Search Results (38)

Land-Cover and Land-Use Change (LCLUC) is a dynamic process affected by the combination and mutual interaction of climatic and socioeconomic drivers. Field studies and surveys, which are typically time- and resource-consuming, have been employed by researchers to better understand LCLUC drivers. However, remotely sensed data may provide the same trustworthy outcomes with less time and expense. This study aimed to assess the relationship between LCLUC and changes in socioeconomic and climatic factors in the Dallas-Fort Worth (DFW) metropolitan area, Texas, USA, between 2000 and 2020. The LCLU, socioeconomic, and climatic data were obtained from the National Land Cover Database of Multi-Resolution Land Characteristics Consortium, NASA’s Socioeconomic Data and Applications Center (SEDAC), and the global climate and weather data website (WorldClim), respectively. Change detection calculated from these data was used to analyze spatial and statistical relationships between LCLUC and changes in socioeconomic and climatic factors. Results showed that LCLUC was significantly predicted by population change, housing and transportation, household and disability change, socioeconomic status change, monthly average minimum temperature change, and monthly mean precipitation change. While socioeconomic factors played a predominant role in driving LCLUC in this study, the influence of climatic factors should not be overlooked, particularly in regions where climate sensitivity is more pronounced, such as arid or transitional zones. These findings highlight the importance of considering regional variability when assessing LCLUC drivers. Full article

This study investigates the potential of gradient-weighted class activation mapping (Grad-CAM++) in enhancing land cover and land use (LCLU) classification using deep learning models. A U-Net and an Attention U-Net model were trained on Sentinel-2 imagery to classify 10 LCLU classes in a study area in Northern Ontario, Canada (centered at 49.17° N, 83.03° W). The classes included water, wetland, deciduous forest, mixed forest, coniferous forest, barren, urban/development, agriculture, shrubland, and no data (masked areas). The U-Net model achieved overall accuracy of 70.68%, a mean intersection over union (IoU) of 0.4852, and an F1 score of 0.7150, slightly outperforming the Attention U-Net model. Grad-CAM++ visualizations revealed that both models correctly focused on relevant features for each LCLU class, enhancing the interpretability of deep learning models in remote sensing applications. The findings suggest that integrating Grad-CAM++ with deep learning architectures can improve model transparency and guide future enhancements in LCLU classification tasks. Full article

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning and development. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by rapid population growth. Over the past decade, multiple projects have been undertaken to produce regional and national land cover maps. However, using different classification systems and legends has made updating and sharing land cover information challenging. This has resulted in the inefficient use of human and financial resources. The development of the Land Cover Meta Language (LCML) based on International Organization for Standardization (ISO) standards offers an opportunity to create a standardized classification system. This system would enable easier integration of regional and national data, efficient management of information, and better resource utilization in West Africa. This article emphasizes the process and the need for multistakeholder collaboration in developing a standardized land cover classification system for West Africa, which is currently nonexistent. It presents the survey data collected to evaluate historical, current, and future land cover mapping projects in the region and provides relevant use cases as examples for operationalizing a standardized land cover classification legend for West Africa. Full article

A new methodology to map Italian coastal areas at risk of flooding is presented. This approach relies on detailed projections of the future sea level from a high-resolution, three-dimensional model of the Mediterranean Sea circulation, on the best available digital terrain model of the Italian coasts, and on the most advanced satellite-derived data of ground motion, provided by the European Ground Motion Service of Copernicus. To obtain a reliable understanding of coastal evolution, future sea level projections and estimates of the future vertical ground motion based on the currently available data were combined and spread over the digital terrain model, using a GIS-based approach specifically developed for this work. The coastal plains of Piombino-Follonica and Marina di Campo (Tuscany Region), Alghero-Fertilia (Sardinia), and Rome and Latina-Sabaudia (Lazio Region) were selected as test cases for the new approach. These coastal stretches are important for the ecosystems and the economic activities they host and are relatively stable areas from a geological point of view. Flood maps were constructed for these areas, for the reference periods 2010–2040, 2040–2070, and 2040–2099. Where possible, the new maps were compared with previous results, highlighting differences that are mainly due to the more refined and resolved sea-level projection and to the detailed Copernicus ground motion data. Coastal flooding was simulated by using the “bathtub” approach without considering the morphodynamic processes induced by waves and currents during the inundation process. The inundation zone was represented by the water level raised on a coastal DTM, selecting all vulnerable areas that were below the predicted new water level. Consequent risk was related to the exposed asset. Full article

Great Lakes Basin landscapes are undergoing rapid land cover and land use (LCLU) change. The goal for this study was to identify changes in land cover occurring in the Great Lakes Basin over three time periods to provide insights into historical land cover changes occurring on a bi-national watershed scale. To quantify potential impacts of anthropogenic changes on important yet vulnerable Great Lakes Wetland ecosystems, the historical changes in land cover over time are assessed via remote sensing. The goal is to better understand legacy effects on current conditions, including wetland gain and loss and the impacts of upland ecosystems on wetland health and water quality. Three key time periods with respect to Great Lakes water level changes and coastal wetland plant invasions were mapped using Landsat-derived land cover maps: 1985, 1995, and 2010. To address change between the three time periods of interest, we incorporate both radiometric and categorical change analysis and open-source tools available for assessing time series data including LandTrendr and TimeSync. Results include maps of annual land cover transition from 1985 to 1995 and 1995 to 2010 basin-wide and by ecoregion and an assessment of the magnitude and direction of change by land cover type. Basin-wide validated change results show approximately 776,854 ha of land changed from c.1980–1995 and approximately 998,400 ha of land changed from c.1995–2010. Both time periods displayed large net decreases in both deciduous forest and agricultural land and net increases in suburban cover. Change by ecoregion is reviewed in this study with many of the change types in central plains showing change in and out of agriculture and suburban land covers, the mixed wood plain ecoregion consisted of a mixture of agricultural, suburban, and forestry changes, and all top five change types in the mixed wood shield consisted of various stages of the forestry cycle for both time periods. In comparison with previous LCLU change studies, overall change products showed similar trends. The discussion reviews why, while most changes had accuracies better than 84%, accuracies found for change from urban to other classes and from other classes to agriculture were lower due to unique aspects of change in these classes which are not relevant for most change analyses applications. The study found a consistent loss in the deciduous forest area for much of the time studied, which is shown to influence the aquatic nitrogen implicated in the expansion of the invasive plant Phragmites australis in the Great Lakes Basin. This underscores the importance of LCLU maps, which allow for the quantification of historical land change in the watersheds of the Great Lakes where invasive species are expanding. Full article

The components of water balance (WBC) that involve precipitation, evapotranspiration, runoff, irrigation, and groundwater recharge are critical for understanding the hydrological cycle and water management of resources in semi-arid and arid areas. This paper assesses temporal and spatial distributions of surface runoff, actual evapotranspiration, and groundwater recharge upstream of the New Assiut Barrage (NAB) in the Nile Valley, Upper Egypt, using the WetSpass-M model for the period 2012–2020. Moreover, this study evaluates the effect of land cover/land use (LULC) alterations in the study period on the WBC of the NAB. The data provided as input for the WetSpass-M model in the structure of raster maps using the Arc-GIS tool. Monthly meteorological factors (e.g., temperature, rainfall, and wind speed), a digital elevation model (DEM), slope, land cover, irrigation cover, a soil map, and depth to groundwater are included. The long-term temporal and spatial mean monthly irrigation and precipitation (127 mm) is distributed as 49% (62 mm) actual evapotranspiration, 15% (19 mm) groundwater recharge, and 36% (46 mm) surface runoff. The replacement of cropland by built-up areas was recognized as the primary factor responsible for the major decrease in groundwater, an increase in evapotranspiration and an increase in surface runoff between LCLU in 2012 and 2020. The integration of the WetSpass model with GIS has shown its effectiveness as a powerful approach for assessing WBC. Results were more accurate and reliable when hydrological modeling and spatial analysis were combined. The results of this research can help make well-informed decisions about land use planning and sustainable management of water resources in the upstream area of the NAB. Full article

In Kibira National Park, Burundi, socioeconomic and demographic factors lead to environmental performance challenges that impede biodiversity; thus, understanding the impact of these determinants on land use and land cover change is important to address these challenges. In this study, a multivariate analysis of variance (MANOVA) model was used to quantify the impact of socioeconomic and demographic factors on land cover/land use (LCLU) changes using Landsat images captured between 1990 and 2021. In addition, the impact of the environmental performance index (EPI), particularly ecosystem vitality (ECO), on landscape fragmentation was examined using a Spearman correlation analysis. A Pearson correlation analysis and a principal component analysis (PCA) were used to investigate the connections between the indicators of relevance in this study. The results reveal a decrease in forestland from 86.1% to 81.32%, a decrease in water bodies from 0.352% to 0.178%, and a decrease in open land from 2.124% to 1.134%, whereas grassland increased from 11.43% to 17.37% between 1990 and 2021. The landscape fragmentation in the edge density, contagion (CONTAG), largest patch index (LPI), number of patches (NP), and patch density (PD) was reduced in 2011 but increased again from 2016 to 2021, when only the ED fragmentation continued to decrease. The MANOVA results show that the rural population had a significant impact on LCLU changes at the 5% level of significance. Demographic factors significantly contributed to changes in grassland and forestland at a probability of 5%. In addition, moderately significant connections were observed between population growth per year and water and between gross domestic product (GDP) and grassland at the 10% level. ECO issues in ecosystem services (ECSs) were statistically significant for the increased fragmentation metrics, while biodiversity and habitat (BDH) were important for reducing the edge density (ED) at a 5% level of significance. The Pearson correlations showed a substantial positive relationship between the socioeconomic and demographic components, whereas a negative connection was found between the forestland and BDH indicators. These findings are essential for understanding the significant drivers of LCLU changes and the influence of environmental performance on the landscape pattern. Full article

A prevalent engineering task in practice is calculating the annual balance of sediments on some watercourses. This is particularly challenging when assessing the backfilling of river reservoirs that have a multifunctional purpose. Trakošćan Lake was built in the period from 1850 to 1862 as a pond and landscape addition to the park and Trakošćan castle. After 60 years, the lake was drained in 2022, and the work began on sediment excavation to improve the lake’s ecological condition due to about 200,000 cubic meters of deposited silt in the lake. In this research, the annual sediment production is calculated for the long-term period 1961–2020, based on empirical parametric methods (Fleming, Brunne). The results are compared with results from previous projects and recent sediment deposit investigations. Since there are no changes in LC/LU on this natural catchment, the decreasing trends in long-term sediment transport were compared with meteorological values, daily rainfall, and snow days. It is concluded that the intensity characteristics of the rainfall should be investigated more in detail and could provide much more tangible information regarding climate change impacts. Some targets for future monitoring design and research techniques are set. Full article

Land cover land use (LCLU) products provide essential information for numerous environmental and human studies. Here, we assess the accuracy of eleven global and regional products over the conterminous U.S. using 25,000 high-confidence randomly distributed samples. Results show that in general, the National Land Cover Database (NLCD) and the Land Change Monitoring, Assessment and Projection (LCMAP) outperform other multi-class products, both in terms of higher individual class accuracy and with accuracy variability across classes. More specifically, F1 accuracy comparisons between the best performing USGS and non-USGS products indicate: (i) similar performance for the water class, (ii) USGS product outperformance in the developed (+1.3%), grass/shrub (+3.2%) and tree cover (+4.2%) classes, and (iii) non-USGS product (WorldCover) gains in the cropland (+5.1%) class. The NLCD and LCMAP also outperformed specialized single-class products, such as the Hansen Global Forest Change, the Cropland Data Layer and the Global Artificial Impervious Areas, while offering comparable results to the Global Surface Water Dynamics product. Spatial visualizations also allowed accuracy comparisons across different geographic areas. In general, the NLCD and LCMAP have disagreements mainly in the middle and southeastern part of conterminous U.S. while Esri, WorldCover and Dynamic World have most errors in the western U.S. Comparisons were also undertaken on a subset of the reference data, called spatial edge samples, that identifies samples surrounded by neighboring samples of different class labels, thus excluding easy-to-classify homogenous areas. There, the WorldCover product offers higher accuracies for the highly dynamic grass/shrub (+4.4%) and cropland (+8.1%) classes when compared to the NLCD and LCMAP products. An important conclusion while looking at these challenging samples is that except for the tree class (78%), the best performing products per class range in accuracy between 55% and 70%, which suggests that there is substantial room for improvement. Full article

The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats. Full article

The characteristics of dissolved organic matter (DOM) and its relationships with other environmental factors are beneficial for comprehending water pollution in watersheds. This study aimed to improve our understanding of the association of DOM with water quality by connecting the spectroscopic characteristics of DOM with land cover and land use (LCLU). Clustering the tributaries of the Miho upstream watershed according to LCLU resulted in Clusters 1 and 2 having a large proportion of farmland and a large forest area, respectively. Various fluorescence indices derived from fluorescence excitation-emission matrix spectra revealed that livestock effluent resulted in the enrichment of autochthonous organic matter of algal or microbial origin in catchment areas with a high proportion of farmland. Furthermore, to analyze water quality changes according to the land-use characteristics, the water quality and spectroscopic characteristics of DOM were utilized based on the period of farmland use. Further correlation analysis indicated a high correlation between the fluorescence index (FI) in Cluster 1 and organic matter parameters and nitrogenous pollution (Total nitrogen (TN), Dissolved total nitrogen (DTN) and Nitrate nitrogen (NO₃-N)) (planting season, r = 0.991, post-planting season, r = 0.971). This suggests that the FI can be used as a surrogate to estimate the degree of water pollution in watersheds largely affected by land uses related to agricultural activity and the livestock industries. Full article

High-resolution remote sensing images with rich land surface structure can provide data support for accurately understanding more detailed change information of land cover and land use (LCLU) at different times. In this study, we present a novel scene change understanding framework for remote sensing which includes scene classification and change detection. To enhance the feature representation of images in scene classification, a robust label semantic relation learning (LSRL) network based on EfficientNet is presented for scene classification. It consists of a semantic relation learning module based on graph convolutional networks and a joint expression learning framework based on similarity. Since the bi-temporal remote sensing image pairs include spectral information in both temporal and spatial dimensions, land cover and land use change monitoring can be improved by using the relationship between different spatial and temporal locations. Therefore, a change detection method based on swin transformer blocks (STB-CD) is presented to obtain contextual relationships between targets. The experimental results on the LEVIR-CD, NWPU-RESISC45, and AID datasets demonstrate the superiority of LSRL and STB-CD over other state-of-the-art methods. Full article

This study investigated the performances of different techniques, including random forest (RF), support vector machine (SVM), maximum entropy (maxENT), gradient-boosting machine (GBM), and logistic regression (LR), for landslide susceptibility mapping (LSM) in the rugged terrain of northern Pakistan. Initially, a landslide inventory of 200 samples was produced along with an additional 200 samples indicating nonlandslide areas and divided into training (70%) and validation (30%) groups using a stratified loop-based random sampling approach. Then, a geospatial database of 12 possible landslide influencing factors (LIFs) was generated, including elevation, slope, aspect, topographic wetness index (TWI), topographic position index (TPI), distance to drainage, distance to fault, distance to road, normalized difference vegetation index (NDVI), rainfall, land cover/land use (LCLU), and a geological map of the study area. None of the LIFs were redundant for the modeling, as indicated by the multicollinearity test (tolerance > 0.1) and information gain ratio (IGR > 0). We extended the evaluation measures of each algorithm from area-under-the-curve (AUC) analysis to the calculation of performance overall (POA) with the help of precision, recall, F1 score, accuracy (ACC), and Matthew’s correlation coefficient (MCC). The results showed that the SVM was the most promising model (AUC = 0.969, POA = 2669) for the LSM, followed by RF (AUC = 0.967, POA = 2656), GBM (AUC = 0.967, POA = 2623), maxENT (AUC = 0.872, POA = 1761), and LR (AUC = 0.836, POA = 1299). It is important to note that the SVM, RF, and GBM were the top performers, with almost similar accuracy. Thus, each of these could be equally effective for LSM and can be used for risk reduction and mitigation measures in the rugged terrain of Pakistan and other regions with similar topography. Full article

As natural wetlands are degrading worldwide, artificial wetlands can operate as a substitute to provide waterbirds with refuge, but they cannot replace natural wetlands. Reservoirs, one of the most common artificial wetlands in China, can be of great importance to waterbirds. Miyun reservoir in Beijing, China, has undergone a process similar to a natural lake being constructed in a reservoir. In this study, we surveyed waterbird community composition and evaluated the corresponding land cover and land use change with satellite and digital elevation model images of both before and after the water level change. The results showed that in all modelled scenarios, when the water level rises, agricultural lands suffer the greatest loss, with wetlands and forests following. The water level rise also caused a decrease in shallow water areas and a decline in the number and diversity of waterbird communities, as the components shifted from a shallow-water preferring group (waders, geese and dabbling ducks) to a deep-water preferring group (most diving ducks, gulls and terns). Miyun reservoir ceased to be an important waterbird habitat in China and is no longer an important stopover site for white-naped cranes. A similar process is likely to occur when a natural lake is constructed in a reservoir. Therefore, we suggest that policymakers consider the needs of waterbirds when constructing or managing reservoirs. Full article

Land cover/land use (LCLU) is currently a very important topic, especially for coastal areas that connect the land and the coast and tend to change frequently. LCLU plays a crucial role in land and territory planning and management tasks. This study aims to complement information on the types and rates of LCLU multiannual changes with the distributions, rates, and consequences of these changes in the Crozon Peninsula, a highly fragmented coastal area. To evaluate the multiannual change detection (CD) capabilities using high-resolution (HR) satellite imagery, we implemented three remote sensing algorithms: a support vector machine (SVM), a random forest (RF) combined with geographic object-based image analysis techniques (GEOBIA), and a convolutional neural network (CNN), with SPOT 5 and Sentinel 2 data from 2007 and 2018. Accurate and timely CD is the most important aspect of this process. Although all algorithms were indicated as efficient in our study, with accuracy indices between 70% and 90%, the CNN had significantly higher accuracy than the SVM and RF, up to 90%. The inclusion of the CNN significantly improved the classification performance (5–10% increase in the overall accuracy) compared with the SVM and RF classifiers applied in our study. The CNN eliminated some of the confusion that characterizes a coastal area. Through the study of CD results by post-classification comparison (PCC), multiple changes in LCLU could be observed between 2007 and 2018: both the cultivated and non-vegetated areas increased, accompanied by high deforestation, which could be explained by the high rate of urbanization in the peninsula. Full article