 hm2 in 1995 to 1.90×104 hm2 in 2018. (2) The soil erosion modulus in Guishui River Basin fluctuated from 8.71 t/(hm2·a) in 1995 to 4.56 t/(hm2·a), and then increased to 11.07 t/(hm2·a) in 2018. (3) The intensity of soil erosion in Guishui River Basin is mainly in slight erosion. From 1995 to 2015, the intensity of soil erosion gradually decreased and remained stable, and the proportion of moderate and above erosion area decreased from 4.95% to 3.05%, and increase to 7.42% after 2018. (4) In Guishui River Basin, the soil stability decreased and the unstable soil area increased during the study period. [Conclusion] In the urbanization process of Guishui River Basin, the coverage of forest and grass decreased slightly; the intensity of soil erosion decreased totally, but increased slightly in the last stage; unstable soil land takes up a small proportion. The results of this study provide a basis for comprehensive management and land use planning of Guishui River Basin.

The expanding cropland profoundly affects stream water quality. However, the relationships between landscape patterns and stream water quality in different cropland composition classes remain unclear. We observed total nitrogen (TN), total phosphorus (TP) concentrations, and landscape patterns changed in 78 sub-watersheds of the Taihu Lake Basin’s Jiangsu segment from 2005 to 2020. The results showed that cropland area was positively correlated with TN and TP concentrations. The 21.10% reduction in cropland area, coupled with a 41.00% increase in building land, has led to an escalation in cropland fragmentation. Meanwhile, TN and TP concentrations declined by 26.67% and 28.57%, respectively. Partial least squares suggested that forest interspersion and juxtaposition metrics and forest area percentage were dominant factors influencing water quality in high- and medium-density cropland zones, respectively. The Cellular Automata–Markov Model shows reasonable distribution of forests. Scenarios with enhanced forest interspersion and juxtaposition metrics (75.28 to 91.12) showed reductions in TP (26.92% to 34.61%) and TN (18.45% to 25.89%) concentrations by 2025 compared to a natural economic development scenario. Landscape configuration optimization could assist managers in improving water quality.

Land use and land cover changes significantly affect the function and value of ecosystem services (ES). Exploring the spatial correspondence between changes in land cover and ES is conducive to optimizing the land use structure and increasing regional coordinated development. Thus, this study aimed to examine changes in land use and land cover (30 × 30 m) in Laos between 2000 and 2020 and their effects on ecosystem services value (ESV) using the Global Surface Cover Database land use data for 2000 to 2020, ArcGIS technology, and the table of Costanza’s value coefficients. The study results indicated that forest (79.5%), cultivated land (10.6%), and grassland (8.3%) were the dominant land use types in Laos over the past two decades. The forest area decreased significantly, while there were increases in other land types, and the forest was transformed into cultivated land and grassland. ES in Laos was valued at about USD 140–150 billion, with forest contributing the most, followed by cultivated land and grassland. ESV over the last two decades in Laos has increased by USD 3.94 million. Large values were assigned to regulating services (40%) and supporting services (14%). The ESV of food production, soil formation, and water supply increased, and the ESV of climate regulation, genetic resources, and erosion control decreased. In addition, the elasticity value of artificial surfaces was more prominent, with a more evident impact on ESV. For future development, Laos should rationally plan land resources, develop sustainable industries, maintain the dynamic balance of second-category ESV, and achieve sustainable economic and ecological development. This study provides a scientific basis for revealing changes in ESV in Laos over the past two decades, maintaining the stability and sustainable development of the environment in Laos, and realizing the sustainable use and efficient management of the local environmental resources.

In the face of urban expansion, ensuring sustainable water consumption is paramount. This study aims to develop a domestic water demand forecast model that considers population heterogeneity and the urban area distribution in a city in the Brazilian Semiarid Region. The methodology comprises three main steps: (1) spatial data collection to identify explanatory variables for a future Land Use and Cover (LULC) model; (2) simulation of LULC data for 2030, 2040, and 2050 using the MOLUSCE plugin; and (3) estimation of domestic water demand based on projected urban area expansion and a linear regression model incorporating demographic indicators of household income, residents per household, total population, and gender. The results demonstrated a consistent LULC simulation, indicating an urban expansion of 4 km2 between 2030 and 2050, with reductions of 0.6 km2 in natural formations and 3.4 km2 in farming areas. Using LULC data, the study predicted a 14.21% increase in domestic water consumption in Campina Grande for 2050 compared to 2010, equivalent to an increase of 2,348,424.96 m3. Furthermore, the spatial analysis draws a spatial profile of water consumption among residents, highlighting the areas with the highest per capita consumption. Thus, this research offers a consistent approach to estimating water demand in regions with limited consumption data, providing valuable insights for decision-makers to consider in urban planning. [ABSTRACT FROM AUTHOR]

Air pollution, particularly fine particulate matter (PM2.5), poses a significant health risk, especially in high-density urban areas. Urban green space (UGS) can effectively mitigate this pollution. Despite their potential, strategies for effectively leveraging Land Use/Land Cover (LULC) optimization to combat PM2.5 remain largely unexplored. Ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) were employed to investigate the spatial heterogeneity relationship between UGS conversion and PM2.5 fluctuations across various scales and evolutionary stages, developing a multiscale practical framework for LULC synergy in combating air pollution. The areas of UGSs to/from other LULCs, PM2.5 concentrations and corresponding variation zones exhibited significant spatial clustering. These UGS conversions explained more than 65% of the PM2.5 changes in the study area, peaking at 76.4% explanatory power in the fourth stage. Compared to global spatial analysis (OLS: 0–0.48), local spatial regression analysis significantly improved the R2 value (GWR: 0.32–0.75, MGWR: 0.48–0.90), but the fitting quality of local spatial regression analysis decreased with increasing scale, highlighting the importance of scale diagnosis. A 2 km scale was identified as optimal for assessing the spatial heterogeneity impact of UGS and other LULC conversions on PM2.5 changes. Conversion areas from water bodies and bare land to UGSs maintain stable local spatial properties at this scale (bandwidths: 44–99). Our research provides new insights into LULC management and planning, offering a coordinated approach to mitigating urban air pollution. Additionally, a practical framework was established for addressing spatially continuous variables such as PM2.5, revealing effective approaches for addressing urban environmental issues.

This study provides a detailed analysis of land use and land cover (LULC) changes at the district level within the Delhi–Mumbai Industrial Corridor (DMIC) from 2001 to 2021. Using the Indian Meteorological Department’s sub-divisional framework and MODIS data across seven primary LULC classes, the analysis is instrumental in informing infrastructure planning for existing and future smart cities and industrial clusters within the DMIC. The key findings reveal a yearly increase of 3031.40 sq. km. per year in agricultural land, with decreases in shrubland, grassland, and bareland of −1774.72 sq. km. per year, −1119.62 sq. km. per year, and −203.76 sq. km. per year, respectively. On the other hand, forests grew by a modest 148.14 sq. km. per year, while waterbodies and built-up lands saw minor increases of 55.73 sq. km. and 21.48 sq. km. per year. Ecologically Sensitive Areas (ESAs) were evaluated for LULC changes. The smart cities of Pune and Thane serve as excellent examples of balanced urban development and natural growth management. However, the study also highlights the need for further research to investigate LULC impacts on climatic variables, advocating for a regional planning approach in the DMIC.

The onset of prehistoric farming brought unprecedented changes to landscapes and their biodiversity. Past biodiversity patterns are broadly understood for different parts of Europe, and demonstrate trajectories that have been linked to prehistoric and historic demographic transitions, and associated land-use practices. To our knowledge, this paper is the first attempt to directly link evidence of agricultural practice from the archaeological record to biodiversity patterns. Records of fossil pollen are used to estimate plant and landscape diversity patterns, and novel approaches are employed to analyse 1194 harmonised archaeobotanical samples (plant macrofossil remains) spanning the prehistoric and Roman periods, from southern England. We demonstrate changes in the use of crops and gathered edible plants and non-linear trends in cultivation practices. Whilst, overall, cereal production is characterised by ever larger and extensive regimes, different trajectories are evident for most of early prehistory, the Middle Iron Age and the Late Roman period. Comparisons with the Shannon diversity of fossil pollen records from the same region suggest a positive relationship between developing agricultural regimes and landscape scale biodiversity during the prehistoric period. The Roman period represents a tipping point in the relationship between expanding agriculture and pollen diversity, with declining pollen diversity evident in the records from the region. [ABSTRACT FROM AUTHOR]

Agriculture in free spaces within urban areas sustains residents as they grow crops to ensure food availability while at the same time selling other produce to fund other needs. Due to the value of urban agricultural production, the main focus of this study was to investigate the implications of urban growth on maize yield in Harare metropolitan city. In order to achieve this, Landsat multispectral and multi-temporal data were used to establish the responses of the estimated maize yield to city growth from 1984 to 2018. Initially, Land Use and Land Cover map for each period were produced using multispectral images and field observation in Support Vector Machine (SVM)-based supervised image classification. The maps were reclassified using a binary scheme of croplands and non-croplands, which was used to quantify cropland area in each period and hence change over time. The maize yield for each period was estimated from data obtained from a study of maize yields obtained at the University of Zimbabwe Farm (UZ) farm for maize grown under variable agricultural practices. Results showed that cropland area was reduced from 11,120 ha in 1984 down to 2631 ha in 2018. The estimated average maize yield decreased from 52,264 tons in 1984 to 12,366 tons in 2018. In addition to showing the value of urban agriculture, the findings of this study are important in informing the government, municipalities, and other stakeholders about how urban growth has the potential to compromise food security and livelihoods, especially for the urban poor. [ABSTRACT FROM AUTHOR]

Classification of remotely sensed imagery for reliable land use and land cover (LULC) remains a challenge in areas where spectrally similar LULC features occur. For example, bare soils of harvested crop fields in agricultural watersheds exhibit spectral characteristics similar to high-intensity developed regions and impede an accurate classification. The goal of this study is to improve the accuracy of LULC classification of satellite imagery for the Big Sunflower River Watershed, Mississippi using ancillary data, multiple classification methods, and a post-classification correction (PCC). To determine the best approach, the methodology was applied to Landsat 8 Operational Land Imager (OLI) imagery during the growing season and post-harvest. Imagery for the growing season was acquired on 25 August 2015, and post-harvest was acquired on 7 January 2018. Three classification methods were applied: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). LULC imagery was classified as open water, woody wetlands, harvested crop, rangeland, cultivated crop, high-intensity developed, and mid-low intensity developed areas. Ancillary data such as normalized difference vegetation index (NDVI), thematic maps of urban areas, river networks, transportation networks, high-resolution National Agriculture Imagery Program (NAIP) imagery, Google Earth time-series data, and phenology were used to determine the training dataset. Initially none of the three classification methods performed adequately. Hence, a post-classification correction (PCC) was implemented by masking and applying a majority filter using thematic maps of urban areas. Once PCC was implemented, the accuracies from each of the classification methods increased significantly with the SVM classification method performing best in both the growing season and post-harvest with an overall classification accuracy of 93.5% with a Kappa statistic of 0.88 in the post-harvest imagery and an overall classification accuracy of 84% with a Kappa statistic of 0.789 in the imagery from the growing season. It was found that SVM was the best classification method while PCC is an effective strategy to implement when dealing with spectrally similar LULC features. The use of SVM together with PCC increased the reliability of the information extracted. Strategies from this study can help to evaluate the LULC in agricultural and other watersheds. [ABSTRACT FROM AUTHOR]

Much land has been cleared of indigenous forest for pastoral agriculture worldwide. In New Zealand, the clearance of indigenous forest on hill country has resulted in high food production, but waterways have become turbid, with high nutrient and E. coli concentrations. A range of on-farm mitigations are available, but it is unclear how they should be applied catchment-wide. We have developed a catchment-scale model that integrates economics with ecosystem services to find a better balance between agriculture and nature. In the upper Wairua catchment, Northland, if three actions are prioritised—(1) keeping stock out of streams, (2) constructing flood retention bunds in first-order catchments, and (3) planting trees on highly erodible land—then sediment loads, E. coli levels, and flooding are significantly reduced. Implementing these actions would cost approximately 10% of catchment net revenue, so it is feasible with a combination of regulation and subsidy. Many catchments in New Zealand are primarily pastoral agriculture, as in other countries (in North and South America, Australasia, and the United Kingdom), and would benefit from the analysis presented here to guide development along sustainable pathways. While pastoral agriculture typically stresses waterways, with increased sedimentation and freshwater contaminants, much can be done to mitigate these effects with improved farm and riparian management. [ABSTRACT FROM AUTHOR]

Considering that land use and land cover (LULC) change is one of the most important challenges to biodiversity today, we used Copernicus products to analyze LULC changes at the level of the “Nordul Gorjului de Vest” Natura 2000 site (Romania) from 1990 to 2018. The interpretation of the impact of these changes on areas with very high potential for three regulating ecosystem services (ESs) (local climate regulation, regulation of waste, and water purification) was performed. Forest habitats are the major LULC class category in the “Nordul Gorjului de Vest” Natura 2000 site, with broad-leaved forest as the dominant forest class. In terms of areas lost or gained by the different LULC classes for each analyzed time interval, most transformations took place in the period 2000–2006, changes which were also reflected in the overall study period (1990–2018). During this time frame, the conversion of transitional forest shrubs into broad-leaved forest, which is the second largest transition in terms of absolute area changed, led, in terms of contribution rates, to an increase in the areas with very high potential for two of the three analyzed ESs. The conversion of transitional woodland shrub into broad-leaved forest was conductive only to synergy for all the pairwise interactions between the three ESs.

Rapid urbanization threatens the ecological environment and quality of life by significantly altering land use and land cover (LULC) and heat distribution. One of the most significant environmental consequences of urbanization is the urban heat island effect (UHI). This study investigated the spatiotemporal characteristics of the SUHI and its relationship with land use types from 2000 to 2020 in Urumqi City, located in an arid and semi-arid region of northwestern China. Additionally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the relationship between the land surface temperature (LST) and influencing factors. The results showed that the area of the lower surface temperature classes has decreased significantly. In comparison, the area of the higher surface temperature classes has experienced a steady rise over the last two decades. From 2000 to 2020, the share of the area occupied by the temperature range 2 and adjusted R2 of the GWR were 0.75 and 0.73, obviously larger than the 0.58 of the OLS models. The GWR model’s higher R2 and adjusted R2 compared to the OLS model indicates that the relationship between LST and the influencing factors underlying the model may exhibit spatial non-stationarity, and the GWR model performs better than the OLS model. The results of both OLS and GWR models show that the normalized difference vegetation index (NDVI) and slope were negatively correlated with LST, while the urban index (UI) and normalized difference built-up index (NDBI) were positively correlated with LST. The findings of the study indicate that increasing green spaces and limiting the unplanned expansion of urban areas are effective measures to mitigate the UHIs in the study area. The results of the study may provide valuable insights into the spatiotemporal characteristics of the UHI and its drivers. Understanding the spatiotemporal characteristics of the UHI can help urban planners, policymakers, and scientists develop more effective urban cooling strategies and improve the urban thermal environment.

Arid and semi-arid areas are facing severe land degradation and desertification due to water scarcity. To alleviate these environmental issues, the Chinese government has launched a “water conveyance” project for environmental protection along the Tarim River. While previous studies have mainly focused on environmental conditions, the influence of these policies on land use conditions remains less explored. Therefore, this study first simulated the land use and land cover (LULC) changes in a major city (Korla) around the Tarim River. We found that the water conveyance routes have exerted notable influences on surrounding LULC changes. Next, we primarily focused on the LULC changes among different reaches of the Tarim River. We found that water and forest areas in the lower reaches have increased at the expense of a slight decrease in such areas in the upper and middle reaches, which suggests that the water conveyance policy may also have unintended consequences. These findings could attract the attention of decision makers in many other arid and semi-arid areas, and they could provide practical policy implications for other similar inter-basin water conveyance projects. The benefits and risks of these man-made projects should be carefully balanced.

Much land has been cleared of indigenous forest for pastoral agriculture worldwide. In New Zealand, the clearance of indigenous forest on hill country has resulted in high food production, but waterways have become turbid, with high nutrient and E. coli concentrations. A range of on-farm mitigations are available, but it is unclear how they should be applied catchment-wide. We have developed a catchment-scale model that integrates economics with ecosystem services to find a better balance between agriculture and nature. In the upper Wairua catchment, Northland, if three actions are prioritised—(1) keeping stock out of streams, (2) constructing flood retention bunds in first-order catchments, and (3) planting trees on highly erodible land—then sediment loads, E. coli levels, and flooding are significantly reduced. Implementing these actions would cost approximately 10% of catchment net revenue, so it is feasible with a combination of regulation and subsidy. Many catchments in New Zealand are primarily pastoral agriculture, as in other countries (in North and South America, Australasia, and the United Kingdom), and would benefit from the analysis presented here to guide development along sustainable pathways. While pastoral agriculture typically stresses waterways, with increased sedimentation and freshwater contaminants, much can be done to mitigate these effects with improved farm and riparian management.

Developing conservation strategies to mitigate cumulative impacts requires the understanding of historic land use and land cover changes at the regional scale. By using a multisensory and multitemporal approach, we identified the major changes driving cumulative impacts on native vegetation in northeastern Amazon. Comparing two regions, one with mining as the key driver and another where mining is associated with other industrial activities (cellulose), we explore the land use and land cover historic dynamics and derive implications for the assessment of cumulative impacts. Transitions of forest cover to pastureland, silviculture, and urban expansion were mapped in detail over a 20-year period, revealing that silviculture growth cleared more forests than pastureland expansion when associated with pulp mill activities and kaolin mining. In contrast, in a region with gold and iron mining, pastureland expansion was more relevant, clearing mainly areas surrounding new roads. This research shows that the interplay of major mining and industrial investments can produce cumulative losses of native vegetation, depending on the associated industries and infrastructure required for the project development. Our findings emphasize that the definition of spatial and temporal boundaries for the assessment of cumulative impacts must consider different trends in impact accumulation and changes in their spatial distribution over time. [ABSTRACT FROM AUTHOR]

Humans worldwide depend on ecosystems and the services they provide. Land use and land cover change increasingly, influencing ecosystem values to the extent that the rate and direction of change occurred. The objective of this study was to review the link between changes in Land Use and Land Cover (LULC) and Ecosystem Service Value (ESV), with emphasis on mountainous landscapes in Ethiopia. The reviewers used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guideline in the reviewing process. Area-specific and country-level studies showed that the ESV changed as the result of the LULC changes in the country. The change in land use in Ethiopia resulted not only in the loss of ESVs but also in the gain of ESVs depending on the type of man’s activity. Negative change in LULC—especially the deterioration of land cover types such as forest land, shrub land and grass land—resulted in the loss of ESVs, whereas positive LULC change increased the value of ESVs. In Ethiopia, there is a loss of about USD 85 billion per year from the loss of ecosystem services. To save, improve and promote ESVs, land restoration and rehabilitation activities are important. The review provides insights into the need for and focus of future studies on LULC changes and the valuing of ESVs to understand the impact of changes in LULC on ESVs, considering existing and forecasted population increase in rapidly urbanizing areas.

Accurate and precise spatial population data are critical to the allocation of resources for socioeconomic development and to the decision-making process for environmental management in any country. However, this type of data is not always directly available but can be estimated through spatial statistical analysis. The geo-spatialized population estimates data can provide indispensable evidence for analyzing the potential ecological threats of anthropogenic activities in ecologically protected watersheds. In this study, we applied a multiclass dasymetric mapping to estimate the geospatial distribution of the residential population of JBNERR (a natural research reserve that is located across two municipalities in southeastern Puerto Rico). We then analyzed the spatial variation of the population residing within the reserve watershed over a thirty-year period from 1990 to 2010. The result showed that the population increased by 19.5% with a growth rate of 0.97%, adding 5583 new inhabitants from 1990 to 2010 for the entire area. Where the highest population density corresponds to an urban developed area, with 254.8 ± 12.3 inhab/900 m2 in 1990, 71.2 ± 7.1 inhab/900 m2 in 2000, and 94.0 ± 4.8 inhab/900 m2 in 2010. It was followed by pastures or open areas that increased their maximum population density from 1990 to 2000 but decreased from 2000 to 2010, unlike urban areas. Our methods and results help assess the impact of urban growth on ecologically fragile areas, such as urban development in JBNERR, that may indirectly threaten the recreational activities and ecological envrionments within protected areas.

In the 1990s and 2000s, soybean farming grew sharply, particularly in states located in Brazil's mid-west region. To curb deforestation, the Federal Government implemented command and control policies and, at the same time, soy-buying companies and Civil Society Organizations implemented the Soy Moratorium. This paper focused on the major role of these initiatives in decreasing soybean farming in areas deforested after 2006 and their importance in achieving this result. We considered rich database deforestation, and soybean planted area based on highly detailed remote sensing images, combined with explanatory variables of different sources, forming a panel data of 287 municipalities over 8 years. Spatial panel data models are implemented to avoid any spatial correlation problems and to analyze relationships through regional units and time. The results confirm that lower deforestation rates in the biome laid the foundation for reducing soybean farming in the Amazon biome. However, since 2008, when the Soy Moratorium was launched, there was a structural decline in this relationship that was decisive for a decoupling of soybean farming from deforestation. Therefore, government programs to reduce deforestation made room for a new environment for agricultural expansion in line with Brazilian law and environmental commitments. The Soy Moratorium reinforced this new order, and this production chain became a case study on public and private governance, given its importance in reducing soybean farming in deforested areas after the cut-off date. These public and private effects stress the importance of coordinated actions to achieve efficient results, especially in a large social and environmentally complex region as the Brazilian Amazon, to halt biodiversity degradation and increase participation in value added markets. [ABSTRACT FROM AUTHOR]

The polarization and diffusion effects of landscape patterns are important features of megalopolis development. Under the urbanized effects, green space is a key spatial unit in delivering vital ecosystem services for sustainable urban planning. However, currently, fast urban developing is swamping the green space. In this study, by tracing landscape pattern changes of a fast-developing megalopolis, the Chengdu-Chongqing Megalopolis in the southeast of China, and using land-use data from 1980 to 2020, we aimed to determine the polarization and diffusion effects of the megalopolis and their impacts on the green space within and between the cities. We found that: (1) during the past four decades, spatial expansion of the megalopolis mainly occupied grassland and farmland, triggering an increase in landscape fragmentation; (2) based on socio-economic indicators, the spatial-attraction network analysis showed a significant polarization effect; however, based on the natural landscape, this analysis demonstrated a more scattered pattern; (3) importantly, the megalopolis developed at quite a similar pace, which caused the green rural area between the central cities demonstrating an encroached trend by the urbanization. To promote sustainability of the fast-developing megalopolis, we suggest that the boundary of the green space should be broadened to form a green network in which natural green space and urban green space are interconnected, improving the connectivity of habitats within the megalopolis for urban biodiversity. Our study implied that maintaining the green buffer shall be considered in advance for sustainable megaregional planning and establishing resilience of the fast-developing megalopolis.

Land use and land cover (LULC) change in tropical regions can cause huge amounts of carbon loss and storage, thus significantly affecting the global climate. Due to the differences in natural and social conditions between regions, it is necessary to explore the correlation mechanism between LULC and carbon storage changes in tropical regions from a broader geographical perspective. This paper takes Hainan Island as the research object, through the integration of the CA-Markov and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models, based on multi-source data, analyses the dynamics of LULC and carbon storage from 1992 to 2019 and the relationship between the two, and predicts future LULC and carbon storage under different scenarios. The results show that (1) the built-up land area of Hainan Island expanded from 103.59 km2 to 574.83 km2 from 1992 to 2019, an increase of 454.91%; the area of cropland and shrubland decreased; and the area of forest increased. (2) Carbon storage showed an upward trend during 1992–2000, and a downward trend during 2000–2019. Overall, LULC changes during 1992–2019 reduced carbon storage by about 1.50 Tg. (3) The encroachment of cropland in built-up land areas is the main reason for the reduction of carbon storage. The conversion of shrubland to forest is the main driving force for increasing carbon storage. The increase and decrease of carbon storage have obvious spatial clustering characteristics. (4) In the simulation prediction, the natural trend scenario (NT), built-up land priority scenario (BP) and ecological priority scenario (EP) reduce the carbon storage of Hainan Island, and the rate of decrease is BP> NT > EP. The cropland priority scenario (CP) can increase the LULC carbon storage, and the maximum increase in 2050 can reach 0.79 Tg. This paper supplements and improves the understanding of the correlation between LULC and carbon storage changes in tropical regions, and can provide guidance for the optimization of LULC structure in tropical regions with high economic development from a low-carbon perspective.

The monitoring of different crops (cultivated plots) and types of surface (bare soils, etc.) is a crucial economic and environmental issue for the management of resources and human activity. In this context, the objective of this study is to evaluate the contribution of multispectral satellite imagery (optical and radar) to land use and land cover classification.Object-oriented supervised classifications, based on a Random Forest algorithm, and majority zoning post-processing are used. This study emerges from the experiment on multi-sensor crop monitoring (MCM'10, Baup et al., 2012) conducted in 2010 on a mixed farming area in the southwest of France, near Toulouse. This experiment enabled the regular and quasi-synchronous collection of multi-sensor satellite data and in situ observations, which are used in this study. 211 plots with contrasting characteristics (different slopes, soil types, aspects, farming practices, shapes and surface areas) were monitored to represent the variability of the study area. They can be grouped into four classes of land cover: 39 grassland areas, 100 plots of wheat, 13 plots of barley, 20 plots of rapeseed, and 2 classes of bare soil: 23 plots of small roughness and 16 plots of medium roughness. Satellite radar images in the X-, C- and L-bands (HH polarization) were acquired between 14 and 18 April 2010. Optical images delivered by Formosat-2 and corresponding field data were acquired on 14 April 2010.The results show that combining images acquired in the L-band (Alos) and the optical range (Formosat-2) improves the classification performance (overall accuracy = 0.85, kappa = 0.81) compared to the use of radar or optical data alone. The results obtained for the various types of land cover show performance levels and confusions related to the phenological stage of the species studied, with the geometry of the cover, the roughness states of the surfaces, etc. Performance is also related to the wavelength and penetration depth of the signal providing the images. Thus, the results show that the quality of the classification often increases with increasing wavelength of the images used.

Scale effects are inherent in spatial analysis. Quantitative knowledge about them is necessary for properly interpreting and scaling analysis results. The objective of this study was to systematically model patch area scaling and the associated uncertainty. A hybrid approach was taken to tackle the difficulty involved. Recognizing that patch’s size and shape play the key role in shaping its scaling behavior, a function model of patch area scaling based on patch morphology was first conceptually formulated. It was then substantiated by sampling and interpolating in the scale-integrated domain of patch morphology, which is characterized by a one-dimensional size index, namely the relative support range (RSR), and a compactness index, namely filling. The area scaling model obtained unveils a simple consistent scaling pattern of all patches and an overall fading range between 0.12 and 3.16 in terms of RSR. The uncertainty model built exhibits a filling-dependent pattern of the variance of patch area, which can be as large as 0.67 (i.e., 67%) in terms of standard deviation. The models were validated by using them to predict patch and class area scaling of the test patches and landscapes. This study demonstrated the basic feasibility of analytically modeling scaling behavior. It also revealed the uncertainty of scale effects is very significant due to the inevitable randomness in rasterization.

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

A time series of annual land use and land cover (LULC) maps that cover an extended period of time is a key dataset for climatological studies investigating land-atmosphere interaction. Change in LULC can influence regional climate by altering the surface roughness, soil moisture, heat flux partition, and terrestrial carbon storage. Although annual global LULC maps are generated from Moderate-resolution Imaging Spectroradiometer (MODIS) data, the earliest MODIS LULC map is for 2001, which limits the potential time period for climatological analyses. This study produced a continuous series of annual LULC maps of China from 1982 to 2013 using random forest classification of 19 phenological metrics derived from Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) third generation NDVI (NDVI3g) data. The classifier was trained using reference data derived from the MODIS land cover type product (MCD12Q1). Based on a comparison with Google Earth images, the overall accuracy of a simplified eight-class version of our 2012 LULC map is 73.8%, which is not significantly different from the accuracy of the MODIS map of the same year. Our maps indicate that for the three decades studied, the area of croplands and forests in China increased, and the area of grasslands decreased. These annual maps of land cover will be an important dataset for future climate studies, and the methodologies used in this study can be applied to other geographical regions where availability of continuous time series of LULC maps is limited. [ABSTRACT FROM AUTHOR]

West African savannas are experiencing rapid land cover change that threatens biodiversity and affects ecosystem productivity through the loss of habitat and biomass, and carbon emissions into the atmosphere exacerbating climate change effects. Therefore, reducing carbon emissions from deforestation and forest degradation in these areas is critical in the efforts to combat climate change. For such restorative actions to be successful, they must be grounded on a clear knowledge of the extent to which climate change affects carbon storage in soil and biomass according to different land uses. The current study was undertaken in semi-arid savannas in Dano, southwestern Burkina Faso, with the threefold objective of: (i) identifying the main land use and land cover categories (LULCc) in a watershed; (ii) assessing the carbon stocks (biomass and soil) in the selected LULCc; and (iii) predicting the effects of climate change on the spatial distribution of the carbon stock. Dendrometric data (Diameter at Breast Height (DBH) and height) of woody species and soil samples were measured and collected, respectively, in 43 plots, each measuring 50 × 20 m. Tree biomass carbon stocks were calculated using allometric equations while soil organic carbon (SOC) stocks were measured at two depths (0–20 and 20–50 cm). To assess the impact of climate change on carbon stocks, geographical location records of carbon stocks, remote sensing spectral bands, topographic data, and bioclimatic variables were used. For projections of future climatic conditions, predictions from two climate models (MPI-ESM-MR and HadGEM2-ES) of CMIP5 were used under Representative Concentration Pathway (RCP) 8.5 and modeling was performed using random forest regression. Results showed that the most dominant LULCc are cropland (37.2%) and tree savannas (35.51%). Carbon stocks in woody biomass were higher in woodland (10.2 ± 6.4 Mg·ha−1) and gallery forests (7.75 ± 4.05 Mg·ha−1), while the lowest values were recorded in shrub savannas (0.9 ± 1.2 Mg·ha−1) and tree savannas (1.6 ± 0.6 Mg·ha−1). The highest SOC stock was recorded in gallery forests (30.2 ± 15.6 Mg·ha−1) and the lowest in the cropland (14.9 ± 5.7 Mg·ha−1). Based on modeling results, it appears clearly that climate change might have an impact on carbon stock at horizon 2070 by decreasing the storage capacity of various land units which are currently suitable. The decrease was more important under HadGEM2-ES (90.0%) and less under MPI-ESM-MR (89.4%). These findings call for smart and sustainable land use management practices in the study area to unlock the potential of these landscapes to sequestering carbon.

Water quality of a river is a function of surrounding environment and land use due to its connectivity with land, resulting in pollutants finding their way through land. This necessitates a spatially explicit study of river ecology. The paper presents a pioneer study to establish and explore the linkage between land use and water quality of river Ganga in Varanasi district. The land use land cover (LULC) map of 20 km of river stretch for buffer radii of 1000 m in Varanasi revealed that riparian vegetation is negligible in the district. The hierarchical cluster analysis of LULC data suggested that there are two major land use categories, viz., urban and agriculture. The land use wise principal component analysis (PCA) suggested that urbanized areas are major contributor of metals, whereas agricultural land contributes organic matter into the river. The Spearman correlation study revealed that with rising urbanization, the pollutant load into the river increased compared to that from agricultural land use. The statistical analysis of the data clearly concluded that water quality of river Ganga at Varanasi was a function of adjacent land use. The study provides an insight anticipating the Indian government to embrace the relationship of land use to river water quality while formulating policies for the upcoming River Regulation Zone. [ABSTRACT FROM AUTHOR]

The Midwest United States produces around one-quarter of the world's grain supply. The demand for corn ethanol is likely to cause a shift toward greater corn planting. To be prepared for the potential impacts of increased corn production, we need a better understanding of the current state of ecosystem services in this region. In this article, we describe a unique procedure for developing a dataset containing multiple variables useful in modeling ecological responses and tradeoffs. We demonstrate how to construct a detailed land cover classification and link it to yield and agricultural practices. We used the 2001 National Land Cover Database (NLCD) to spatially constrain the datasets during overlay analysis. With this method, we found that the percent agreement between classifications was frequently greater than 80%, indicating little change to the original base layer accuracies. Using three different land cover datasets, we were able to add 18 classes for agriculture and 155 classes for natural cover. We then linked variables for yield, fertilizer, and pesticide application rates, field residue, irrigation percentages, and tillage practices to the land cover data. The final Midwest dataset contained 15.5 million grid values and 15 variables. Capturing the land cover and land management information at the 30-m grid scale allows for aggregation and modeling of the ecosystem services at a variety of spatial scales. As a final step, we demonstrate a tradeoff evaluation between corn yield and nitrogen loadings using our dataset. The effort required to develop the Midwest dataset was greater than initially anticipated; however, the benefit of being able to calculate derivative variables and add new variables justifies the time expenditure needed to create such a detailed database. [ABSTRACT FROM AUTHOR]

This article presents a study which examined the spatial pattern of land-use and land-cover (LULC) change in the coffee growing area in India's Western Ghats. It makes use of image-processing techniques to examine the spatial pattern of LULC which occurred in the area during the international coffee crisis in the 1990s. The study employed the qualitative research methods, such as archival studies and interviews of individuals with knowledge, in determining the driving forces of these changes. It observes that when coffee prices increase farmer increase the land area for coffee cultivation resulting in land degradation in rice paddies.

• Contributions and complementarities of the different frequencies for detecting 4 types of land use (and two states of bare soils. • The use of one mono-frequency images provides limited overall results. • A significant improvement in the classifications by fusing images delivered by optical and radar sensors, for most of the classes studied. • The complementary nature of the contributions made by L-band and optical waves. The monitoring of different crops (cultivated plots) and types of surface (bare soils, etc.) is a crucial economic and environmental issue for the management of resources and human activity. In this context, the objective of this study is to evaluate the contribution of multispectral satellite imagery (optical and radar) to land use and land cover classification. Object-oriented supervised classifications, based on a Random Forest algorithm, and majority zoning post-processing are used. This study emerges from the experiment on multi-sensor crop monitoring (MCM'10, Baup et al., 2012) conducted in 2010 on a mixed farming area in the southwest of France, near Toulouse. This experiment enabled the regular and quasi-synchronous collection of multi-sensor satellite data and in situ observations, which are used in this study. 211 plots with contrasting characteristics (different slopes, soil types, aspects, farming practices, shapes and surface areas) were monitored to represent the variability of the study area. They can be grouped into four classes of land cover: 39 grassland areas, 100 plots of wheat, 13 plots of barley, 20 plots of rapeseed, and 2 classes of bare soil: 23 plots of small roughness and 16 plots of medium roughness. Satellite radar images in the X-, C- and L-bands (HH polarization) were acquired between 14 and 18 April 2010. Optical images delivered by Formosat-2 and corresponding field data were acquired on 14 April 2010. The results show that combining images acquired in the L-band (Alos) and the optical range (Formosat-2) improves the classification performance (overall accuracy = 0.85, kappa = 0.81) compared to the use of radar or optical data alone. The results obtained for the various types of land cover show performance levels and confusions related to the phenological stage of the species studied, with the geometry of the cover, the roughness states of the surfaces, etc. Performance is also related to the wavelength and penetration depth of the signal providing the images. Thus, the results show that the quality of the classification often increases with increasing wavelength of the images used. [ABSTRACT FROM AUTHOR]