Landsat Images Research Articles

A Universal Framework for Near-Real-Time Detection of Vegetation Anomalies from Landsat Data

Vegetation anomalies are frequently occurring and may greatly affect ecological functions. Many near-real-time (NRT) detection methods have been developed to detect these anomalies in a timely manner whenever a new satellite observation is available. However, the undisturbed vegetation conditions captured by these methods are only applicable to a particular pixel or vegetation type, resulting in a lack of universality. Also, most methods that use single characteristic parameter may ignore the multi-spectral expression of vegetation anomalies. In this study, we developed a universal framework to simultaneously detect various vegetation anomalies in NRT from Landsat observations. Firstly, Landsat surface reflectance data from the Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites were selected as a reference vegetation dataset to calculate the normalized difference vegetation index (NDVI) and the normalized burn ratio (NBR), which describe vegetation conditions from the perspectives of greenness and moisture, respectively. After the elimination of cloud-contaminated pixels, the high-quality NDVI and NBR data over the BELMANIP sites were further normalized in order to remove the differences in the growth of the varying vegetation. Based on the normalized NDVI and NBR, kernel density estimation (KDE) was used to create a universal measure of undisturbed vegetation, which described the uniform spectral frequency distribution of different undisturbed vegetation with a series of accumulated probabilities on a monthly basis. Whenever a new Landsat observation is collected, the vegetation anomalies are determined according to the universal measure in NRT. To demonstrate the potential of this framework, three study areas with different anomaly types (deforestation, fire event, and insect outbreak) in distinct ecozones (rainforest, coniferous forest, and deciduous broad-leaf forest) were used. The quantitative analyses showed generally high overall accuracies (>90% with the kappa >0.82). The user accuracy for the fire event and the producer accuracy for the earlier insect infestation were relatively lower. The accuracies may be affected by the complexity of the land surface, the quality of the Landsat image, and the accumulated probability threshold.

Assessing the Flood and Riverbank Erosion Impacts and Coping Strategies in Hatia Union of Ulipur, Kurigram, Bangladesh

The present paper aims to focus on the flood and riverbank erosion impacts and coping strategies in Hatia Union of Ulipur, Kurigram of Bangladesh. Due to the prevailing geographical location and topographical features, Bangladesh is considered as one of the most disaster-prone countries in the world. Kurigram district, located in the northern part of Bangladesh, is particularly flood and riverbank erosion prone because of its extensive river networks and its location within the Teesta-Brahmaputra River basin. Hatia Union of Ulipur Upazila was chosen as the study area due to its susceptibility to these disasters. In order to identify the flood and riverbank erosion impacts and coping strategies, field level data and information were collected through field survey and interviewing the respondents. Using ArcGIS Pro software, Sentinel-1 (SAR) and LANDSAT images with a resolution of 30 meters, the flood and riverbank erosion maps were produced. Floods and riverbank erosion both have significant impacts on local communication, food, shelter, health, employment, security, and people’s movement. Local people suffer a lot and their lives and livelihoods are affected severely by these disaster events. The affected communities undertake various coping strategies with the support of governmental and non-governmental organizations to overcome the incumbent impacts of flood and riverbank erosion. Policy level development and effective implementation of various projects and programs prioritizing the most vulnerable areas, focusing on socio-economic advancement, and resolving relevant issues can limit the sufferings of local people. J. Asiat. Soc. Bangladesh, Sci. 50(1-2): 69-88, June-December 2024

Revealing the process and mechanism of non-grain production of cropland in rapidly urbanized Deqing County of China.

Non-grain production of cropland (NGPCL) is a common consequence of rapid urbanization, but excessive NGPCL threatens food security and sustainable cropland use. However, the evolutionary processes and mechanisms of different NGPCL types remain largely unknown, compromising the scientific basis for NGPCL management. Thus, taking rapidly urbanized Deqing as the study area, this research constructed an NGPCL theoretical framework from the perspective of agricultural location, classified NGPCL types using Landsat images, random forest algorithm and Google Earth Engine, and revealed their spatiotemporal changes and different influencing factors through the Multinomial Logit Regression model, and provided targeted zoning and categorized policy suggestions. Our constructed theoretical framework modeled NGPCL by the complex interactions among geographical location, natural conditions, socioeconomic levels, and policy forces. From 2000 to 2022, NGPCL in Deqing County had a high degree and significantly increased from 68.65% to 89.19%. Pond farming and horticultural crops increased by 4046.46ha (38.0%) and 1307ha (27.1%), while tree planting slightly decreased by 54ha (-2.5%). However, these NGPCL expansions resulted in a drastic decrease of 5299ha (-65.5%) in grain crops plantation. Different NGPCL types showed varied spatial distribution characteristics and differentiated driving mechanism. Horticultural crops depended on geographical location and socioeconomic factors and were widely planted near towns and roads with high population density and economic strength. Pond farming mainly extended along the eastern flat rivers and relied on resource endowment. Tree planting was closely related to the natural condition and was concentrated in the sparsely populated mountainous areas. The NGPCL is the result of balancing various factors, and under conditions of extremely scarce cropland resources, the Chinese government needs to strike a reasonable threshold for non-grain production and propose targeted policies of zoning classification to prevent excessive NGPCL from threatening grain yield security.

Leveraging airborne imaging spectroscopy and multispectral satellite imagery to map glacial sediment plumes in Kachemak Bay, Alaska.

Study Region Kachemak Bay is a fjord-type estuary in the northern Gulf of Alaska. Water quality and habitat characteristics are strongly influenced by freshwater and sediment input from multiple glacierized catchments. Study Focus We present a new method combining imaging spectroscopy from an airborne survey with Landsat and Sentinel-2 imagery to map water surface turbidity originating from glacial runoff based on spectral abundance. We compare the spectral characteristics of turbid glacial water to clear water and generate a high resolution reference map of glacial turbidity in Kachemak Bay. This informs the subsequent analysis of a homogenized, Rayleigh corrected time series of Landsat and Sentinel-2 images and seasonal patterns of turbidity. New Hydrological Insights for the Region Our results provide the most comprehensive data set on water surface turbidity in Kachemak Bay to date and improve understanding of spatial and seasonal variability of glacial turbidity in a data sparse region. July and August have the largest plumes with median sizes around 150 km , or around a quarter of Kachemak Bay. Plume sizes typically decrease with decreasing glacier runoff in September and October. We show that imaging spectroscopy aids assessments of turbid water in glacial marine catchments across scales. Leveraging high resolution spectral information allows for water color analyses that are customized to local conditions and catchment characteristics as well as scalable to wider regions.

Forest fires under the lens: needleleaf index - a novel tool for satellite image analysis

Forest fires under the lens: needleleaf index - a novel tool for satellite image analysis

Remote Sensing-Based Assessment of the Long-Term Expansion of Shrimp Ponds Along the Coastal and Protected Areas of the Gulf of California

Shrimp farming has expanded over coastal areas in Mexico, particularly in the protected regions of Sonora and Sinaloa. Over the past 30 years, the economic activity associated with these farms has grown so much that the amount of shrimp produced in these ponds now exceeds that harvested from traditional shrimp fisheries. Establishing shrimp ponds has led to significant land changes. The construction of these ponds has fragmented local ecosystems, resulting in permanent alterations to areas such as floodplains, mangrove forests, and dunes, many of which are protected zones. This study aimed to investigate the long-term growth of shrimp farms from 1993 to 2022 and their impact on land-use changes in surrounding ecosystems, focusing on protected areas in the Sinaloa and Sonora coastal regions. We analyzed Landsat images using the Google Earth Engine platform. Our findings indicate that shrimp farm development over the past three decades has been extensive, with protected areas experiencing fragmentation and changes. Remote sensing and platforms like Google Earth Engine enable the effective monitoring of these spatiotemporal changes and their impacts, helping to identify the most affected areas.

An Arctic sea ice concentration data record on a 6.25 km polar stereographic grid from 3 years of Landsat-8 imagery

Abstract. The decline in Arctic sea ice in the global warming era has received much attention as a contributing factor to the changes in the weather and climate in the Arctic and beyond. The coverage of Arctic sea ice (i.e. sea ice concentration (SIC)) has been monitored since 1972 using satellite passive microwave (PMW) measurements because of their extensive coverage and all-weather capability. However, the fundamental basis of algorithms for estimating SIC has not improved much since the early days due to the lack of reference SIC data, leading to discrepancies between existing PMW SIC algorithms. To overcome this issue, this study aims to construct data records of reference SIC over Arctic sea ice using 30 m resolution imagery from the Operational Land Imager (OLI) on board Landsat-8. In order to collect relatively bright and clear scenes, thresholds of solar elevation >15° and cloud cover <10 % were applied in this study. Clouds in each Landsat-8 scene were masked using the cloud-masking array provided in Landsat data. Due to the poor accuracy of the cloud-masking array over ice-covered surface types, an additional step of visually inspecting the state of the cloud mask using the true-colour image was designated in this study. Each Landsat-8 scene was sorted into four categories depending on the state of the cloud mask. The normalized difference snow index and OLI band-5 reflectivity were used to differentiate between ice and open water within each selected Landsat-8 pixel. The classified data were projected onto a 6.25 km polar stereographic grid, and SIC for each grid cell was obtained by counting ice-classified pixels within the grid. SIC was only computed for grid cells where more than 99 % of their area was covered with Landsat-8 pixels to limit the uncertainty in SIC arising from grids that are not fully concentrated with Landsat-8 pixels. Uncertainty in the produced SIC was 1 %–4 %, inferred using the Gaussian error propagation method. Out of 15 286 collected Landsat-8 images, 14 297 images were translated into SIC maps, and a total of 2 934 399 Landsat-8 SIC grid cells were generated. Evaluation of Landsat-8 SIC with SIC from ice charts revealed a good linear relationship (correlation coefficient of 0.96) between the two products, as well as a mean negative bias which fell within the uncertainty range of Landsat-8 SIC. SIC based on Landsat-8 can be used as reference SIC to evaluate existing SIC products, and, thus, one can improve SIC products, as well as use the improved SIC for other applications such as data assimilation and retrieval studies. The vast amount of Landsat-8 SIC generated in this study may also be used to train deep-learning models for the estimation of Arctic SIC coverage. The Landsat-8 SIC dataset can be publicly accessed at https://doi.org/10.5281/zenodo.10973297 (Jung et al., 2024), and the Python codes for the production and evaluation of the Landsat-8 SIC dataset are accessible at https://doi.org/10.5281/zenodo.12754602 (Jung, 2024).

Predicting land use and land cover changes for sustainable land management using CA-Markov modelling and GIS techniques

This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies. Landsat images from various sensors (TM, OLI) were employed for supervised classification, attaining high accuracy (> 90%). Historical LULC changes from 1994 to 2024 were analyzed, revealing significant transformations in Lahore. The build-up area expanded by 359.8 km², indicating rapid urbanization, while vegetation cover decreased by 198.7 km² and barren lands by 158.5 km². Water bodies remained relatively stable during this period. Future LULC trends were projected for 2034 and 2044 using the CA-Markov hybrid model (CA-MHM), which achieved a high prediction accuracy with a kappa coefficient of 0.92. The research indicated significant urban growth at the expense of vegetation and barren land. Future forecasts suggest ongoing urbanization, underscoring the necessity for sustainable land management techniques. This research is a significant framework for urban planners, providing insights that combine development with ecological conservation. The results highlight the necessity of incorporating predictive models into urban policy to promote sustainable development and environmental preservation in quickly changing areas such as Lahore.

Insights into Landscape Structure Change in Urbanising Rainforest and Guinea Savanna Ecological Regions of Nigeria.

In the face of unabated urban expansion, understanding the intrinsic characteristics of landscape structure is pertinent to preserving ecological diversity and managing the supply of ecosystem services. This study integrates machine-learning-based geospatial and landscape ecological techniques to assess the dynamics of landscape structure in cities of the rainforest (Akure and Owerri) and Guinea savanna (Makurdi and Minna) ecological regions of Nigeria between 1986 and 2022. Supervised classification using the random forest (RF) machine-learning classifier was performed on Landsat images on the Google Earth Engine (GEE) platform, and landscape metrics were calculated with FRAGSTATS to assess landscape composition, configuration, and connectivity. The results reveal a consistent pattern of urban expansion in all four cities at varying intensities. The proportion of the built-up class exhibited positive correlations with the largest patch index (r = 0.86, p < 0.05) and aggregation (r = 0.39, p < 0.05), indicating a concurrent rise in landscape densification as urban expansion persists. For the agricultural and vegetation landscapes, landscape proportion correlates negatively with fragmentation (r = -0.88, p < 0.05) and connectivity (r = -0.77, p < 0.05), but positively with aggregation (r = 0.89, p < 0.05). The increased patch density indicates a rising magnitude of landscape fragmentation and heterogeneity over time with varying implications for ecosystem functioning. These findings demonstrate the complex interplay between urbanisation and ecological processes within and across different ecoregions, highlighting the need for targeted ecological management, sustainable urban planning, and regionally informed landscape conservation strategies.

Environmental impacts of urban growth and land use changes in tropical cities

Urban centers across the globe are undergoing rapid land use changes due to population growth, economic development, and urbanization. These changes have a profound impact on environmental dynamics and urban livelihoods. This study investigates land use changes and their environmental implications on urban livelihoods in Iwo, Nigeria. The research explores the patterns and drivers of land use changes over the past four decades and evaluates their effects on environmental quality and urban livelihoods. A combination of remote sensing and survey methods was employed. Landsat images from 1982 to 2023 were analyzed using supervised classification techniques to map changes in land cover categories such as built-up areas, vegetation, farmland, bare land, and water bodies. Additionally, a survey was conducted with 550 residents, selected through simple random sampling, to identify the key drivers of urban expansion from the residents’ perspectives. Descriptive analysis revealed that most respondents had lived in Iwo for over 30 years, with many engaged in farming, trading, and civil service. Factor analysis was used to identify significant variables driving urban growth. The results indicate a substantial increase in built-up areas, from 9.30 km2 in 1982 to 30.69 km2 in 2023, alongside a significant decrease in vegetation cover. Farmland area showed an increasing trend, while bare land decreased. Key drivers of urban growth identified include religious activities, availability of land resources, small-scale businesses, government initiatives, and educational institutions. The environmental implications include deforestation, reduced green spaces, increased surface runoff, and pollution, which affect air quality, water resources, and biodiversity. This study highlights the importance of sustainable urban planning to balance growth with environmental preservation and social equity. The implementation of green infrastructure, effective waste management, and comprehensive urban planning policies is crucial to enhancing resilience and quality of life in Iwo.

Dynamics of Built-Up Areas and Loss of Vegetation in Secondary Towns: Case Study of Sarh Town in Chad, Central Africa

The dynamics of the urbanisation of Sarh town in Chad, although less rapid than that of the capital city, has led to a significant loss of vegetation and unsustainable land use. This research aims to analyse the dynamics of land use, focusing on the expansion of built-up areas and the loss of vegetation. The methodology used includes the analysis of Landsat images from 1994, 2003, 2013, and 2022, supplemented by field data, statistical analysis, interviews, and documentary analysis. The results show that the built-up area, estimated at 806 hectares in 1994, reached 2603 hectares in 2022, representing an annual increase of 4.1%. Moreover, the area of vegetation decreased from 759 hectares to 231 hectares, a reduction of 69%. In addition, there is a strong negative correlation (r = −0.93) between the expansion of built-up areas and the loss of vegetation. On average, the annual growth of built-up areas (4.1%) exceeds that of the population (3.33%). Field surveys reveal that this situation is due to a preference for more spacious housing, inadequate land management, and the limited resources for vegetation rehabilitation. This research highlights the critical need for effective urban planning and management strategies to address the challenges posed by rapid urbanisation in secondary towns like Sarh.

Mangrove biomass productivity and sediment carbon storage assessment at selected sites in Mauritius: The effect of tidal inundation, forest age and mineral availability

Abstract CO2 is the most abundant anthropogenic greenhouse gas released in the environment and is considered one of the main drivers of global warming and ensuing climate change. Biological carbon sequestration mitigates CO2 through ecosystems like forests, wetlands, and agricultural lands. Mangrove forests, being highly carbon-dense ecosystems, sequester significant carbon in their biomass and soils. This study aims at evaluating the carbon storage potential of R.mucronatanatural and planted forests in Mauritius and the effect of tidal inundation and mineral availability.&amp;#xD;Plant height and diameter at breast height were measured in situ with the GLOBE Observer application and a measuring tape, respectively. Rate of canopy coverage over the past twenty years was assessed using historical Landsat images available on Google Earth Pro. Allometric equations were used to estimate the above-ground biomass (AGB) and below-ground biomass (BGB) of R.mucronata. Total organic carbon (TOC) and all essential nutrients for plant growth were analysed using standard methods. &amp;#xD;Our findings show that in both natural and planted forests, the more inundated zones were first established. However, tree and sapling density and AGB were negatively correlated with sodium (r = -0.830; -0.880, respectively). Positive correlations between AGB and NO3-N, NH4-N, phosphate, and manganese suggest that these minerals were limiting factors. Nevertheless, the combination of forest age and salinity was found to play key roles on the AGB and TOC, which is linked to materials originating from the mangroves. It is noteworthy that the Ferney forest with a relatively lower salinity (5-15 ppt) and the only forest that had already reached a steady state in 2010, had a relatively much higher AGB (326.2±26.3 t ha-1) than the global average for R.mucronata (94.8 t ha-1), let alone Rhizophora spp. (281 t ha-1). The TOC registered at Ferney (47.34%) was also higher than the global values reported (2.00±2.20% to 40.00±2.20%)

Geomodeling soil quality using remote sensing and land use metrics in Abeokuta, Nigeria

Remote sensing techniques offer potent and accurate geospatial modeling of Soil quality (SQ) for sustainable food security and development. The study aims to model SQ and landscape changes in Federal University of Agriculture Abeokuta, Nigeria using Landsat images of 2014 and 2024. We applied support vector machine and land use cover metrics like Number patches (NP), Largest-path (LP), and Effective—MESH combined with spectra Bare-soil (BSI), Normalized Difference Vegetation (NDVI), and Soil Adjusted Vegetation (SAVI) Indexes to assess land use and land cover, focusing on built-up, vegetation and wetlands of the area. A composite of 70 soil samples (0 to 30 cm) were analyzed for their physical, chemical, and biological soil properties. The additive, weighted-additive, and proposed models were used to estimate SQ of the studied area. Results showed that NP, LP, and MESH revealed substantial discontinuity and landscape fragmentation especially in the built-up areas. At the same time, BSI, NDVI, and SAVI signify shifts in wetland areas and significant decrease in vegetation cover of the area respectively. The models ranked SQ of the area as high (199 ha), moderate (1333 ha), and low (126 ha) hectares, respectively. The findings suggest valuable ways for SQ improvement and could contribute to efforts on achieving food security, well-being and meet sustainable development goal 2.

Harnessing complexity: integrating remote sensing and fuzzy expert system for evaluating land use land cover changes and identifying mangrove forest vulnerability in Bangladesh

Purpose: This study analyzes Landsat images to examine the alterations in land cover within the Sundarbans and its surrounding regions in Bangladesh, spanning twenty-one years from 2000 to 2021. Furthermore, we develop a mangrove vulnerability map considering the combined effect of eight socioeconomic, geophysical, and climatic factors. Methods: Land use land cover (LULC) changes in the study area over a 21-year period were assessed using a random forest model, and the vulnerability analysis employed a fuzzy expert-based multicriteria decision-making (MCDM) approach. Results: The results show that a significant portion of the mangrove forest has been transformed into aquaculture practices because of the expansion of high-value shrimp cultivation. A decrease in forest areas and the expansion of aquaculture zones suggest a livelihood shift among the local population over time. This transition has adversely affected human activities within the ecosystem and the biodiversity of mangrove forests. Consequently, it is imperative to implement suitable measures to enhance the state of mangrove forests and safeguard their biodiversity. The vulnerability analysis shows that the highly vulnerable, moderately vulnerable, and low vulnerable areas cover 35.66%, 26.86%, and 19.42%, respectively. Conclusion: The vulnerability maps generated in this research could serve as a valuable resource for coastal planners seeking to ensure the sustainable stewardship of these coastal mangrove forests. These results offer a detailed understanding of coastal mangrove LULC patterns and vulnerability status, which will be useful for policymakers and resource managers to urgently incorporate into coastal land use and environmental management practices.

Spatiotemporal patterns of annual clear-cutting distribution in tropical and subtropical regions of China with time series Landsat and CCDC

ABSTRACT Spatial and temporal clear-cutting distribution is an important data source for investigating forest dynamics and formulating policies related to timber production, land use, and sustainable development. Subtropical and tropical regions of China due to their favorable weather conditions for tree growth are important timber providers, but the spatial distribution of annual clear-cutting areas was unavailable. This research leveraged dense time-series Landsat images (1986–2022) and a novel approach combining Continuous Change Detection and Classification (CCDC) with Random Forest to produce the first annual 30 m resolution clear-cutting map for the China’s subtropical and tropical regions. The per-polygon approach based on the sub-compartment and field survey data was used to evaluate the developed clear-cutting product. The spatiotemporal distribution of clear-cutting areas was further analyzed along longitude and latitude as well as in different provinces. The results indicated that the combination of CCDC and Random Forest successfully detected spatial and temporal distribution of clear-cutting areas with an overall accuracy of 75.6% based on sub-compartments and 93.3% based on field polygons. The cumulative clear-cutting area between 1987 and 2021 was approximately 21.26 × 106 ha, with a mean annual clear-cutting area of 0.61 × 106 ha in China’s subtropics and tropics. About 10.24% of subtropical/tropical vegetation experienced clear-cutting and most areas (85.94%) were clear-cut once. Clear-cutting activities gradually clustered at low latitudes and moderate longitudes, represented by Guangxi Province. More frequent clear-cutting was observed during the later period (2001–2021). This research contributes to an improved understanding of forest dynamics over time at large scale, and provides scientific data to make proper decisions for forest sustainability.

High-resolution mapping of global winter-triticeae crops using a sample-free identification method

Abstract. Winter-triticeae crops, such as winter wheat, winter barley, winter rye and triticale, are important in human diets and are planted worldwide, and thus accurate spatial distribution information on winter-triticeae crops is crucial for monitoring crop production and food security. However, there is still a lack of global high-resolution maps of winter-triticeae crops because of the reliance of existing crop mapping methods on training samples, which limits their application at the global scale. In this study, we propose a new method based on the Winter-Triticeae Crops Index (WTCI) for global winter-triticeae crop mapping. This is a new sample-free method for identifying winter-triticeae crops based on differences in their normalized difference vegetation index (NDVI) characteristics from the heading to harvesting stages and those of other types of vegetation. We considered state (or province) or country to be an identification unit and employed the WTCI to produce the first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 using Landsat and Sentinel images. Validation using field survey samples and visual interpretation samples from Google Earth images indicated that the method exhibited satisfying performance and stable spatiotemporal transferability, with producer's accuracy, user's accuracy and overall accuracy values of 81.12 %, 87.85 % and 87.7 %, respectively. Moreover, compared with the Cropland Data Layer (CDL) and EuroCrops datasets, the overall accuracy and F1 score in most regions of the United States and Europe were more than 80 % and 75 %, respectively. The identified area of winter-triticeae crops was consistent with the agricultural statistical area in almost all the investigated countries or regions, and the correlation coefficient (R2) between the identified area and the statistical area was over 0.6, while the relative mean absolute error (RMAE) was less than 30 % in all 6 years. Overall, this study provides a reliable and automatic identification method for winter-triticeae crops without any training samples. The high-resolution distribution maps of global winter-triticeae crops are expected to support multiple agricultural applications. The distribution maps can be obtained at https://doi.org/10.57760/sciencedb.12361 (Fu et al., 2023a).

Multi-temporal analysis of land use change using GIS and satellite imagery: Implications for sustainable urban planning

Urbanization is a significant driver of land use change, particularly in rapidly growing metropolitan areas. This research investigates Greenfield City land use change in the 20-year period (2000-2020) using GIS and satellite data. The mapping shows where the greatest land-use changes occurred, ranging from increased residential and commercial developments to the loss of agricultural fields and the omission of green space. The work applies multi-temporal analyses of Landsat satellite images taken in 2000, 2010 and 2020 to estimate land cover change and its effects on urban planning and sustainability. They indicate that theres a clear rise in housing and business developments, but also a steep decline in farming and greenspace. These transformations affect the environment, with habitat loss, biodiversity destruction and encroachment on natural resources. The paper wraps up by focusing on the issues of sustainability in urban planning and how better land use planning is required to reduce the negative environmental effects of urban sprawl.

Landscape Transformations (1987–2022): Analyzing Spatial Changes Driven by Mining Activities in Ayapel, Colombia

Gold mining is an activity that has developed in Colombia due to the great availability of mineral resources geographically distributed throughout the territory. The extraction techniques used are linked to the domain of illegality and to armed actors who have generated notable landscape impacts. This study, focused on the Municipality of Ayapel, Colombia, identifies the landscape units and analyzes the changes in land use and cover resulting from gold mining between the years 1987, 2002, and 2022, applying the CORINE Land Cover methodology, an adapted legend for Colombia, using Landsat satellite images. For this, the recognition of the physical geographical characteristics of the area was carried out in order to group homogeneous landscape units through a cartographic overlay of various layers of information, considering variables such as topography, geomorphology, and lithology. This research identifies a total of 16 landscape units, 8 of which were intervened in 1987, mainly associated with denudational hills. However, in 2022, 13 landscape units were intervened, with a considerable increase in the affected area. Particularly noteworthy is the occupation of landscape units associated with alluvial valleys, with an average of more than 30% of their total area. This demonstrates that they are the most attractive and vulnerable areas for mining exploitation, as they are the zones with the greatest potential for hosting mineral deposits. This impact has worsened over the last decade due to the introduction of other extraction techniques with machinery (dredges, dragon boats, backhoes, and bulldozers) that generate higher productive and economic yields but, at the same time, cause deep environmental liabilities due to the lack of administrative controls. The changes in extraction techniques, the increase in the international price of the commodity, and the absence of government attention have been the breeding ground that has driven gold mining activity.

Fast Expansion of Surface Water Extent in Coastal Chinese Mainland from the 1980s to 2020 Based on Remote Sensing Monitoring

High-resolution satellite imagery providing long-term, continuous information on surface water extent in highly developed regions is paramount for elucidating the spatiotemporal dynamics of water bodies. The landscape of water bodies is a key indicator of water quality and ecological services. In this study, we analyzed surface water dynamics, including rivers, lakes, and reservoirs, using Landsat images spanning from the 1980s to 2020, with a focus on the highly developed Coastal Chinese Mainland (CCM) region. Our objectives were to investigate the temporal and spatial variations in surface water area extent and landscape characteristics, to explore the driving forces behind these variations, to gain insights into the complex interactions between water bodies and evolving environmental conditions, and ultimately to support sustainable development in coastal regions. Our findings revealed that reservoirs constitute the largest proportion of surface water, while lakes occupy the smallest share. Notably, a trend of expansion in surface water extent in the CCM was observed, mainly from the construction of new reservoirs. These reservoirs primarily gained new areas from agricultural land and river floodplains in the early stages (1980s–2000), while a greater proportion of construction land was encroached upon by reservoirs in later periods (2001–2020). At the landscape level, a tendency toward fragmentation and complexity in surface water, particularly in reservoirs, was evident. Human interference, particularly urbanization, played a pivotal role in driving the expansion of water surfaces. While reservoir construction benefits water resource assurance, flood control, and prevention, it also poses eco-hydrological challenges, including water quality deterioration, reduced hydrological connectivity, and aquatic ecosystem degradation. The findings of this study provide essential data support for sustainable water resource development. These insights underscore the urgency and importance of integrated water resource management strategies, particularly in efforts aimed at conservation and restoration of natural water bodies and the scientific regulation of artificial water bodies. Balancing human development needs with the preservation of ecological integrity is crucial to facilitating a water resource management strategy that integrates climatic and socio-economic dimensions, ensuring sustainable water use and protection for future generations.

Assessing cropping system dynamics over three decades: remote sensing and GIS insights in Murshidabad-Jiaganj Block.

Agriculture is a significant contributor to the country's economic development. We used multiple Landsat images from 1990 to 2021 in the Murshidabad-Jiaganj Block to assess changes in the agricultural system and their underlying causes. The Rabi season saw a 10.99% growth in agrarian regions from 1990 to 2000 and an 8.86% increase in 2010, yet it declined by 28.12% in 2021. During the summer, the cultivated lands diminished by 26.63%, 19.43%, and 19.64%, while in the Kharif season, they declined by 21.78%, 15.68%, and 11.99% from 1990 in the years 2000, 2010, and 2021, respectively. The agricultural area had 36.82%, 34.16%, and 19.01% increases between 1990 and 2021, respectively. Regarding direction, farmland acreage decreased in all zones except the SSE, which had a 0.95% increase. Mono-, double-, and triple-cropping systems have decreased in area, while multi-cropping systems have experienced increases of 43.51%, 4.50%, and 18.49% in 1990-2021, respectively. The multi-cropping system has a good correlation with all agroclimatic factors. The reduction of irrigated lands post-2009 significantly affected the agriculture system. The fall in agricultural employment in recent decades is attributable to migration seeking higher-paying occupations. The advancement of accurate remote sensing-based modeling is crucial for mitigating food security risks, particularly those posed by climate change, and informing policy decisions.