Assessing the land use dynamics and thermal environment using geospatial techniques in the industrial city of Chotanagpur Plateau Region, India.

The phenomenon of urban heat island (UHI) is characterized by industrial, economic development, unplanned and unregulated land use as well as a rapid increase in urban population, resulting a warmer inner core in contrast to the surrounding natural environment, thus requiring immediate attention for a sustainable urban environment. This study examined the land use/land cover (LULC) change, pattern of spectral indices (Normalized Difference Vegetation Index, NDVI; Normalized Difference Water Index, NDWI; Normalized Difference Built-up Index, NDBI and Normalized Difference Bareness Index, NDBaI), retrieval of land surface temperature (LST) and Urban Thermal Field Variance Index (UTFVI) as well as identification of UHI from 2000 to 2022. The relationship among LST and LULC spectral indices was estimated using Pearson's correlation coefficient. The Landsat-5 (TM) and Landsat-8 (OLI/TIRS) satellite data have been used, and all tasks were completed through various geospatial tools like ArcGIS 10.8, Google Earth Engine (GEE), Erdas Imagine 2014 and R-Programming. The result of this study depicts over the period that built-up area and water bodies increased by 119.78 and 35.70%, respectively. On the contrary, fallow and barren decreased by 55.33 and 32.31% respectively over the period. The mean and maximum LST increased by 3.61 °C and 2.62 °C, and the study reveals that a high concentration of UTFVI and UHI in industrial areas, coal mining sites and their surroundings, but the core urban area has observed low LST and intensity of UHI than the peripheral areas due to maintained vegetation cover and water bodies. An inverse relationship has been found among LST, NDVI and NDWI, while adverse relationships were observed among LST, NDBI and NDBaI throughout the period. Sustainable environment planning is needful for the urban area, as well as the periphery region and plantation is one of the controlling measures of LST and UHI increment. This work provides the scientific base for the study of the thermal environment which can be one of the variables for planning of Asansol City and likewise other cities of the country as well as the world.

Impacts of future climate and land use/land cover change on urban runoff using fine-scale hydrologic modeling.

Future changes in land use/land cover (LULC) and climate (CC) affect watershed hydrology. Despite past research on estimating such changes, studies on the impacts of both these nonstationary stressors on urban watersheds have been limited. Urban watersheds have several important details such as hydraulic infrastructure that call for fine-scale models to predict the impacts of LULC and CC on watershed hydrology. In this paper, a fine-scale hydrologic model-Personal Computer Storm Water Management Model (PCSWMM)-was applied to predict the individual and joint impacts of LULC changes and CC on surface runoff attributes (peak and volume) in 3800 urban subwatersheds in Midwest Florida. The subwatersheds a range of characteristics in terms of drainage area, surface imperviousness, ground slope and LULC distribution. The PCSWMM also represented several hydraulic structures (e.g., ponds and pipes) across the subwatersheds. We analyzed changes in the runoff attributes to determine which stressor is most responsible for the changes and what subwatersheds are mostly sensitive to such changes. Six 24-h design rainfall events (5- to 200-year recurrence intervals) were studied under historical (2010) and future (year 2070) climate and LULC. We evaluated the response of the subwatersheds in terms of runoff peak and volume to the design rainfall events using the PCSWMM. The results indicated that, overall, CC has a greater impact on the runoff attributes than LULC change. We also found that LULC and climate induced changes in runoff are generally more pronounced in greater recurrence intervals and subwatersheds with smaller drainage areas and milder slopes. However, no relationship was found between the changes in runoff and original subwatershed imperviousness; this can be due to the small increase in urban land cover projected for the study area. This research helps urban planners and floodplain managers identify the required strategies to protect urban watersheds against future LULC change and CC.

Exploring the relationship between land use/land cover and apparent temperature in China (1996-2020): implications for urban planning.

In recent decades, rising air temperatures (AT) and apparent temperatures (AP) have posed growing health risks. In the context of China's rapid urbanization and global climate change, it is crucial to understand the impact of urban land use/land cover (LULC) changes on AP. This study investigates the spatial distribution and long-term variation patterns of AT and AP, using data from 834 meteorological stations across China from 1996 to 2020. It also explores the relationship between AT, AP, and LULC in the urban core areas of 30 major cities. Study reveals that AT and AP exhibit overall high spatial similarity, albeit with greater spatial variance in AP. Notably, regions with significant disparities between the two have been identified. Furthermore, it's observed that the spatial range of high AP change rates is wider than that of AT. Moreover, the study suggests a potential bivariate quadratic function relationship between ΔT (the difference between AT and AP) and Wa_ratio and Ar_ratio, indicating the presence of a Least Suitable Curve (LSC), [Formula: see text]. Urban LULC planning should carefully avoid intersecting with this curve. These findings can provide valuable insights for urban LULC planning, ultimately enhancing the thermal comfort of urban residents.

Explaining the role of land use changes on land surface temperature in an arid and semi-arid metropolitan area with multi-scale spatial regression analysis.

Urban Heat Islands (UHIs), Land Surface Temperature (LST), and Land Use Land Cover (LULC) changes are critical environmental concerns that require continuous monitoring and assessment, especially in cities within arid and semi-arid (ASA) climates. Despite the abundance of research in tropical, Mediterranean, and cold climates, there is a significant knowledge gap for cities in the Middle East with ASA climates. This study aimed to examine the effects of LULC change, population, and wind speed on LST in the Mashhad Metropolis, a city with an ASA climate, over a 30-year period. The research underscores the importance of environmental monitoring and assessment in understanding and mitigating the impacts of urbanization and climate change. Our research combines spatial regression models, multi-scale and fine-scale analyses, seasonal and city outskirts considerations, and long-term change assessments. We used Landsat satellite imagery, a crucial tool for environmental monitoring, to identify LULC changes and their impact on LST at three scales. The relationships were analyzed using Ordinary Least Squares (OLS) and Spatial Error Model (SEM) regressions, demonstrating the value of these techniques in environmental assessment. Our findings highlight the role of environmental factors in shaping LST. A decrease in vegetation and instability of water bodies significantly increased LST over the study period. Bare lands and rocky terrains had the most substantial effect on LST. At the same time, built-up areas resulted in Urban Cooling Islands (UCIs) due to their lower temperatures compared to surrounding bare lands. The Normalized Difference Vegetation Index (NDVI) and Dry Bare-Soil Index (DBSI) were the most effective indices impacting LST in ASA regions, and the 30×30 m2 micro-scale provides more precise results in regression models, underscoring their importance in environmental monitoring. Our study provided a comprehensive understanding of the relationship between LULC changes and LST in an ASA environment, contributing significantly to the literature on environmental change in arid regions and the methodologies for monitoring such changes. Future research should aim to validate and expand additional LST-affecting factors and test our approach and findings in other ASA regions, considering the unique characteristics of these areas and the importance of tailored environmental monitoring and assessment approaches.

Spatio-temporal prediction of land use and land cover change in Bahi (Manyoni) Catchment, Tanzania, using multilayer perceptron neural network and cellular automata-Markov chain model.

Evaluation of land use and land cover (LULC) change is among vital tools used for tracking environmental health and proper resource management. Remote sensing data was used to determine LULC change in Bahi (Manyoni) Catchment (BMC) in central Tanzania. Landsat satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used, and support vector machine (SVM) algorithm was applied to classify the features of BMC. The obtained kappa values were 0.74, 0.83 and 0.84 for LULC maps of 1985, 2005 and 2021, respectively, which indicates the degree of accuracy from produced being substantial to almost perfect. Classified maps along with geospatial, socio-economic and climatic drivers with sufficient explanatory power were incorporated into MLP-NN to produce transition potential maps. Transition maps were subsequently used in cellular automata (CA)-Markov chain model to predict future LULC for BMC in immediate-future (2035), mid-future (2055) and far-future (2085). The findings indicate BMC is expected to experience significant expansion of agricultural lands and built land from 31.89 to 50.16% and 1.48 to 9.1% from 2021 to 2085 at the expense of open woodland, shrubland and savanna grassland. Low-yield crop production, water scarcity and population growth were major driving forces for rapid expansion of agricultural lands and overall LULC in BMC. The findings are essential for understanding the impact of LULC on hydrological processes and offer insights for the internal drainage basin (IDB) board to make necessary measures to lessen the expected dramatic changes in LULC in the future while sustaining harmonious balance with livelihood activities.

Spatial soil loss prediction impacted by long-term land use/land cover change: a case study of Swat District.

Soil erosion is a destructive consequence of land degradation caused by deforestation, improper farming practices, overgrazing, and urbanization. This irreversible effect negatively impacts the limited renewable soil resource, causing soil truncation, reduced fertility, and unstable slopes. To address the anticipation of erosion modulus resulting from long-term land use and land cover (LULC) changes, a study was conducted in the Swat District of Khyber Pakhtunkhwa (Kpk), Pakistan. The study aimed to predict and evaluate soil erosion concerning these changes using remote sensing (RS), geographic information systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE) model. We also evaluated the impact of the Billion Tree Tsunami Project (BTTP) on soil erosion in the region. Model inputs, such as rainfall erosivity factor, topography factor, land cover and management factor, and erodibility factor, were used to calculate soil erosion. The results revealed that significant soil loss occurred under 2001, 2011, and 2021 LULC conditions, accounting for 67.26%, 61.78%, and 65.32%, falling within the category of low erosion potential. The vulnerable topographical features of the area indicated higher erosion modulus. The maximum soil loss rates observed in 2001, 2011, and 2021 were 80 t/ha-1/year-1, 120 t/ha-1/year-1, and 96 t/ha-1/year-1, respectively. However, the observed reduction in soil loss in 2021 as compared to 2001 and 2011 suggests a positive influence of the BTTP on soil conservation efforts. This study underscores the potential of afforestation initiatives like the BTTP in mitigating soil erosion and highlights the significance of environmental conservation programs in regions with vulnerable topography.

Analysis of land use and land cover change using machine learning algorithm in Yola North Local Government Area of Adamawa State, Nigeria.

The dynamic use of land that results from urbanization has an impact on the urban ecosystem. Yola North Local Government Area (Yola North LGA) of Adamawa state, Nigeria, has experienced tremendous changes in its land use and land cover (LULC) over the past two decades due to the influx of people from rural areas seeking for the benefits of its economic activities. The goal of this research is to develop an efficient and accurate framework for continuous monitoring of land use and land cover (LULC) change and quantify the transformation in land use and land cover pattern over a specific period (between 2002 and 2022). Land sat images of 2002, 2012, and 2022 were obtained, and the Support Vector Machine classification method was utilized to stratify the images. Land Change Modeler (LCM) tool in Idrissi Selva software was then used to analyze the LULC change. SVM produced a good classification result for all three years, with 2022 having the highest overall accuracy of 95.5%, followed by 2002 with 90% and 2012 with 87.7% which indicates the validity of the algorithm for future predictions. The results showed that severe land changes have occurred over the course of two decades in built-up (37.32%), vegetation (forest, scrubland, and grassland) (-3.27%), bare surface (-33.47%), and water bodies (-0.59%). Such changes in LULC could lead to agricultural land lost and reduced food supply. This research develops a robust framework for continuous land use monitoring, utilizing machine learning and geo-spatial data for urban planning, natural resource management, and environmental conservation. In conclusion, this study underscores the efficacy of support vector machine algorithm in analyzing complex land use and land cover changes.

LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia.

Recurrent changes recorded in LULC in Guna Tana watershed are a long-standing problem due to the increase in urbanization and agricultural lands. This research aims at identifying and predicting frequent changes observed using support vector machines (SVM) for supervised classification and cellular automata-based artificial neural network (CA-ANN) models for prediction in the quantum geographic information systems (QGIS) plugin MOLUSCE. Multi-temporal spatial Landsat 5 Thematic Mapper (TM) imageries, Enhanced Thematic Mapper plus 7 (ETM+), and Landsat 8 Operational Land Imager (OLI) images were used to find the acute problem the watershed is facing. Accuracy was assessed using the confusion matrix in ArcGIS 10.4 produced from ground truth data and Google Earth Pro. The results acquired from kappa statistics for 1991, 2007, and 2021 were 0.78, 0.83, and 0.88 respectively. The change detection trend indicates that urban land cover has an increasing trend throughout the entire period. In the future trend, agriculture land may shoot up to 86.79% and 86.78% of land use class in 2035 and 2049. Grassland may attenuate by 0.03% but the forest land will substantially diminish by 0.01% from 2035 to 2049. The increase of land specifically was observed in agriculture from 3128.4 to 3130 km2. Judicious planning and proper execution may resolve the water management issues incurred in the basin to secure the watershed.

Assessing the status and spatial-temporal dynamics of the Bamenda Mountains (BM), North West region of Cameroon.

Change in land use and land cover (LULC) contributes in worsening ecological issues. Studying the trends of change in land use is highly significant to deal with global climate change and sustainable development. The aim of this paper is to evaluate the spatial-temporal dynamics of LULC of the Bamenda Mountains (BM) in the North West region of Cameroon, over a period of 34 years (1988-2022) and predict 34 years (2022-2056) future land use scenario of this site using time series satellite imagery (MSS, TM, ETM+, and OLI-TIRS) and ancillary data and to comprehend the driving forces of land use/land cover change (LULCC). The trends of LULCC were quantified; LULC maps were derived by classifying time series satellite images. Six LULC categories were identified during the study period (1988-2022). The research revealed a significant LULCC of the BM which can be justified by increase in the human population observed in the study area and the desire to extend agricultural lands to sustain the growing population. Overall, cultivated area 5684 ha (10.47%), 10680 ha (19.57 %), and 15163 ha (27.78%) and built-up area 449 ha (0.83%), 996 ha (1.83%), and 3242 ha (5.94%) for the study years 1988, 2003, and 2022, respectively, were all on the increase throughout the study period at the expense of other land cover types. The predicted figures of 2056 showed a continuous reduction of montane forest and savanna: 2401.92 ha (4.40%) and 25,862.67 ha (47.39%), respectively. Bare area is expected to drop in 2056 (2905.92 ha (5.32%)). The above decrease, when compared to 2022 figures, represents a loss of 3.97%, 4.53%, and 0.57%, respectively. The losses observed are gained by built-up and cultivated land (5.72% and 3.39%, respectively), covering surfaces areas of 6364.89 ha (11.66%) and 17,008.56 ha (31.17%), respectively. The above findings suggest that population growth is likely the major menace to the natural environment. It is thus safe to say that substantial LULCC was observed throughout the study period and will undoubtedly continue if nothing is done. This necessitates urgent measures such as reforestation and afforestation, encouraging off-farm activities and even improving technologies to combat the rate of forest degradation of the BM. Additionally, rebuilding trust between the French and English Cameroons through dialogue is premodial, to end the curent conflictual civil war and lessen the landscape configuration in Bamenda.

Modeling land use/cover change based on LCM model for a semi-arid area in the Latian Dam Watershed (Iran).

The monitoring and modeling of changes, based on a time-series LULC approach, is fundamental for planning and managing regional environments. The current study analyzed the LULC changes as well as estimated future scenarios for 2027 and 2037. To achieve accuracy in predicting LULC changes, the Land Change Modeler (LCM) was used for the Latian Dam Watershed, which is located approximately in the northeast of Tehran. The LULC time-series technique was specified utilizing four atmospherically endorsed surface reflectance Landsat images for the years t1 (1987), t2 (1998), t3 (2007), and t4 (2017) to authenticate the LULC predictions, so to obtain estimates for t5 (2027) and t6 (2037). The LULC classes identified in the watershed were water bodies, build-up areas, vegetated areas, and bare lands. The dynamic modeling of the LULC was based on a multi-layer perceptron (MLP), the neural network in LCM, which presented good results with an average accuracy rate equivalent to 84.89 percent. The results of the LULC change analysis showed an increase in the build-up area and a decrease in bare lands and vegetated areas within the duration of the study period. The results of this research could help in the formulation of public policies designed to conserve environmental resources in the Latian Dam Watershed and, consequently, minimize the risks of the fragmentation of orchards and vegetated areas. Also, careful regional planning ensuring the preservation of natural landscapes and open spaces is critical to creating a resilient regional environment and sustainable development.

The nexus between land use, land cover dynamics, and soil erosion: a case study of the Temecha watershed, upper Blue Nile basin, Ethiopia.

At the current times, soil erosion is the major problem that affects land and water resources, especially in Ethiopia's highlands. Due to the dynamics of land use land cover change, land degradation, and soil erosion increase significantly and result in a loss of fertile soil every year and lead reduction in agricultural production. This study was therefore designed to explore the land use land cover (LULC) dynamics from 1986 to 2020, to estimate mean annual soil erosion rates and identify erosion hotspot areas from 1986 to 2020, and finally, to evaluate the impacts of land use land cover change on soil loss of 1986 to 2020. For this, Landsat imageries of 4 years from 1986 to 2020 were used. Maximum likelihood supervised classification methods were used to classify LULCs. The dynamics of LULC change were used as an input for measuring soil loss by employing the combination of geospatial technologies with the revised universal soil loss equation (RUSLE). The LULC maps of 1986, 1997, 2009, and 2020 were used for identifying crop management (C) factor and conservation practice (P) factor. Rainfall erosivity factor (R), soil erodibility factor (K), and slope length and steepness factor (LS) were also used as sources of data. Based on the five factors, soil erosion intensity maps were prepared for each year. Results showed that the annual soil loss in the watershed ranged from 0 to 3938.66 t/ha/year in 1986, 0 to 4550.94 t/ha/year in 1997, 0 to 5011.21 t/ha/year in 2009, and 0 to 6953.23 t/ha/year in 2020. The annual soil loss for the entire watershed was estimated at 36.889, 42.477, 47.805, and 48.048 t/ha/year in 1986, 1997, 2009, and 2020, respectively. The mean soil loss of 1986, 1997, 2009, and 2020 was higher in cultivated land followed by shrub land, grazing land, and forest land. Mean soil loss increased from 1986 to 1997, from 1997 to 2009, and from 2009 to 2020. This is because of the expansion of agricultural land at the expense of grazing land and shrub land. Therefore, urgent soil and water conservation practices should be made in hotspot areas.

Impacts of land use/land cover and soil property changes on soil erosion in the black soil region, China.

Soil erosion (SE) is seriously threatening grain production and the ecological environment in the black soil region. Understanding the impact of changes in land use/land cover (LULC) and soil properties on SE is critical for agricultural sustainability and soil management. However, the contribution of soil property changes to SE is often ignored in existing studies. This study analyzed changes in LULC and soil properties from 1980 to 2020 in the black soil region, China. Then, the revised universal soil loss equation was used to explore the spatiotemporal changes of SE from 1980 to 2020. Finally, the contribution of LULC change and soil property change to SE was separated by scenario comparison. The results showed that cropland increased (by 24,157 km2) at the expense of grassland and forest from 1980 to 2020. Sand in cropland decreased by 21.95%, while the silt, clay, and SOC increased by 21.37%, 1.43%, and 15.38%, respectively. Soil erodibility in cropland increased greatly (+9.85%), while in forest and grassland decreased (-6.05% and -4.72%). LULC change and soil properties change together aggravated SE in the black soil region. LULC change and soil property change resulted in a 22% increase in SE, of which LULC change resulted in a 14% increase, and soil property change resulted in an 8% increase. Agricultural development policy was the main reason driving LULC change. The combination of LULC change, climatic factors, and long-term tillage resulted in changes in soil properties. Ecosystem management and policy can reduce SE through vegetation restoration and soil improvement. This study can provide important references for soil conservation and agricultural development in the black soil region.

A Comparative Study of Various Land Use and Land Cover Change Models to Predict Ecosystem Service Value.

Ecosystem services are closely related to human well-being and are vulnerable to high-intensity human land-use activities. Understanding the evolution of land use and land cover (LULC) changes and quantifying ecosystem service value (ESV) are significant for sustainable development. In this study, we used land use and land cover data and other data from 2000 to 2020 to analyze the evolution of land use and land cover and ESV in Tongliao, China. With the goal of exploring the characteristics of different cellular automata (CA)-based models, CA-Markov, Future Land Use Simulation (FLUS), and Patch-generating Land Use Simulation (PLUS) models were used to simulate future land use and land cover, and the results were verified and compared. Considering the impacts of policies for capital farmland (CF) and ecological protection red line (EPRL) in the context of territorial spatial planning, four scenarios (inertial development, S1; CF, S2; EPRL, S3; EPRL and CF, S4) were set. The results showed that from 2000 to 2020, farmland and built-up land increased the most (341.18 km2 and 220.56 km2), while grassland had the largest decrease (380.08 km2). The main mutual transitions were from grassland and farmland. The total ESV showed a decreasing trend (from 52,364.56 million yuan to 51,620.62 million yuan). The simulation results for 2035 under four scenarios were similar, where farmland would decrease the most (96.81 km2). The ESV in 2035 would decrease from 51,620.62 million yuan to 51,541.12 million. In addition, under scenarios for the impact of policy, the land showed a trend of scattered expansion. This study provides a scientific basis for making regional sustainable development policy decisions and implementing ecological environmental protection measures.

The impacts of LULC and climate change scenarios on the hydrology and sediment yield of Rib watershed, Ethiopia.

Watershed-scale hydrology and soil erosion are the main environmental components that are greatly affected by environmental perturbations such as climate and land use and land cover (LULC) changes. The purpose of this study is to assess the impacts of scenario-based LULC change and climate change on hydrology and sediment at the watershed scale in Rib watershed, Ethiopia, using the empirical land-use change model, dynamic conversion of land use and its effects (Dyna-CLUE), and soil and water assessment tool (SWAT). Regional climate model (RCM) with Special Report on Emission Scenarios (SRES) and Representative Concentration Pathway (RCP) outputs were bias-corrected and future climate from 2025 to 2099 was analyzed to assess climate changes. Analysis of the LULC change indicated that there has been a high increase in cultivated land at the expense of mixed forest and shrublands and a low and gradual increase in plantation and urban lands in the historical periods (1984-2016) and in the predictions (2016-2049). In general, the predicted climate change indicated that there will be a decrease in precipitation in all of the SRES and RCP scenarios except in the Bega (dry) season and an increase in temperature in all of the scenarios. The impact analysis indicated that there might be an increase in runoff, evapotranspiration (ET), sediment yield, and a decrease in lateral flow, groundwater flow, and water yield. The changing climate and LULC result in an increase in soil erosion and changes in surface and groundwater flow, which might have an impact on reducing crop yield, the main source of livelihood in the area. Therefore, short- and long-term watershed-scale resource management activities have to be designed and implemented to minimize erosion and increase groundwater recharge.

Modeling the impacts of land use/land cover change on meteorology and air quality during 2000-2018 in the Yangtze River Delta region, China.

The land use/land cover (LULC) change in the fast-developing city clusters of China exhibits impacts on both the meteorology and air quality. However, this effect, especially in the Yangtze River Delta (YRD), has not been well quantified. In this study, the LULC data are extracted from Landsat satellite imageries for year 2000 and 2018 for the YRD region. The Weather Research and Forecasting with Chemistry (WRF/Chem) model is applied to investigate the impact of historical LULC change on regional meteorology and air pollution over the YRD region during the past two decades. Two simulation scenarios are performed with two sets of LULC data to represent the pre-urbanization (LULC of year 2000) and the most recent urban pattern (LULC of year 2018). Results indicate that rapid urbanization leads to an increase of monthly mean 2-m temperature by 0.4-2.1 °C but decrease of the 10-m wind speed by 0.5-1.3 m/s in urban areas; the maximum increase of daytime planetary boundary layer height (PBLH) in July and November is 289 and 132 m, respectively. Affected by favorable changes in the meteorological conditions due to LULC change, the PM2.5 concentrations in most urban areas show a decreasing trend, especially during the nighttime in summer. On the contrary, surface ozone (O3) concentration in urban areas has increased by 7.2-9.8 ppb in summer and 1.9-2.1 ppb in winter. Changes in O3 concentration are inversely proportional to changes in NOx and the spatial distribution of PM2.5. Areas with higher O3 concentration are consistent with areas of higher temperature and lower wind speed. Our findings reveal that LULC changes during the past years bring observable changes in air pollutant concentrations, which should not be neglected in the YRD region regarding air quality trends as well as policy evaluations under the warming threat.

Land-use/land-cover changes and implications in Southern Ethiopia: evidence from remote sensing and informants.

Understanding land use/cover (LULC) changes and their impacts on the catchment are imperative for proper land management. Hence, useful information concerning responses to LULC changes becomes important to minimize negative impacts on future land uses. The aim of the study was to evaluate the LULC changes and consequences of the change at Bilate catchment from 1986 to 2018. The LULC change evaluations were undertaken by using Landsat images of 1986, 2002 and 2018. Supervised image classification was employed to map the land cover classes. Informant interviews and group discussions with field observations were used to identify the consequences of the changes. Over the past periods, built-up areas, water bodies, cultivation, and barren lands have increased by 0.97, 0.13, 9.27, and 1.36%, respectively. However, the forest and grazing lands have decreased by 8.56 and 3.18% respectively. Exhaustive land cultivation without appropriate management and cultivation of sloppy lands have increased soil erosion and sediment yield to water bodies. A decline in crop yields, livestock products and numbers, and fish population in Lake Abaya are the major implications of LULC change in the catchment. Therefore, to ensure sustainable land use, responsible bodies commit and work closely with communities through participatory approaches.

Land use and land cover dynamics and traditional agroforestry practices in Wonchi District, Ethiopia.

BACKGROUND: Investigating the land use and land cover (LULC) dynamics and the status of traditional agroforestry practices provide important data for policymakers. The main objective of this study was to assess the LULC dynamics and traditional agroforestry practices among smallholder farmers across the two agro-ecological zones in Wonchi District of Ethiopia. METHODS: Landsat images were acquired from Earth Explorer, and changes in LULC were quantified with three Landsat sensors in the three time-series (1985, 2001, and 2019). Supervised classification with maximum likelihood technique was employed using ERDAS Imagine and ArcGIS. A ground survey was conducted with 100 key informants who were selected from 10 sites using a purposive sampling method. The collected data were subjected to direct matrix ranking, use-value analysis of most important multipurpose plant species, and semi-structured interviews were conducted for qualitative analysis. RESULTS: In total, 103 agroforestry plant species belonging to 44 families were identified in Wonchi District, of which 74 were indigenous including seven endemic and 29 exotic species. The highest species (13) were recorded in the Fabaceae family. About 61% of species were reported in the midland agro-ecological zone. A mixed farming system was the most frequently (56%) reported source of income. The results of LULC changes from 1985 to 2019 showed that the agroforestry cover increased from 31.1% to 34.9% and settlement including road construction increased from 12.5% to 31.6% of the total area with an annual rate change of 0.3% and 2.7%, respectively. These changes corresponded with a decreasing trend of the forest, cropland, water body, and shrub at a rate of 4.7%, 1.3%, 0.8%, and 0.5%, respectively. The LULC changes were more pronounced in the highlands than in the midlands of Wonchi District. Expansion of settlement and tenure policy change are the main drivers for these changes in the area. The authors recommended that protecting and planting indigenous and multipurpose plant species is essential as restoration techniques for all degraded land-use types. Therefore, strengthening agroforestry practices and land-use planning is urgently needed for achieving multiple goals.

Long-term water quality assessments under changing land use in a large semi-arid catchment in South Africa.

Increasing nutrient loads from land use and land cover (LULC) change degrade water quality through eutrophication of aquatic ecosystems globally. The Vaal River Catchment in South Africa is an agriculturally and economically important area where eutrophication has been a problem for decades. Effective mitigation strategies of eutrophication in this region require an understanding of the relationship between LULC change and water quality. This study assessed the long-term impacts of LULC changes on nitrate (NO3-N) and orthophosphate (PO4-P) pollution in the lower Vaal River Catchment between 1980 and 2018. Multi-year LULC was mapped from Landsat imagery and changes were determined. Long-term trends in NO3-N and PO4-P loads and concentrations in river water samples were analysed, while multi-year LULC data were ingested into the Soil and Water Assessment Tool (SWAT) to simulate the impacts of LULC changes in NO3-N and PO4-P loads. Main LULC changes included an increase in the irrigated area by 262% and in built-up area by 33%. This occurred at the expense of cultivated dryland fields and rangelands. In situ data analysis showed that at the catchment inlet, PO4-P concentration and loads significantly increased, while NO3-N concentration and loads decreased between 1980 and 2018. At the catchment outlet, only PO4-P loads increased, while NO3-N loads and concentrations remained the same. SWAT simulations at the Hydrologic Response Unit scale showed that irrigated land was the largest contributor to NO3-N leaching per ha. Aggregation of nutrient loads by LULC type showed increased nutrient loads from irrigated and built-up areas over time, while loads from dryland areas decreased. At catchment scale, dryland remained an important contributor of the annual nutrient loads total because of its large area. In future, research efforts should focus on crop management practices to reduce nutrient loads.

Legalization of Cannabis and Agricultural Frontier Expansion.

The environmental impacts of cannabis cultivation have been an issue of growing concern, with legalization often framed as a means to introduce regulations that hinder damaging practices. However, the concept of frontier expansion presents the possibility that the widespread establishment of this new industry may institute an additional source of habitat encroachment. Here, through geospatial analysis, we employ Colorado as a case study to investigate the distribution of licensed recreational cannabis cultivators, potential habitat infringement of threatened and endangered species, and LULC change. From 2011 to 2016, licensed cannabis cultivation has resulted in over 67 ha of LULC change toward more developed land uses. In addition, nearly 15 km of new fencing was constructed establishing over 38 ha of fenced areas, and nearly 60 ha of vegetation was cleared. Much of this cannabis-driven LULC change is identified within the habitats of threatened and endangered species, as well as areas recognized as containing high biodiversity values with the potential for conservation. Thus, notable cannabis-driven frontier expansion is evident. Cannabis-driven LULC change is found to be primarily produced by outdoor and greenhouse facilities, as well as operations utilizing mixed-cultivation methods in rural areas. Therefore, policy instruments that inter alia encourage indoor cannabis cultivation in urban areas are recommended and discussed.

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

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