Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.

Identifying critical source areas of non-point source pollution to enhance water quality: Integrated SWAT modeling and multi-variable statistical analysis to reveal key variables and thresholds

By integrating soil and water assessment tool (SWAT) modeling and land use and land cover (LULC) based multi-variable statistical analysis, this study aimed to identify driving factors, potential thresholds, and critical source areas (CSAs) to enhance water quality in southern Alabama and northwest Florida's Choctawhatchee Watershed. The results revealed the significance of forest cover and of the lumped developed areas and cultivated crops ("Source Areas") in influencing water quality. The stepwise linear regression analysis based on self-organizing maps (SOMs) showed that a negative correlation between forest percent cover and total nitrogen (TN), organic nitrogen (ORGN), and organic phosphorus (ORGP), highlighting the importance of forests in reducing nutrient loads. Conversely, Source Area percentage was positively correlated with total phosphorus (TP) loads, indicating the influence of human activities on TP levels. The receiver operating characteristic (ROC) curve analysis determined thresholds for forest percentage and Source Area percentage as 37.47 % and 20.26 %, respectively. These thresholds serve as important reference points for identifying CSAs. The CSAs identified based on these thresholds covered a relatively small portion (28 %) but contributed 47 % of TN and 50 % of TP of the whole watershed. The study underscores the importance of considering both physical process-based modeling and multi-variable statistical analysis for a comprehensive understanding of watershed management, i.e., the identification of CSAs and the associated variables and their tipping points to maintain water quality.

Spatial transformation of land use and land cover and identification of hotspots using geospatial technology: a case of major industrial zone of eastern India

Technology-driven population expansion is closely linked to land use change. Unregulated mining, urbanization, industrialization, and forest clearing threaten land use and cover. This study used GIS and statistical methods to examine land use and cover changes in eastern India's Asansol-Durgapur Development Authority (ADDA). The Kappa coefficient was used to validate each year's LULC map accuracy. This region is changing rapidly due to industrial and urban development, which might cause environmental issues. Thus, this area is ideal for a scientific land-use change study. The central hypothesis of this study is that the LULC of an industrial area is spatially heterogeneous and that the number of hotspots is gradually increasing in response to the dynamicity of land use change over time and space. Three years (1992, 2007, and 2022) were used to determine the estimated transition rate. Hotspots of land use change were identified using autocorrelation statistics for LULC clustering using Moron's I and Gi Z statistics. The proportion of land encompassed by natural vegetation experienced a decline from 12% in 1992 to 4% in 2022. Similarly, the extent of land occupied by agricultural activities decreased from 47 to 38% during the period spanning from 1992 to 2022. The industrial and coal mining sectors experienced a modest growth rate of 1% during the period spanning from 1992 to 2022. If the current rate of land use change persists, it will gradually and consistently alter the existing landscape. This study's findings can potentially inform strategies to mitigate the adverse impacts of industrialization and urbanization on the region's natural resources.

Using multilayer perceptron and similarity-weighted machine learning algorithms to reconstruct the past: A case study of the agricultural expansion on grasslands in the Uruguayan savannas

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 km2 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 2023;00:1-16. © 2023 SETAC.

Accurate classification of land use and land cover using a boundary-specific two-level learning approach augmented with auxiliary features in Google Earth Engine

Land use land cover (LULC) classification using remote sensing images is a valuable resource in various fields such as climate change, urban development, and land degradation monitoring. The city of Madurai in India is known for its diverse geographical elements and rich heritage, which includes the cultural sport of "Jallikattu": whose main competitor, the zebusare deeply affected by the conversion of their waterbodies and pastures into concrete jungles. Hence, monitoring land degradation is vital in preserving the geography and cultural heritage of the study area, Madurai. The "Landsat 8 Operational Land Imager tier_2 collection_2 Level_2 Surface Reflectance" image was taken for this study. The LULC classification is performed based on the following classes: forest, agriculture, urban, water bodies, uncultivated land, and bare land. The objective of the study is to incorporate auxiliary features to spectral and textural features along with a simple non-iterative clustering (SNIC) segmentation algorithm and implement a boundary-specific two-level learning approach based on support vector machines (SVM) and k nearest neighbors (kNN) classification algorithms. The overall accuracy (OA) of 95.78% and 0 .94 Kappa score (K) were obtained using a boundary-specific two-level model augmented with auxiliary feature and SNIC algorithm in comparison to PB, OB, and OBS, which achieve OA (K) of 81% (0.76), 91% (0.89), and 94.42% (0.92), respectively. The results demonstrate a notable enhancement in overall classification accuracy when augmenting the features and refining classification decisions using a boundary-specific two-level learning approach.

Landscape resistance index aiming at functional forest connectivity

Resistance models may quantify the ability of the landscape to impede species' movement and represent suitable habitats. Moreover, the performance of resistance models parameterized by land-use/land cover attributes evidence that the integrity of the environments subject to urban sprawl is poorly understood. In this sense, the study assumed we could identify the forest functional connectivity in a landscape considering the disparity in the landscape mosaic. In this context, we sought to develop a landscape resistance index through structural equation modeling (SEM), supported by the criteria of heat emission, biomass, and anthropogenic barriers, obtained by remote sensing, called observed variables. The landscape studied in the Green Belt Biosphere Reserve of São Paulo has significant remnants of the Atlantic Forest, a biodiversity hotspot. However, our results indicated criteria variability in the landscape modeled through the SEM, obtaining a significant adjustment of the landscape resistance index, with comparative fit index (CFI) of 1.00 and root mean square error of approximation (RMSEA) of 0.00. The index reflects the resistance levels of the land use/land cover, expressed by the class interval, ranging from 0% (1.73) to 100% (493.88), with the highest values associated with the anthropized uses and forest isolation. Thus, our index based on environmental attributes reflects the structure of functional forest connectivity and offers a framework to design forest corridors across landscapes.

Implications of CO2 emissions on the main land and forest uses in the Brazilian Amazon

The emission of soil carbon dioxide (CO₂) in agricultural areas is a process that results from the interaction of several factors such as climate, soil, and land management practices. Agricultural practices directly affect the carbon dynamics between the soil and atmosphere. Herein, we evaluated the temporal variability (2020/2021 crop season) of soil CO₂ emissions and its relationship with related variables, such as the CO₂ flux model, enhanced vegetation index (EVI), gross primary productivity (GPP), and leaf area index (LAI) from orbital data and soil temperature, soil moisture, and soil CO₂ emissions from in situ collections from native forests, productive pastures, degraded pastures, and areas of high-yield potential soybean and low-yield potential soybean production. A significant influence (p < 0.01) was observed for all variables and between the different land uses and occupation types. September and October had lower emissions of soil CO₂ and low means of soil moisture and soil temperature, and no differences were observed among the treatments. On the other hand, there was a significant effect of the CO₂ flux model in productive pastures, high-yield potential soybean areas, and low-yield potential soybean areas. The months with the highest CO₂ flux values in the model, regardless of land use and land cover, were October and November, which is the beginning of the rainy season. There were positive correlations between soil CO₂ emissions and GPP (0.208), LAI (0.354), EVI (0.363), and soil moisture (0.280) and negative correlations between soil CO₂ emissions and soil temperature (-0.240) and CO₂ flux model (-0.314) values. Land use and land cover showed negative correlations with these variables, except for the CO₂ flux model variable. Soil CO₂ emission values were lower for high-yield potential soybean areas (averages from 0.834 to 6.835 μmol m^-2 s^-1) and low-yield potential soybean areas (from 0.943 to 5.686 μmol m^-2 s^-1) and higher for native forests (from 2.279 to 8.131 μmol m^-2 s^-1), whereas the opposite was true for the CO₂ flux model.

Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques

The impact of land use on water quality is becoming a global concern due to the increasing demand for freshwater. This study aimed to assess the effects of land use and land cover (LULC) on the surface water quality of the Buriganga, Dhaleshwari, Meghna, and Padma river system in Bangladesh. To determine the state of water, water samples were collected from twelve locations in the Buriganga, Dhaleshwari, Meghna, and Padma rivers during the winter season of 2015 and collected samples were analysed for seven water quality indicators: pH, temperature (Temp.), conductivity (Cond.), dissolved oxygen (DO), biological oxygen demand (BOD), nitrate nitrogen (NO₃-N), and soluble reactive phosphorus (SRP) for assessing water quality (WQ). Additionally, same-period satellite imagery (Landsat-8) was utilised to classify the LULC using the object-based image analysis (OBIA) technique. The overall accuracy assessment and kappa co-efficient value of post-classified images were 92% and 0.89, respectively. In this research, the root mean squared water quality index (RMS-WQI) model was used to determine the WQ status, and satellite imagery was utilised to classify LULC types. Most of the WQs were found within the ECR guideline level for surface water. The RMS-WQI result showed that the "fair" status of water quality found in all sampling sites ranges from 66.50 to 79.08, and the water quality is satisfactory. Four types of LULC were categorised in the study area mainly comprised of agricultural land (37.33%), followed by built-up area (24.76%), vegetation (9.5%), and water bodies (28.41%). Finally, the Principal component analysis (PCA) techniques were used to find out significant WQ indicators and the correlation matrix revealed that WQ had a substantial positive correlation with agricultural land (r = 0.68, P < 0.01) and a significant negative association with the built-up area (r =  - 0.94, P < 0.01). To the best of the authors' knowledge, this is the first attempt in Bangladesh to assess the impact of LULC on the water quality along the longitudinal gradient of a vast river system. Hence, we believe that the findings of this study can support planners and environmentalists to plan and design landscapes and protect the river environment.

Remote sensing strategies to monitoring land use maps with AVHRR and MODIS data over the South Asia regions

In South Asia, annual land use and land cover (LULC) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vitally essential to obtain correct information on the LULC in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LULC map of South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using the annual map of the LULC time series, and the space-time dynamics of the LULC map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years show the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrublands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

Effects of landscape conservation on the ecohydrological and water quality functions and services and their driving factors

Since the implementation of landscape conservation of the green heart area in the Changsha-Zhuzhou-Xiangtan Metropolitan Region, the landscape structure and pattern have changed significantly. The ecosystem service functions in the area have been improved, but the status of ecohydrological and water quality and service functions (EHWQSFs) is still unclear. To clarify the status of EHWQSFs and their driving factors influenced by landscape conservation, this study analysed landscape changes using remote sensing image data from 1998, 2008, and 2018 and the changes and their spatial characteristics using the Soil and Water Assessment Tool (SWAT) and spatial analysis methods. The results showed that the dominant land types in the area were forestland and cropland from 1998 to 2018; the area of forestland and construction land expanded and that of cropland decreased year by year; the annual average surface runoff volume rose, and the annual average actual evapotranspiration and soil water content fell from 1998 to 2008 and rose from 2008 to 2018; and all pollutant indicators decreased significantly after 2008. The areas with higher surface runoff were mainly concentrated in the central and southern regions, those with higher evapotranspiration were in the northwestern and southwestern regions, those with higher soil water content were in the northern region, and those with higher sediment and nitrogen and phosphorus pollutant contents were in the central and southeastern regions. The results showed that land use, land cover and meteorological factors were the most significant drivers on EHWQSFs and illustrated that EHWQSFs in the area decreased after 1998. There was a significant improvement after 2008 and the area currently has a good status. This study not only provides insights into land use, land cover and meteorological factors that have significant impacts on EHWQSFs but also highlights that the landscape conservation of the area can improve ecosystem service functions.

Impacts of rapid urbanization on spatial dynamics of land use-based carbon emission and surface temperature changes in the Semarang Metropolitan Region, Indonesia

This study explores the relationship between carbon emission patterns and the land surface temperature (LST) changes due to the rapid urbanization in the Semarang Metropolitan Region (SMR), an Indonesian area that has experienced rapid urban growth compared to other urban areas. This research used the stock-difference and gain-loss methods to calculate carbon stocks and emissions. Then, band 6 on Landsat 5 TM (2008) and band 10 on Landsat 8 OLI (2013 and 2018) were used to calculate the LST changes. These results showed that the peri-urban area had a more significant change. The correlation between carbon emissions and an increased SMR temperature correlates to 0.646. This shows that the carbon emissions pattern promotes temperature dynamics in the SMR. Furthermore, this study proved the release of carbon emissions in line with LST dynamics spatially. In this case, this study proved that rapid urbanization in the SMR promotes both carbon emission and LST. Those changes are also affected by vegetation canopy availability and other activities. As a result, the government must prioritize spatial planning in the SMR to mitigate environmental change risk. In addition, the government must develop novel strategies to deal with a wide range of fast and unpredictable potential changes in the urban area and its surroundings.

Habitat quality dynamics in China's first group of national parks in recent four decades: Evidence from land use and land cover changes

As the most biodiversity-rich part of the protected areas system, habitats within the pilot national parks have long been threatened by drastic human-induced land use and land cover changes. The growing concern about habitat loss has spurred China's national park project to shift from pilot to construction phase with the official establishment of China's first group of national parks (CFGNPs) in October 2021. But far too little attention has been paid to the synergistic work concerning the habitat quality (HQ) dynamics of all five national parks. Here, the InVEST model, combined with a satellite-derived land use and land cover product and a hot spot analysis (HSA) method, was used to investigate the HQ dynamics at the park- and pixel-scale within the CFGNPs. Our results demonstrate that the past ecological conservation practices within national parks have been unpromising, especially in Giant Panda National Park, Northeast China Tiger and Leopard National Park (NCTL), and Wuyi Mountain National Park (WYM), where HQ as a whole showed a significant decline. Furthermore, more than half of Hainan Tropical Rainforest National Park (87.2%), WYM (77.4%), and NCTL (52.9%) showed significant HQ degradation from 1980 to 2019. Besides, increasing trends in the area shares of HQ degraded pixels were observed in all five national parks from 1980-1999 to 2000-2019. The HSA implied that the hot spots of high HQ degradation rates tend to occur in areas closer to urban settlements or on the edge of national parks, where human activities are intensive. Despite these disappointing findings, we highlighted from the observed local successes and the HQ plateau that the construction of CFGNPs is expected to reverse the deteriorating HQ trends. Thus, we concluded our paper by proposing an HSA-based regulatory zoning scheme that includes five subzones to guide the future construction of China's national park system.

Black carbon flux in terrestrial and aquatic environments of Kodaikanal in the Western Ghats, South India: Estimation, source identification, and implication

Evolving Anthropocene epoch wields significant influence in altering atmospheric carbon, which affects the carbon cycle, leading to climate change. Understanding the carbon stock, fate, and transport across ecosystems are essential in determining India's carbon budget, hitherto, unavailable. In this study, we have analysed the stock, source, distribution, flux, and the relationship between terrestrial and aquatic black carbon over a high-altitude mountainous area in the Western Ghats region using the data collected from September 2019 to February 2021. Soil Organic Carbon (SOC) and Black Carbon (BC) are the highest in the forest region (SOC:23 ± 3 g of C/kg (dry weight (dw)), BC:6 ± 3 g/kg) and are the lowest in the urban region (SOC: 13 ± 2 g of C/kg (dw), BC:2 ± 1 g/kg). SOC is labile, whereas BC is non-labile. The BC/SOC ratio represents soil carbon lability. Topsoil BC/SOC ratios vary by land use and land cover, with urban areas having greater labile carbon pools than the forests. Dissolved BC (DBC) concentrations were most strongly correlated with bulk Dissolved Organic Carbon (DOC) concentrations in midstream (R = 0.6, p < 0.05), headwater streams (R = 0.3, p < 0.05) and to the soil bulk DBC (R = 0.3, p < 0.05), indicating the presence of transfer mechanism of soil to streams. The molecular associations revealed the presence of biolabile autochthonous compounds suggesting the crucial role land use and land cover play on watersheds. A positive relationship between DOC with seasonal hydrology and gradient significantly influences the DBC flux across regional streams. Intercomparison of observed terrestrial and aquatic carbon stocks with globally modelled data indicates an overestimation of regional-scale stock. These new findings have repercussions to policy framework on regional climate change. Further, the results suggest that a consistent quantification of BC and integration of regional, and global source-to-sink process are needed in order to understand and better quantify biogeochemical process cycles and associated climatic impacts.

Vegetational succession assessment in a fragment of the Brazilian Atlantic Forest

Vegetational succession assessment is an important step for better management practices, providing relevant quantitative and qualitative information. With the advancements of remote sensing algorithms and access to data, land use and land cover (LULC) monitoring has become increasingly feasible and important for the evaluation of changes in the landscape at different spatial and temporal scales. This study aims to analyze the vegetation succession achieved by a project funded by the Brazilian Environmental Ministry (Ministério do Meio Ambiente, in Portuguese) intended to recover degraded areas. A 2014 and a 2019 LULC map was generated using high-resolution (10 cm) images. Given the great challenge of classifying high-resolution images, three classification algorithms were compared. The techniques to regenerate degraded areas were efficient to increase arboreal vegetation area by more than 30% between 2014 and 2019. Land cover and land use change monitoring is of paramount importance to strengthen sustainable practices, especially in the highly threatened Atlantic Forest biome. This study also shows that funding opportunities are essential for projects that make such actions possible, including the present research and the analysis of environmental regeneration.

Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu, India

The study on land use and land cover (LULC) changes assists in analyzing the change and regulates environment sustainability. Hence, this research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009-2019 and 2019-2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classified in GEE using Random Forest (RF). LULC maps were then framed with the CA-Markov model to forecast future LULC change. It was carried out in four steps: (1) change analysis, (2) transition potential, (3) change prediction, and (4) model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are found to be under stress, developing into built-up. The overall accuracy was above 89%, and the kappa coefficient was above 87% for all 3 years. This study can provide suggestions and a basis for urban development planning as it is highly susceptible to coastal flooding.

Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system, Kerala: a machine learning approach

Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the goal of this study is to identify the LULC for the Vembanad Lake system (VLS), Kerala, in the short term, i.e., within a decade, utilizing three standard machine learning approaches, random forest (RF), classification and regression trees (CART), and support vector machines (SVM), on the Google Earth Engine (GEE) platform. When comparing the three techniques, SVM performed poor at an average accuracy of around 82.5%, CART being the next at accuracy of 87.5%, and the RF model being good at the average of 89.5%. The RF outperformed the SVM and CART in almost identical spectral classes such as barren land and built-up areas. As a result, RF-classified LULC is considered to predict the spatio-temporal distribution of LULC transition analysis for 2035 and 2050. The study was conducted in Idrisi TerrSet software using the cellular automata (CA)-Markov chain analysis. The model's efficiency is evaluated by comparing the projected 2019 image to the actual 2019 classified image. The efficiency was good with more than 94.5% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the area.

Temporal and spatial patterns of fire activity in three biomes of Brazil

The trade-off between conservation of natural resources and agribusiness expansion is a constant challenge in Brazil. The fires used to promote agricultural expansion increased in the last decades. While studies linking annual fire occurrence and rainfall seasonality are common, the relationship between fires, land use, and land cover remains understudied. Here, we investigated the frequency of the fires and performed a trend analysis for monthly, seasonal, and annual fires in three different biomes: Cerrado, Pantanal, and Atlantic Forest. We used burned area and integrated models in distinct scales (interannual, intraseasonal, and monthly) using Probability Density Functions (PDFs). The best fitting was found for Generalized Extreme Values (GEV) distribution at all three biomes from the several PDFs tested. We found the most fire in the Pantanal (wetlands), followed by Cerrado (Brazilian Savanna) and Atlantic Forest (Semideciduous Forest). Our findings indicated that land use and land cover trends changed over the years. There was a strong correlation between fire and agricultural areas, with increasing trends pointing to land conversion to agricultural areas in all biomes. The high probability of fire indicates that expanding agricultural areas through the conversion of natural biomes impacts several natural ecosystems, transforming land cover and land use. This land conversion is promoting more fires each year.

Analysis of land surface temperature with land use and land cover and elevation from NASA MODIS satellite data: a case study of Bali, Indonesia

The Moderate Resolution Imaging Spectroradiometer (MODIS) of the National Aeronautics and Space Administration (NASA) offers numerous land products of the Earth's datasets. On the other hand, researchers find it difficult to retrieve this data for specific places. The methods for extracting and analyzing land surface temperature (LST), land use and land cover (LULC), and elevation are presented in this study. The R commands provided make the time-consuming process of extracting data for specific places much more accessible. As a result, a statistical study of LST over Bali is shown as an example. Over the 15 regions of Bali, a quadratic polynomial identified five possible warming patterns, while a logistic regression model assessed the probability of warming. The findings suggest that 25.2% of Bali has warmed during the last two decades, with temperatures being highest in urban and built-up areas and deciduous forests and inversely associated with elevation. Global warming has sparked a lot of academic interest and has become a serious climate problem. The techniques proposed in this work simplify the extraction of LST, LULC, and elevation data from MODIS satellites. These approaches can also be used on other datasets with identical topologies, such as the normalized difference vegetation index (NDVI), aerosol optical depth (AOD), and night light data.

Investigating Changes Driving Cumulative Impacts on Native Vegetation in Mining Regions in the Northeastern Brazilian Amazon

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.

Spatio-temporal assessment on land use and land cover (LULC) and forest fragmentation in shifting agroecosystem landscape in Ukhrul district of Manipur, Northeast India

The information on land use and land cover (LULC) plays a critical role in understanding the interactions between human activities and the natural environment. The changes in LULC have a significant impact on the ecological integrity of forests, biodiversity, and natural resources, which in turn trigger global environmental change. Forest fragmentation is an important conservation challenge that includes interdependent forest loss components and spatial shift patterns. Over the years, Northeast India has experienced major changes in LULC and forest fragmentation. There are limited information and data regarding the change in LULC patterns and causes of forest fragmentation. The present study was carried out with an attempt to analyze the change in LULC and forest fragmentation using satellite data of three different time series: 1991, 2005, and 2020 for Ukhrul district, Manipur, Northeast India. Different LULC classes were classified using the supervised method, viz., maximum likelihood algorithm in ERDAS Imagine 2014 and generated thematic maps in ArcGIS 10.4 software. Considering the classified forest class, fragmentation in the forest area was grouped into different categories of fragmentation using the Landscape Fragmentation Tool (LFT v 2.0). The distribution of the perforated category has tremendously increased in 2020 from 1991. The outcome of the present study will help to understand the inherent forest vulnerability and to adopt sustainable management strategies for forest and agriculture ecosystems in the hill landscape.

Spatio-temporal variation in potential habitats for rare and endangered plants and habitat conservation based on the maximum entropy model

Rare and endangered plants (REPs) act as key indicators for species habitat priorities, and can thus be critical in global biodiversity protection work. Human activities and climate change pose great threats to REPs, so protection should be a top priority. In this study, we used the maximum entropy model (Maxent) to identify current and future (2050) potential habitats of REPs in the Xishuangbanna tropical area of China. We compared potential habitats with existing protected areas (PAs) in gap analysis, and used a transfer matrix to quantify changes in potential habitats. By comparing the potential distribution obtained with existing land use and land cover, we analyzed the impact of human-dominated land use changes on potential habitats of REPs and identified the main habitat patch types of REPs. The results showed that the current potential habitat area of hotspots is 2989.85 km², which will be reduced to 247.93 km² by 2050, accounting for 15.60% and 1.29% of the total research area, respectively. Analysis of land use and land cover showed that rubber plantation was the human-dominated land use posing the greatest threat to potential habitats of REPs, occupying 23.40% and 21.62% of current and future potential habitats, respectively. Monsoon evergreen broad-leaved forest was identified as the main habitat patch type for REPs in Xishuangbanna and occupied the highest proportion of potential habitat area. Gap analysis showed that only 35.85% of habitat hotspots are currently included in existing PAs and that this will decrease to 32.26% by 2050. This emphasizes the importance of protecting current and future potential habitats of REPs in a dynamic conservation approach that can adapt to changes in future climate and human activities.

Thermal heterogeneity in the proximity of municipal solid waste landfills on forest and agricultural lands

Information on the spatial extent of potential impact areas near disposal sites is vital to the development of a sustainable natural resource management policy. Eight Canadian landfills of various sizes and shapes in different climatic conditions are studied to quantify the spatial extent of their bio-thermal zone. Land surface temperature (LST) and normalized difference vegetation index (NDVI) are examined with respect to different Land Use Land Cover (LULC) classes. Within 1500 m of the sites, LST ranged from 18.3 °C to 29.5 °C and 21.3 °C-29.7 °C for forest land and agricultural land, respectively. Linear regression shows a decreasing LST trend in forest land for five out of seven landfills. A similar trend, however, is not observed for agricultural land. Both the magnitude and the variability of LST are higher in agricultural land. The size of the bio-thermal zone is sensitive to the respective LULC class. The approximate bio-thermal zones for forest class and agricultural classes are about 170 ± 90 m and 180 ± 90 m from the landfill perimeter, respectively. For the forest class, NDVI was negatively correlated with LST at six out of seven Canadian landfills, and stronger relationships are observed in the agricultural class. NDVI data has a considerably larger spread and is less consistent than LST. LST data appears more appropriate for identifying landfill bio-thermal zones. A subtle difference in LST is observed among six LULC classes, averaging from 23.9 °C to 27.4 °C. Geometric shape makes no observable difference in LST in this study; however, larger landfill footprint appears to have higher LST.

Tracing land use and land cover change in peri-urban Delhi, India, over 1973-2017 period

Land use and land cover changes over 1973-2017 period in peripheral Delhi were mapped based on digital classification of satellite data and their driving forces ascertained. Urban area expanded and agricultural area diminished at annual rates of 38.6% and 2.1%, respectively, during the 1973-2017 period. Urban expansion occurred more in scrub and sparse vegetation areas than in cultivated lands or ponds. Loss of cultivated land happened mostly due to abandonment of cropping and tree planting in farmhouses developed by the urban elites. Improvement in the state of forests in terms of their expansion as well as densification offsets their loss due to urbanisation, encroachment and logging. The increment in the green cover was due to strict enforcement of compensatory afforestation/forest conservation law, growing demand of ecotourism, emergence of tree-clad farmhouses and increased environmental awareness and surveillance. This research will help in comprehending policies favouring sustainable urban development.

Characteristics of surface evapotranspiration and its response to climate and land use and land cover in the Huai River Basin of eastern China

The Huai River Basin (HRB) has experienced significant climate and land use and land cover changes (LUCC) which have impacted the water cycle in recent times. However, little is understood about the impact of climate change and LUCC affecting evapotranspiration (ET). Thus, we investigate how surface ET response to climate change and LUCC in the HRB for the period from 2001 to 2014. ET and land cover types products (i.e., MOD16 and MCD12Q1, respectively) from MODerate-resolution Imaging Spectroradiometer (MODIS) were employed in this research. Water balance method and D20 pan evaporation data (E_pan) as well as eddy covariance (EC) measurements were used to validate the MOD16 product, and the Theil-Sen's slope estimator and Mann-Kendall (M-K) test were adopted to estimate the magnitude and significance of ET trends. Moreover, daily meteorological data of 137 weather stations from 2001 to 2014 were also employed to explore the correlation mechanism between ET and meteorological factors. The results showed that the accuracy of MOD16 product data were convincible and could be used to estimate ET in the HRB. The higher values of ET are mainly distributed in the south and lower values in the north. ET decreased significantly in all seasons except in spring, especially in winter. The results also depicted that the land-use type in the HRB is mainly croplands, followed by forests and grasslands. Croplands area showed a decreasing trend at a rate of -176.2 km²·a^-1, grasslands area presents a w-type fluctuation decreasing trend with a rate of -35.8 km²·a^-1, urban/built-up area increased at a rate of 138.3 km²·a^-1, water bodies area decreased gradually at a rate of -1.38 km²·a^-1, wetlands area increased significantly at a rate of 43.6 km²·a^-1, and barren area decreased gradually at a rate of -9.5 km²·a^-1. The average annual ET is closely related to land-use types and shows a significant downward trend in general. The corresponding ET magnitude is exhibited as follows: forests>grasslands>croplands>wetlands>water bodies>urban/build-up lands>barren. Results of the study also suggest water conditions (precipitation (Pre) and relative humidity (RH) decrease) are major controlling factors in the decline of ET. Overall, the LUCC has a smaller influence on ET than climate change in the HRB. This research will provide a better insight into climate change and LUCC impacts on water resources.

Improving the management effectiveness and decision-making by stakeholders' perspectives: A case study in a protected area from the Brazilian Atlantic Forest

Assessing management effectiveness in protected areas is a fundamental instrument to achieve socio-biodiversity protection goals. This study aimed to analyze the management effectiveness of Cunhambebe State Park (from now on, "PEC") in the State of Rio de Janeiro in Brazil, from the perception of stakeholders and the multi-temporal analysis of land use and land cover between 1998 and 2018. We used the Rapid Assessment and Prioritization of Protected Area Management method for a participatory approach. Seventy-two indicators were used and applied to assess the perception of stakeholders related to the Advisory Council. The management effectiveness of PEC was classified as moderately satisfactory (63.41%). Indicators of "Legal security", "Vulnerability", "Site design and planning" and "Financial resources" revealed the weaknesses and threats of management and what should be the priority projects for better effectiveness. Through the multi-temporal analysis, we identified that the advances of pasture and urban areas are the highest pressures and threats, as perceived by stakeholders. In our case study, we provide evidence of actions that must be performed by the PEC management team. These actions must consider the weaknesses and threats presented by the SWOT analysis. Finally, we recommend some political and management measures: 1) Financial resources for the land regularization of areas overlapping with PEC, 2) Guidelines about PEC areas must be included in the Master Plans of municipalities that cover PEC limits, 3) Technical assistance to improve land management, and 4) Strengthen environmental education initiatives at all school levels.

Climatic seasonality and water quality in watersheds: a study case in Limoeiro River watershed in the western region of São Paulo State, Brazil

Applying the climatological water balance (WB) concept to describe the relationship between climatic seasonality and surface water quality according to different forms of land use and land cover (LULC) is an important issue, but little explored in the literature. In this paper, we evaluate the influence of WB on surface water quality and its impacts when interacting with LULC. We monitored 11 sampling points during the four seasons of the year, from which we estimate WQI (water quality index) and TSI (trophic state index). We found an effect of the seasonality factor on both WQI values (F(3,30) = 12.472; p < 0.01) and in TSI values (F(3,30) = 6.967; p < 0.01). We noticed that LULC interferes in the way that the water balance influences the WQI and TSI values since in sampling points closest to higher urban density, with little or no riparian protection, the correlation between water balance and water quality was lower. In the stations that had the lowest water surplus and deficit, there was positive linearity between water balance and WQI. However, in the seasons when the surplus and water deficit recorded were extreme, there was no linearity. We conclude that water deficiency impairs the quality of surface water. In the extreme surplus water season, the homogeneity of WQI samples was lower, suggesting a higher interaction between rainwater and LULC. This study contributes to design management strategies of water resources, considering the climatic seasonality for optimization.

Relative influence of watershed and geomorphic features on nutrient and carbon fluxes in a pristine and moderately urbanized stream

Streams are important sites of elemental transformations due to the relatively high contact rates between flowing water and biogeochemically reactive sediments. Increased urbanization typically results in higher nutrient and carbon (C) inputs to streams from their watersheds and increased flow rates due to modification in channel form, reducing within stream net retention and increasing downstream exports. However, less is known on how moderate urbanization might influence the joint processing of C, nitrogen (N), and phosphorus (P) in streams or the relative influence of changes in watershed and stream features on their fluxes. In this study, we performed mass-balances of different C, N, and P species in multiple reaches with contrasting land use land cover and geomorphic features (pools, riffles, runs) to determine the effects of geomorphology versus human influence on elemental fluxes in a pristine and a semi-urban stream. N was the most responsive of all elements, where nitrate concentrations were 3.5-fold higher in the peri-urban stream. Dissolved organic carbon was only slightly higher in the peri-urban site whereas total P not significantly different between streams. In terms of fluxes, nitrate behaved differently between the streams with net retention occurring in the majority of the reaches of the pristine site, whereas net export was observed in all of the reaches of the semi-urban one. We found a decrease in nitrate concentrations with an increase in excess deuterium of the water (d-excess), an indicator of how overall water retention capacity of the watershed favored N loss. Within the stream, the presence of pools, and reduced channel slope, which also increase water retention time, again favored N loss. Overall, nitrate was the most sensitive nutrient to slight urbanization, where higher export to stream was influenced by land use, but where geomorphic features were more important in driving retention capacity.

Impact of land use/land cover change on the topsoil selenium concentration and its potential bioavailability in a karst area of southwest China

Selenium (Se) is an essential micronutrient for human health, and its abundance and potential bioavailability in the soil are of increasing concern worldwide. To date, how total soil Se and its bioavailability would respond to human disturbance or future environmental change is not yet clear, and associated controlling factors remain incompletely understood. Here, we collected soil samples (0-15 cm) from different land use/land cover types, including active cropland, grassland, shrubland, and secondary forest, in a Se-enriched area of Guangxi, southwest China. Total Se concentration and its potential bioavailability, as estimated by phosphate extractability, were investigated. Total soil Se concentration (Se_total) for all samples ranged from 220 to 1820 μg kg^-1, with an arithmetic average value of 676 ± 24 μg kg^-1 (Mean ± SE, the same below). The concentration of phosphate extractable Se (Se_phosphate) varied between 1 and 257 μg kg^-1, with an arithmetic mean value of 79 ± 5 μg kg^-1, accounting for on average 13 ± 1% of the Se_total. Among the four land use/land cover types, Se_total and Se_phosphate were generally more enriched in the secondary forest than those in the grassland and cropland. The content of soil organic carbon (SOC) was the overriding edaphic factor controlling the abundance and potential bioavailability of Se in topsoils. In addition, climatic variables such as mean annual precipitation and mean annual temperature were also key factors affecting the abundance and potential bioavailability of soil Se. Our results suggest that changes in land use/land cover types may deeply influence Se biogeochemistry likely via alterations in soil properties, particularly SOC content.

Development of Land-Use/Land-Cover Maps Using Landsat-8 and MODIS Data, and Their Integration for Hydro-Ecological Applications

The Athabasca River watershed plays a dominant role in both the economy and the environment in Alberta, Canada. Natural and anthropogenic factors rapidly changed the landscape of the watershed in recent decades. The dynamic of such changes in the landscape characteristics of the watershed calls for a comprehensive and up-to-date land-use and land-cover (LULC) map, which could serve different user-groups and purposes. The aim of the study herein was to delineate a 2016 LULC map of the Athabasca River watershed using Landsat-8 Operational Land Imager (OLI) images, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) images, and other ancillary data. In order to achieve this, firstly, a preliminary LULC map was developed through applying the iterative self-organizing data analysis (ISODATA) clustering technique on 24 scenes of Landsat-8 OLI. Secondly, a Terra MODIS-derived 250-m 16-day composite of 30 EVI images over the growing season was employed to enhance the vegetation classes. Thirdly, several geospatial ancillary datasets were used in the post-classification improvement processes to generate a final 2016 LULC map of the study area, exhibiting 14 LULC classes. Fourthly, an accuracy assessment was carried out to ensure the reliability of the generated final LULC classes. The results, with an overall accuracy and Cohen’s kappa of 74.95% and 68.34%, respectively, showed that coniferous forest (47.30%), deciduous forest (16.76%), mixed forest (6.65%), agriculture (6.37%), water (6.10%), and developed land (3.78%) were the major LULC classes of the watershed. Fifthly, to support the data needs of scientists across various disciplines, data fusion techniques into the LULC map were performed using the Alberta merged wetland inventory 2017 data. The results generated two useful maps applicable for hydro-ecological applications. Such maps depicted two specific categories including different types of burned (approximately 6%) and wetland (approximately 30%) classes. In fact, these maps could serve as important decision support tools for policy-makers and local regulatory authorities in the sustainable management of the Athabasca River watershed.

Effect of photo-biodegradation and biodegradation on the biogeochemical cycling of dissolved organic matter across diverse surface water bodies

The objective of this research was to quantify the temporal variation of dissolved organic matter (DOM) in five distinct waterbodies in watersheds with diverse types of land use and land cover in the presence and absence of sunlight. The water bodies were an agricultural pond, a lake in a forested watershed, a man-made reservoir, an estuary, and a bay. Two sets of samples were prepared by dispensing unfiltered samples into filtered samples in 1:10 ratio (V/V). The first set was exposed to sunlight (10 hr per day for 30 days) for examining the combined effect of photo-biodegradation, while the second set was stored in dark for examining biodegradation alone. Spectroscopic measurements in tandem with multivariate statistics were used to interpret DOM lability and composition. The results suggest that the agricultural pond behaved differently compared to other study locations during degradation experiments due to the presence of higher amount of microbial humic-like and protein-like components derived from microbial/anthropogenic sources. For all samples, a larger decrease in dissolved organic carbon (DOC) concentration (10.12% ± 9.81% for photo-biodegradation and 6.65% ± 2.83% for biodegradation) and rapid transformation of DOM components (i.e., terrestrial humic-like components into microbial humic and protein-like components) were observed during photo-biodegradation experiments. Results suggest that sunlight facilitated DOM biodegradation, resulting in simpler recalcitrant molecules regardless of original composition. Overall, it was found that combined effects of light and bacteria are more efficient than bacterial effects alone in remineralizing and altering DOM, which highlights the crucial importance of sunlight in transforming aquatic DOM.

Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata-Markov model

The detection and prediction of land use/land cover (LULC) change is crucial for guiding land resource management, planning, and sustainable development. In the view of seasonal rhythm and phenological effect, detection and prediction would benefit greatly from LULC maps of the same seasons for different years. However, due to frequent cloudiness contamination, it is difficult to obtain same-season LULC maps when using existing remote sensing images. This study utilized the spatiotemporal data fusion (STF) method to obtain summer Landsat-scale images in Hefei over the past 30 years. The Cellular Automata-Markov model was applied to simulate and predict future LULC maps. The results demonstrate the following: (1) the STF method can generate the same inter-annual interval summer Landsat-scale data for analyzing LULC change; (2) the fused data can improve the LULC detection and prediction accuracy by shortening the inter-annual interval, and also obtain LULC prediction results for a specific year; (3) the areas of cultivated land, water, and vegetation decreased by 33.14%, 2.03%, and 16.36%, respectively, and the area of construction land increased by 200.46% from 1987 to 2032. The urban expansion rate will reach its peak until 2020, and then slow down. The findings provide valuable information for urban planners to achieve sustainable development goals.

Riverine dissolved organic carbon in Rukarara River Watershed, Rwanda

Dissolved organic carbon (DOC) loading is rarely estimated in tropical watersheds. This study quantifies DOC loading in the Rukarara River Watershed (RRW), a Rwandan tropical forest and agricultural watershed, and evaluates its relationship with hydrological factors, land use and land cover (LULC), and topography to better understand the impact of stream DOC export on watershed carbon budgets. The annual average load for the study period was 977.80 kg C, which represents approximately 8.44% of the net primary productivity of the watershed. The mean daily exports were 0.37, 0.14, 0.075 and 0.32 kg C/m² in streams located in natural forest, tea plantation, small farming areas, and at the outlet of the river, respectively. LULC is a factor that influences DOC loading. The quick flow was the main source of stream DOC at all study sites. Stream DOC increases with increasing water flow, indicating a positive relationship. Thus, the expectation is that a change in land cover and/or rainfall will result in a change of stream DOC dynamics within the watershed. Topography was also found to influence the dynamics of stream DOC through its effect on overland flow in terms of drainage area and total length of flow paths. Tea plantations were located in areas of high drainage density and projected increase of rainfall in the region, as a consequence of climate change, could increase stream DOC content and affect stream water quality, biodiversity, balance between autotrophy and heterotrophy, and bioavailability of toxic compounds within the RRW.

Relationship between landslides and mountain development-Integrating geospatial statistics and a new long-term database

Development in mountainous areas is inevitable in countries with high population densities, but the actual relationship between development and landslides remains uncertain. Clarifying the key current or historical factors resulting in landslides is crucial for hazard prevention and mitigation. This study focused on the Shihmen Reservoir catchment in Taiwan. Two combinations of explanatory variables in five different years (1946, 1971, 2001, 2004, and 2012) collected from a geodatabase and digital archives were used to conduct proximity and discrete logistic regression analyses. The results demonstrate that landslides increased dramatically from 1946 to 2012 in the catchment area. The proximity and overlapping of human development with landslides increased. However, the logistic regression results indicated that variation in susceptibility to landslides was due to natural causes, with the exception of historical deforestation and newly constructed road systems. Therefore, well-recovered historical woodland sites might currently be landslide-prone areas. We suggest that cumulative historical events should be considered as explanatory variables in future landslide prediction analysis.

OMI satellite observed formaldehyde column from 2006 to 2015 over Xishuangbanna, southwest China, and validation using ground based zenith-sky DOAS

Formaldehyde (HCHO) provides a proxy to reveal the isoprene and biogenic volatile organic compounds emission which plays important roles in atmospheric chemical process and climate change. The ground-based observation with zenith-sky DOAS is carried out in order to validate the HCHO columns from OMI. It has a good correlation of 0.71678 between the HCHO columns from two sources. Then we use the OMI HCHO columns from January 2006 to December 2015 to indicate the interannual variation and spatial distribution in Xishuangbanna. The HCHO concentration peaks appeared in March or April for each year significantly corresponding to the intensive fire counts at the same time, which illustrate that the high HCHO columns are strongly influenced by the biomass burning in spring. Temperature and precipitation are also the important influence factors in the seasonal variation when there is nearly no biomass burning. The spatial patterns over the past ten years strengthen the deduction from the temporal variation and show the relationship with land cover and land use, elevation and population density. It is concluded that the biogenic activity plays a role in controlling the background level of HCHO in Xishuangbanna, while biomass burning is the main driving force of high HCHO concentration. And forests are greater contributor to HCHO rather than rubber trees which cover over 20% of the land in the region. Moreover, uncertainties from HCHO slant column retrieval and AMFs calculation are discussed in detail.

Multi-criteria evaluation of hydro-geological and anthropogenic parameters for the groundwater vulnerability assessment

Groundwater contamination assessment is a challenging task due to inherent complex dynamisms associated with the groundwater. DRASTIC is a very widely used rapid regional tool for the assessment of vulnerability of groundwater to contamination. DRASTIC has many lacunas in the form of subjectivities associated with weights and ratings of its hydro-geological parameters, and, therefore, the accuracy of the DRASTIC-based vulnerability map is questioned. The present study demonstrates the optimisation of the DRASTIC parameters along with a scientific consideration to the anthropogenic factors causing groundwater contamination. The resulting scientific consistent weights and ratings to DRASTIC parameters assist in the development of a very precise groundwater vulnerability map highlighting different zones of different gravity of contamination. One of the most important aspects of this study is that we have considered the impact of vadose zone in a very comprehensive manner by considering every sub-surface layer from the earth surface to the occurrence of groundwater. The study area for our experiment is Fatehgarh Sahib district of Punjab which is facing several groundwater issues.

Impact of highway construction on water bodies: a geospatial assessment

India has witnessed a massive infrastructure boom in the past few years. One of such projects is National Highway-7 (NH-7), a North-South highway connecting Kanyakumari, Tamil Nadu, to Varanasi, Uttar Pradesh, traversing many water bodies. The present study aims to assess the pre- and post-construction impact due to existing, new and widened NH-7 on the physical status of the water bodies, using remote sensing techniques. Satellite images spanning 22 years were procured and analysed for change detection in land use and land cover within the waterbodies. The study indicates that construction activities have led to transformation within the water bodies regarding reduction in area and inter-changing of land use and land cover classes, in turn leading to siltation and reduction of recharge.

Influence of land use and land cover on the spatial variability of dissolved organic matter in multiple aquatic environments

Water quality of lakes, estuaries, and coastal areas serves as an indicator of the overall health of aquatic ecosystems as well as the health of the terrestrial ecosystem that drains to the water body. Land use and land cover plays not only a significant role in controlling the quantity of the exported dissolved organic matter (DOM) but also influences the quality of DOM via various biogeochemical and biodegradation processes. We examined the characteristics and spatial distribution of DOM in five major lakes, in an estuary, and in the coastal waters of the Mississippi, USA, and investigated the influence of the land use and land cover of their watersheds on the DOM composition. We employed absorption and fluorescence spectroscopy including excitation-emission matrix (EEM) combined with parallel factor (PARAFAC) analysis modeling techniques to determine optical properties of DOM and its characteristics in this study. We developed a site-specific PARAFAC model to evaluate DOM characteristics resulting in five diverse DOM compositions that included two terrestrial humic-like (C1 and C3), two microbial humic-like (C2 and C5), and one protein-like (C4) DOM. Our results showed elevated fluorescence levels of microbial humic-like or protein-like DOM in the lakes and coastal waters, while the estuarine waters showed relatively high fluorescence levels of terrestrial humic-like DOM. The results also showed that percent forest and wetland coverage explained 68 and 82% variability, respectively, in terrestrial humic-like DOM exports, while 87% variability in microbially derived humiclike DOM was explained by percent agricultural lands. Strong correlations between microbial humic-like DOM and fluorescence-derived DOM indices such as biological index (BIX) and fluorescence index (FI) indicated autochthonous characteristics in the lakes, while the estuary showed largely allochthonous DOM of terrestrial origin. We also observed higher concentrations of total dissolved phosphorous (TDP) and ammonium nitrogen (NH₄-N) in coastal waters potentially due to photodegradation of refractory DOM derived from the sediment-bound organic matter in the coastal wetlands. This study highlights the relationships between the DOM compositions in the water and the land use and land cover in the watershed. The spatial variability of DOM in three different types of aquatic environments enhances the understanding of the role of land use and land cover in carbon cycling through export of organic matter to the aquatic ecosystems..

Protected areas and agricultural expansion: Biodiversity conservation versus economic growth in the Southeast of Brazil

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.

Monitoring and predicting the fecal indicator bacteria concentrations from agricultural, mixed land use and urban stormwater runoff

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.

Effects of land use and land cover on selected soil quality indicators in the headwater area of the Blue Nile basin of Ethiopia

Understanding changes in soil quality resulting from land use and land management changes is important to design sustainable land management plans or interventions. This study evaluated the influence of land use and land cover (LULC) on key soil quality indicators (SQIs) within a small watershed (Jedeb) in the Blue Nile Basin of Ethiopia. Factor analysis based on principal component analysis (PCA) was used to determine different SQIs. Surface (0-15 cm) soil samples with four replications were collected from five main LULC types in the watershed (i.e., natural woody vegetation, plantation forest, grassland, cultivated land, and barren land) and at two elevation classes (upland and midland), and 13 soil properties were measured for each replicate. A factorial (2 × 5) multivariate analysis of variance (MANOVA) showed that LULC and altitude together significantly affected organic matter (OM) levels. However, LULC alone significantly affected bulk density and altitude alone significantly affected bulk density, soil acidity, and silt content. Afforestation of barren land with eucalypt trees can significantly increase the soil OM in the midland part but not in the upland part. Soils under grassland had a significantly higher bulk density than did soils under natural woody vegetation indicating that de-vegetation and conversion to grassland could lead to soil compaction. Thus, the historical LULC change in the Jedeb watershed has resulted in the loss of soil OM and increased soil compaction. The study shows that a land use and management system can be monitored if it degrades or maintains or improves the soil using key soil quality indicators.

Inferring land use and land cover impact on stream water quality using a Bayesian hierarchical modeling approach in the Xitiaoxi River Watershed, China

Lake eutrophication has become a very serious environmental problem in China. If water pollution is to be controlled and ultimately eliminated, it is essential to understand how human activities affect surface water quality. A recently developed technique using the Bayesian hierarchical linear regression model revealed the effects of land use and land cover (LULC) on stream water quality at a watershed scale. Six LULC categories combined with watershed characteristics, including size, slope, and permeability were the variables that were studied. The pollutants of concern were nutrient concentrations of total nitrogen (TN) and total phosphorus (TP), common pollutants found in eutrophication. The monthly monitoring data at 41 sites in the Xitiaoxi Watershed, China during 2009-2010 were used for model demonstration. The results showed that the relationships between LULC and stream water quality are so complicated that the effects are varied over large areas. The models suggested that urban and agricultural land are important sources of TN and TP concentrations, while rural residential land is one of the major sources of TN. Certain agricultural practices (excessive fertilizer application) result in greater concentrations of nutrients in paddy fields, artificial grasslands, and artificial woodlands. This study suggests that Bayesian hierarchical modeling is a powerful tool for examining the complicated relationships between land use and water quality on different scales, and for developing land use and water management policies.