Land Use And Land Cover Maps Research Articles

Dynamic assessment and prediction of land use alterations influence on ecosystem service value: A pathway to environmental sustainability

In the face of rapid urbanization, understanding and managing changes in Land Use and Land Cover (LULC) is crucial for the sustainability and resilience of urban ecosystems. Rapid urban growth resulting from changes in LULC, can lead to the fragmentation and degradation of Ecosystem Services (ES), including habitat provision, water regulation, and air purification. This study investigates LULC changes in Dhaka City, Bangladesh, from 2010 to 2021 and forecasts their future impacts on ES in 2030 and 2040. Landsat images were processed using the Google Earth Engine platform, and a cellular automata technique was integrated with an Artificial Neural Network (ANN) for LULC mapping and prediction. Results showed an increase in built-up areas from 31% to 42% between 2010 and 2021, with a projected decrease to 34% and 32% in 2030 and 2040, respectively. Water bodies, vegetation, and agricultural land declined from 2010 to 2021 but are expected to increase in 2030 and 2040. This trend indicates a growing awareness of the need for a sustainable environment and resilience in urban ecosystem development. The value of ES is predicted to drop from $172.66 to $160.23 over thirty years (2010–2040), primarily due to the degradation of water bodies ($7.64) and agricultural land ($6.89). However, the projected increase in water bodies, vegetation, and agricultural land by 2040 suggests a potential for recovery and enhancement of these ES values through sustainable practices. Future land use policies should promote sustainable agriculture and water resource management while fostering resilience against rapid urban growth. This approach will ensure the sustainable development of urban ecosystems. This study informs future land use strategies in Dhaka City, supporting sustainable natural resource management, urban ecosystem quality management decisions, and resilience planning. It underscores the importance of integrating sustainability and resilience into urban development policies for the benefit of both the environment and the city's inhabitants.

Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting

Accurate land use and land cover (LULC) is crucial for sustainable urban planning and for many scientific researches. However, the demand for accurate LULC maps is increasing; it is required to compare the classification algorithms to choose the best one. Though, machine and deep learning algorithms are widely used across the world their application is limited in Bangladesh. Accurate urban LULC mapping is challenging because urban heterogeneity affects image classification models in specific feature extraction. In this research, the accuracy of machine learning algorithms (MLA) of RF (Random Forest), SVM (Support Vector Machine), deep learning algorithm (DLA) of ANN (Artificial Neural Network) and traditional Maximum Likelihood (MaxL) method was compared in LULC classification of Dhaka city. Model accuracy of MLA and DLA was tested by statistical indices of sensitivity, specificity, precision, recall F1 etc. There is a high correlation between SVM and ANN models were found. The overall accuracy of the maps was 0.93, 0.94, 0.91 and 0.95 and kappa was 0.89, 0.91, 0.86 and 0.93 for the MaxL, RF, SVM and ANN models respectively. The user accuracy and producer accuracy largely varied according to LULC classes in the applied models. The lowest accuracy of the models was found for bare land classification followed by built-up and vegetation. The high mixture of LULC classes affects the accuracy of built up and bare land classification which produces the lowest accuracy in the MaxL model. The findings indicate that the most accurate and reliable model for urban LULC classification was the ANN model.

Modeling information flow from multispectral remote sensing images to land use and land cover maps for understanding classification mechanism

ABSTRACTInformation on Land Use and Land Cover Map (LULCM) is essential for environment and socioeconomic applications. Such maps are generally derived from Multispectral Remote Sensing Images (MRSI) via classification. The classification process can be described as information flow from images to maps through a trained classifier. Characterizing the information flow is essential for understanding the classification mechanism, providing solutions that address such theoretical issues as “what is the maximum number of classes that can be classified from a given MRSI?” and “how much information gain can be obtained?” Consequently, two interesting questions naturally arise, i.e. (i) How can we characterize the information flow? and (ii) What is the mathematical form of the information flow? To answer these two questions, this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM. This hypothesis is then supported by kinetic-theory-based experiments. Thereafter, upon such an entropy, a generalized Jarzynski equation is formulated to mathematically model the information flow, which contains such parameters as thermodynamic entropy of MRSI, thermodynamic entropy of LULCM, weighted F1-score (classification accuracy), and total number of classes. This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers. This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification, opening a new door for constructing domain knowledge.

Assessing future flood inundation in Nandigama through land use, land cover, and rainfall analysis

Abstract Frequent occurrences of high-intensity rainfall have made urban flooding a significant problem. In the present study, flood inundation maps were prepared for the years 2030 and 2035 based on changes in future land use and land cover (LULC) and rainfall patterns in Nandigama, located in Andhra Pradesh, India. The Hydrologic Engineering Center's Hydrologic Modelling System Hydrologic and Hydrologic Engineering Center–River Analysis System hydraulic models were used to assess future runoff and flood inundation areas for the above years. Future LULC and hydro-meteorological data were analysed and incorporated into the models. Predicted LULC maps showed a high level of agreement with actual LULC maps, as indicated by a Kappa index of 88.34%. The average peak runoff flowing out of the basin increased by 1.82%–3.43% for years 2030–2035, respectively. When considering the average inundation percentage area in Nandigama, it was found that 12.73% and 13.83% of the area flooded in both years. The study recommendations give more attention to the Nandigama flood management authority.

Using Satellite Data to Characterize Land Surface Processes in Morocco

This study endeavors to produce a comprehensive land cover map for Morocco, addressing the absence of such a detailed map in the country. Our research encompasses ecological and climatic aspects specific to Morocco, while the methods used can be adapted to various regions and countries, considering their unique climatic conditions and land cover types. A combination of MODIS and Landsat datasets was employed to create a 5 km resolution Land Use and Land Cover (LULC) map for the entire nation. The process involved the aggregation and advanced processing of these datasets using surface processes algorithms. The resulting LULC map is the first of its kind for Morocco, shedding light on land cover distribution nationwide. It shows that approximately 13.5% of the country is covered by forests, predominantly in the Atlas and Rif mountains, Rabat–Sale, and the southern regions. Grasslands occupy over 16% of the study area, mainly in the north-east and west. Urban areas, including major cities like Casablanca, Rabat, and Marrakech, span nearly 3400 km². Moreover, large areas of shrublands and bare lands are evident across the country, while agricultural lands account for almost 20% of the national territory, mainly in the interior plains and north-western Atlantic coast. This study forms a crucial basis for ecological and climatic research in Morocco and serves as a valuable reference for various disciplines such as agriculture, natural resource management, and climate modeling. The mapping of biophysical parameters for each land cover class is a key feature of our research, and these parameters will be instrumental in a subsequent study examining the impact of urban development on surface climate in Morocco. Overall, our study underscores the importance of understanding biophysical parameters in addressing environmental and societal challenges.

GIS based method for mapping actual LULC by combining seasonal LULCs

One of the most significant applications of remote sensing data is to prepare land use and land cover (LULC) maps. LULC maps are always affected by seasonality and a single LULC map of a particular month is prepared to represent a year in most of the research, especially in change detection research. This does not represent the real view of the landscape because the seasonal variation of different LULC types is always overlooked. Considering the issue, the current method aims to solve the problem by incorporating seasonal LULC using the raster overlay method to remove the seasonality effect on LULC classification. To apply this method, a minimum of two seasonal LULC maps is required for a single study year. The map needs to overlay and then reclassify according to the stable and rotational LULC pattern of the study area. This method will replicate the actual LULC pattern of a study area from satellite images. Summary of the method is as follows:•LULC of each season was classified using image classification technique.•LULC of each seasons are coded and combined using overlay technique.•Combined map is reclassified to prepare the actual LULC map.

Land Use and Land Cover Change Modulates Hydrological Flows and Water Supply to Gaborone Dam Catchment, Botswana

Identifying the mechanism through which changes in land use and land cover (LULC) modulate hydrological flows is vital for water resource planning and management. To examine the impact of LULC change on the hydrology of the Gaborone Dam catchment within the upper Limpopo basin, where Notwane river is the major river within the catchment, three LULC maps for the years 1997, 2008, and 2017 were established based on a mosaic of Landsat 5 for 1997 and 2008 and Landsat 8 for 2017. The 10 m-resolution Version 200 ESA World Land Cover Map for 2021 is used as a ground truth to train the random forest (RF) classifier to identify land cover classes from Landsat 8 imageries of 2021 using the Google Earth Engine (GEE) Python API. The overall accuracy/kappa coefficient of the RF classifier is 0.99/0.99 for the training and 0.73/0.68 for the validation data sets, which indicate excellent and substantial agreements with the ground truth, respectively. With this confidence in the LULC classification, the impact of LULC change on the hydrological flow within the catchment was estimated by employing the Soil and Water Assessment Tool (SWAT) and indicator of hydrological alteration (IHA). The SWAT model calibration and validation were first performed, and the ability of the model to capture the observed stream flow was found to be good. The LULC maps from Landsat images during the 1997–2017 period show a decrease in forests and shrubland in contrast to an increase in pasture land. The expansion of pasture and cropland and the reduction in forests and shrubland led to a decline in the amount of evapotranspiration and groundwater recharge. Furthermore, the LULC change also caused a reduction in low flow during dry periods and an increase in high flow during the rainy season. The findings clearly demonstrate that LULC changes can affect the water table by altering soil water recharge capacity. The study highlighted the importance of LULC for catchment water resource management through land use planning to regulate the water level in the Gaborone Dam against the impact of climate change and growing water demands by the city of Gaborone due to population growth.

Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin

Against the backdrop of accelerated global climate change and urbanization, the frequency and severity of flood disasters have been increasing. In recent years, influenced by climate change, the Hai-River Basin (HRB) has experienced multiple large-scale flood disasters. During the widespread extraordinary flood event from July 28th to August 1st, 2023, eight rivers witnessed their largest floods on record. These events caused significant damage and impact on economic and social development. The development of hydrological models with better performance can help researchers understand the impacts of climate change, provide risk information on different disaster events within watersheds, support decision-makers in formulating adaptive measures, urban planning, and improve flood defense mechanisms to address the ever-changing climate environment. This study examines the potential for enhancing streamflow simulation accuracy in the HRB located in Northeast China by combining the physically-based hydrological model with the data-driven model. Three hybrid models, SWAT-D-BiLSTM, SWAT-C-BiLSTM and SWAT-C-BiLSTM with SinoLC-1, were constructed in this study, in which SWAT was used as a transfer function to simulate the base flow and quick flow generation process based on weather data and spatial features, and BiLSTM was used to directly predict the streamflow according to the base flow and quick flow. In the SWAT-C-BiLSTM model, SWAT parameters with P values less than 0.4 in each hydrological station-controlled watershed were calibrated, while the SWAT-D-BiLSTM model did not undergo calibration. Additionally, this study utilizes both 30 m resolution land use and land cover (LULC) map and the first 1 m resolution LULC map SinoLC-1 as input data for the models to explore the impact on streamflow simulation performance. Among five models, the NSE of SWAT-C-BiLSTM with SinoLC-1 reached 0.93 and the R2 reached 0.95 during the calibration period, and both of them stayed at 0.92 even in the validation period, while the NSE and R2 of the other four models were all below 0.90 in the validation period. The potential impact of climate change on streamflow in the HRB was evaluated by using predicted data from five global climate models from CMIP6 as input for the best-performing SWAT-C-BiLSTM with SinoLC-1. The results indicate that climate change exacerbates the uneven distribution of streamflow in the HRB, particularly during the concentrated heavy rainfall months of July and August. It is projected that the monthly streamflow in these two months will increase by 34% and 49% respectively in the middle of this century. Furthermore, it is expected that the annual streamflow will increase by 5.6% to 9.1% during the mid-century and by 6.7% to 9.3% by the end of the century. Both average streamflow and peak streamflow are likely to significantly increase, raising concerns about more frequent urban flooding in the capital economic region within the HRB.

Toward understanding land use land cover changes and their effects on land surface temperature in yam production area, Côte d'Ivoire, Gontougo Region, using remote sensing and machine learning tools (Google Earth Engine)

Land use and land cover (LULC) changes are one of the main factors contributing to ecosystem degradation and global climate change. This study used the Gontougo Region as a study area, which is fast changing in land occupation and most vulnerable to climate change. The machine learning (ML) method through Google Earth Engine (GEE) is a widely used technique for the spatiotemporal evaluation of LULC changes and their effects on land surface temperature (LST). Using Landsat 8 OLI and TIRS images from 2015 to 2022, we analyzed vegetation cover using the Normalized Difference Vegetation Index (NDVI) and computed LST. Their correlation was significant, and the Pearson correlation (r) was negative for each correlation over the year. The correspondence of the NDVI and LST reclassifications has also shown that non-vegetation land corresponds to very high temperatures (34.33°C–45.22°C in 2015 and 34.26°C–45.81°C in 2022) and that high vegetation land corresponds to low temperatures (17.33°C–28.77°C in 2015 and 16.53 29.11°C in 2022). Moreover, using a random forest algorithm (RFA) and Sentinel-2 images for 2015 and 2022, we obtained six LULC classes: bareland and settlement, forest, waterbody, savannah, annual crops, and perennial crops. The overall accuracy (OA) of each LULC map was 93.77% and 96.01%, respectively. Similarly, the kappa was 0.87 in 2015 and 0.92 in 2022. The LULC classes forest and annual crops lost 48.13% and 65.14%, respectively, of their areas for the benefit of perennial crops from 2015 to 2022. The correlation between LULC and LST showed that the forest class registered the low mean temperature (28.69°C in 2015 and 28.46°C in 2022), and the bareland/settlement registered the highest mean temperature (35.18°C in 2015 and 35.41°C in 2022). The results show that high-resolution images can be used for monitoring biophysical parameters in vegetation and surface temperature and showed benefits for evaluating food security.

Dynamics of the wetland ecosystem health in urban and rural settings in high altitude ecoregion

The focus of the present study was to assess the dynamics of wetland ecosystem health in both urban and rural settings situated in the high-altitude Kashmir Himalayan ecoregion. The basic aim was to identify the drivers responsible for wetland degradation in order to sustain ecosystem services effectively. To achieve this, we examined water quality, trophic status, fish species diversity and human disturbances by analyzing changes in land use and land cover (LULC) since 1980. For the limnological characterization of the two wetlands, we evaluated a total of 21 physico-chemical parameters at 24 sites. Two-way analysis of variance revealed significant (p < 0.05) spatial and temporal variability in the water quality parameters. The trophic state index values of 67.7 and 76.7 indicated that the rural and urban wetlands were in eutrophic and hypertrophic status, respectively, signifying potential environmental stress. The data on fish fauna indicated a decline in fish species over the past 40 years, particularly the schizothoracine species. Urban wetlands showed a more significant decrease in species (06) compared to rural wetlands (01). LULC mapping and change analysis employing the visual interpretation technique showed significant transformations in the immediate catchment of wetlands. Substantial growth in the built-up (433.2 % and 2620 %) and decrease in aquatic vegetation (−83.4 % and − 97.5 %) in the immediate catchment was recorded in both the urban and rural wetlands respectively from 1980 to 2020. Our findings demonstrated a relationship between LULC classes and water quality parameters, with an increase in built-up and road areas showing a significant positive correlation with the rise in decadal mean values of total phosphorus, orthophosphorus, nitrate nitrogen, ammonical nitrogen, and calcium content. Based on these observations, we concluded that changes in land use and land cover within the immediate catchment areas of the wetlands were the primary drivers responsible for the deterioration of wetland ecosystem health.

Determination of future land use changes using remote sensing imagery and artificial neural network algorithm: A case study of Davao City, Philippines

Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource extraction, as well as natural phenomena, for example, erosion and climate change. LULC changes significantly impact ecosystem services, biodiversity, and human welfare. In this study, LULC changes in Davao City, Philippines, were simulated, predicted, and projected using a multilayer perception artificial neural network (MLP-ANN) model. The MLP-ANN model was employed to analyze the impact of elevation and proximity to road networks (i.e., exploratory maps) on changes in LULC from 2017 to 2021. The predicted 2021 LULC map shows a high correlation to the actual LULC map of 2021, with a kappa index of 0.91 and a 96.68% accuracy. The MLP-ANN model was applied to project LULC changes in the future (i.e., 2030 and 2050). The results suggest that in 2030, the built-up area and trees are increasing by 4.50% and 2.31%, respectively. Unfortunately, water will decrease by up to 0.34%, and crops is about to decrease by approximately 3.25%. In the year 2050, the built-up area will continue to increase to 6.89%, while water and crops will decrease by 0.53% and 3.32%, respectively. Overall, the results show that anthropogenic activities influence the land's alterations. Moreover, the study illustrates how machine learning models can generate a reliable future scenario of land usage changes.

Grassland Carbon Change in Northern China under Historical and Future Land Use and Land Cover Change

Land use and land cover (LULC) change has greatly altered ecosystem carbon storage and exerted an enormous impact on terrestrial carbon cycling. Characterizing its impact on ecosystem carbon storage is critical to balance regional carbon budgets and make land use decisions. However, due to the availability of LULC data and the strong variability in LULC change, uncertainty remains high in quantifying the effect of LULC change on the historical and future carbon stock. Based on four historical LULC maps and one future LULC projection, this study combined the Land Use and Carbon Scenario Simulator (LUCAS) with a process-based CENTURY model to evaluate the historical and future LULC change and its impact on grassland carbon storage from 1991 to 2050 in northern China. Results showed that grassland experienced a drastic decrease of 16.10 × 103 km2 before 2005, while agriculture and barren land increased by 16.91 × 103 km2 and 3.73 × 103 km2, respectively. After that, grassland was projected to increase, agriculture kept steady, and barren land decreased. LULC change has resulted in enormous total ecosystem carbon loss, mainly in agro-pasture areas; the maximum 8.54% of carbon loss happened in 2000, which was primarily attributed to agriculture to grassland, forest to grassland, grassland to agriculture, and grassland to barren. Before 2000, the grassland net biome productivity was projected to be −15.54 Tg C/yr and −2.69 Tg C/yr with and without LULC change. After 2001, the LULC change showed a positive impact on the grassland carbon balance, and the region was projected to be a carbon sink. Ecological projects have made a significant contribution to grassland carbon storage. The paper provides a framework to account for the effects of LULC change on ecosystem carbon and highlights the importance of improving grassland management in balancing the grassland carbon budget, which is helpful to understand the regional carbon budget and better inform local land use strategies.

Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia

Accurate spatial information on Land use and land cover (LULC) plays a crucial role in city planning. A widely used method of obtaining accurate LULC maps is a classification of the categories, which is one of the challenging problems. Attempts have been made considering spectral (Sp), statistical (St), and index-based (Ind) features in developing LULC maps for city planning. However, no work has been reported to automate LULC performance modeling for their robustness with machine learning (ML) algorithms. In this paper, we design seven schemes and automate the LULC performance modeling with six ML algorithms-Random Forest, Support Vector Machine with Linear kernel, Support Vector Machine with Radial basis function kernel, Artificial Neural Network, Naïve Bayes, and Generalised Linear Model for the city of Melbourne, Australia on Sentinel-2A images. Experimental results show that the Random Forest outperforms remaining ML algorithms in the classification accuracy (0.99) on all schemes. The robustness and statistical analysis of the ML algorithms (for example, Random Forest imparts over 0.99 F1-score for all five categories and p value le 0.05 from Wilcoxon ranked test over accuracy measures) against varying training splits demonstrate the effectiveness of the proposed schemes. Thus, providing a robust measure of LULC maps in city planning.

Mapping Land Use and Land Cover Classes in São Paulo State, Southeast of Brazil, Using Landsat-8 OLI Multispectral Data and the Derived Spectral Indices and Fraction Images

This work aims to develop a new method to map Land Use and Land Cover (LULC) classes in the São Paulo State, Brazil, using Landsat-8 Operational Land Imager (OLI) data. The novelty of the proposed method consists of selecting the images based on the spectral and temporal characteristics of the LULC classes. First, we defined the six classes to be mapped in the year 2020 as forest, forest plantation, water bodies, urban areas, agriculture, and pasture. Second, we visually analyzed their variability spectral characteristics over the year. Then, we pre-processed these images to highlight each LULC class. For the classification, the Random Forest algorithm available on the Google Earth Engine (GEE) platform was utilized individually for each LULC class. Afterward, we integrated the classified maps to create the final LULC map. The results revealed that forest areas are primarily concentrated in the eastern region of São Paulo, predominantly on steeper slopes, accounting for 19% of the study area. On the other hand, pasture and agriculture dominated 73% of all São Paulo’s landscape, reaching 39% and 34%, respectively. The overall accuracy of the classification achieved 89.10%, while producer and user accuracies were greater than 84.20% and 76.62%, respectively. To validate the results, we compared our findings with the MapBiomas Project classification, obtaining an overall accuracy of 85.47%. Therefore, our method demonstrates its potential to minimize classification errors and offers the advantage of facilitating post-classification editing for individual mapped classes.

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

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

Vejetasyon İndeksleri, Ana Bileşenler Analizi ve Google Earth Engine Kullanılarak Tarımsal Alan Sınıflandırması: Söke/Aydın Örneği

Land use and land cover (LULC) classification is known to be one of the most widely used indicators of environmental change and degradation all over the world. There are various algorithms and methods for LULC classification, whereby reliability of the classification maps presents the principal concern. The study focused on evaluation of accuracies of LULC maps produced from original bands of Sentinel-2 imageries together with Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), and Principal Component Analysis (PCA) using Google Earth Engine (GEE) platform to identify best enhancing method for agricultural land classification. Moreover, short-term LULC changes aimed to be identified in the specified area. To achieve the aims, all available imageries acquired in the same month of different years with less than 10% cloud contamination were used to compose averaged images for May 2018 and May 2022 for generating LULC2018 and LULC2022 maps. The area has separated into seven main classes, namely, olive (O), perennial cultivation (P), non-perennial cultivation (NP), forest (F), natural vegetation (N), settled area-bare land (S), and water surface (W) via random forest algorithym. Reliabilities of LULC maps were evaluated through accuracy assessment procedures considering stratified randomized control points. Transitions between each LULC classes were identified.

Dealing with the uncertainty of technical changes in the CORINE Land Cover dataset: The Portuguese approach

Land Use and Land Cover (LULC) datasets are widely used across disciplines, with many users demanding more and better information. Understanding the uncertainties and errors associated to the main LULC datasets is a required step to facilitate their correct use, as well as to identify what could be improved in the future production of these products. CORINE Land Cover is probably the most well-known and used LULC dataset in Europe, especially valuable for the rich time-series that it provides. Despite being produced through a change mapping first approach, which tries to avoid technical errors and uncertainties in the temporal analysis of LULC changes, the Copernicus Land Monitoring Service distributes status layers of CORINE (CSL), which are not valid for change analysis because of their associated errors and uncertainties. The CORINE layers of changes (CHA) remove a lot of these issues, but do not meet the needs of many users. In Portugal, the national authority in charge of producing CORINE, the DGT, has implemented a backdating approach to produce consistent CSL layers that allow change analysis with low levels of uncertainty. Throughout this paper, we evaluate the changes that can be analyzed through all available CORINE layers in Portugal: Copernicus CSL layers; the national DGT CSL layers; and CHA layers. To this end, we aim to assess what type of changes can be studied through each type of layer, their associated sources of uncertainty and the relevance and utility of the Portuguese backdating approach to produce a consistent time-series of LULC maps. The results prove how the Portuguese CORINE layers distributed by the Copernicus Land Monitoring Service contain important sources of uncertainty, which however have been removed through the national backdating methodology. This methodology can be therefore exported for the production of CORINE in other European countries.

Navigating nature’s toll: Assessing the ecological impact of the refugee crisis in Cox’s Bazar, Bangladesh

The Rohingya crisis in Myanmar’s Rakhine state has resulted in a significant influx of refugees into Cox’s Bazar, Bangladesh. However, the ecological impact of this migration has received limited attention in research. This study aimed to address this gap by utilizing remote sensing data and machine learning techniques to model the ecological quality (EQ) of the region before and after the refugee influx. To quantify changes in land use and land cover (LULC), three supervised machine learning classification methods, namely artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), were applied. The most accurate LULC maps obtained from these methods were then used to assess changes in ecosystem service valuation and function resulting from the land use changes. Furthermore, fuzzy logic models were employed to examine the EQ conditions before and after the Rohingya influx. The findings of the study indicate that the increased number of Rohingya refugees has led to a 9.58% decrease in forest area, accompanied by an 8.25% increase in settlement areas. The estimated total ecosystem services value (ESV) in the research area was $67.83 million in 2017 and $67.78 million in 2021, respectively. The ESV for forests experienced a significant decline of 21.97%, equivalent to a decrease of $5.33 million. Additionally, the reduction in forest lands has contributed to a 13.58% decline in raw materials and a 14.57% decline in biodiversity. Furthermore, utilizing a Markovian transition probability model, our analysis reveals that the EQ conditions in the area have deteriorated from “very good” or “good” to “bad” or “very bad” following the Rohingya influx. The findings of this study emphasize the importance of integrating ecological considerations into decision-making processes and developing proactive measures to mitigate the environmental impact of such large-scale migrations.

Assessing Impacts of Land Use and Land Cover (LULC) Change on Stream Flow and Runoff in Rur Basin, Germany

Understanding the impact of land use/land cover (LULC) change on hydrology is the key to sustainable water resource management. In this study, we used the Soil and Water Assessment Tool (SWAT) to evaluate the impact of LULC change on the runoff in the Rur basin, Germany. The SWAT model was calibrated against the observed data of stream flow and runoff at three sites (Stah, Linnich, and Monschau) between 2000 and 2010 and validated between 2011 and 2015. The performance of the hydrological model was assessed by using statistical parameters such as the coefficient of determination (R2), p-value, r-value, and percentage bias (PBAIS). Our analysis reveals that the average R2 values for model calibration and validation were 0.68 and 0.67 (n = 3), respectively. The impacts of three change scenarios on stream runoff were assessed by replacing the partial forest with urban settlements, agricultural land, and grasslands compared to the 2006 LULC map. The SWAT model captured, overall, the spatio-temporal patterns and effects of LULC change on the stream runoffs despite the heterogeneous runoff responses related to the variable impacts of the different LULC. The results show that LULC change from deciduous forest to urban settlements, agricultural land, or grasslands increased the overall basin runoff by 43%, 14%, and 4%, respectively.

Spatial and Temporal Variations of Land Use and Land Cover In Kadiri Watershed Using Rs &amp; Gis Techniques

The change detection and land use and land cover (LULC) maps are more important powerful forces behind numerous ecological systems and fallow land. It is widely known that land use/land cover (LULC) changes significantly alter watershed hydrology and sediment yields. The impact, especially on erosion and sedimentation, is likely to be exacerbated in regions dominated by high rainfall patterns such as monsoons. RS &amp; GIS technologies are very useful to determine the LULC changes. Present research area Kadiri Watershed of Anantapur district of Andhra Pradesh, India, drains an area about 240 km2 including 15 villages. The average annual rainfall in this area varies between 600mm to 700mm in which 60%, 35% and 5% of total rainfall occurs in South-West (S-W),North-East (N-E) and in Summer seasons respectively. The current research focuses on demarcating the spatiotemporal LULC changes. These effects directly affect the ecosystem, land resources, cropping pattern and agriculture. LULC assessment and surveillance are essential for long-term planning and sustainable use of natural resources. The LULC maps were prepared for the study area using Landsat-5, Senthinel 2 images pertaining to pre-monsoon and post-monsoon seasons of 2013, 2017 and 2021. Variations in the extents of agriculture, plantations, scrubs, water bodies, open lands, and built-up areas were noted during these years in the pre-monsoon season, and they ranged from 22 to 35 and 43%, 2 to 3 and 5%, 22 to 28%, 1 to 4%, 52 to 29 and 15%, as well as 1 to 5%, respectively. Similar fluctuations were seen in the post-monsoon season, where they ranged from 29 to 40 and 47%, 3 to 7%, 33 to 30%, 2 to 4%, 32 to 20 and 7%, as well as 1 to 5%. Kadiri watershed, an agriculturally dominant area, has seen an increase in cultivated land due to the conversion of fallow land and open scrub into cropland as a result of good rainfall received during the south-west monsoon during in assessment years and implementation of watershed development activities. These findings highlight the potential impacts of LULC changes in a monsoon-dominated watershed and may contribute to the development of successful LULC-based watershed management strategies for prevention of flooding and sediment loss.