Land Use Map Research Articles

Land-use and its implication on physicochemical parameters of groundwater: Evidence from Ikenne Local Government Area, Ogun State

Groundwater is essentially the major source of fresh water and used widely for drinking purpose. In any given area, groundwater within an aquifer, or groundwater produced by a well, has some vulnerability to contamination from human activities. Therefore, information on human activities that leads to contamination of groundwater is important. Such information can aid in the choice of proper locations for certain activities, so that the adverse effects on groundwater are minimized and protection of groundwater achieved. Against this background, the study examines land-use and its implications on physiochemical properties of groundwater in Ikenne Local Government Area, Ogun State, Nigeria. The study obtains and analyses the existing land-use maps. Water samples were collected for laboratory test to benchmark physicochemical parameters based on World Health Organisation standards. Water Quality Index (WQI) was used to determine groundwater quality. Findings from analyses of land-use map show that residential landuse has highest percentage in Iperu (79.9%) and Ilishan (52.8%) towns, while industrial land-use constitutes the least, representing for 0.6% and 1.9% in Iperu and Ilishan respectively. The study found that WQI for Ilishan I (39.4), II (33.5), III (43.8) and Iperu I (32.9), II (31.4) was of good quality, while Iperu III (19.4) was excellent. ANOVA results of F = 0.596 and p > 0.05 established that there is no statistical significant variation in the physiochemical properties of groundwater. It can be concluded that absence of land-uses that generate contaminants reduce the likelihood of groundwater contamination. Therefore, strategies for effective implementation of zoning regulations should be put in place by relevant government agency.

Mapping land-use and land-cover changes through the integration of satellite and airborne remote sensing data.

An integrated, remotely sensed approach to assess land-use and land-cover change (LULCC) dynamics plays an important role in environmental monitoring, management, and policy development. In this study, we utilized the advantage of land-cover seasonality, canopy height, and spectral characteristics to develop a phenology-based classification model (PCM) for mapping the annual LULCC in our study areas. Monthly analysis of normalized difference vegetation index (NDVI) and near-infrared (NIR) values derived from SPOT images enabled the detection of temporal characteristics of each land type, serving as crucial indices for land type classification. The integration of normalized difference built-up index (NDBI) derived from Landsat images and airborne LiDAR canopy height into the PCM resulted in an overall performance of 0.85, slightly surpassing that of random forest analysis or principal component analysis. The development of PCM can reduce the time and effort required for manual classification and capture annual LULCC changes among five major land types: forests, built-up land, inland water, agriculture land, and grassland/shrubs. The gross change LULCC analysis for the Taoyuan Tableland demonstrated fluctuations in land types over the study period (2013 to 2022). A negative correlation (r = - 0.79) in area changes between grassland/shrubs and agricultural land and a positive correlation (r = 0.47) between irrigation ponds and agricultural land were found. Event-based LULCC analysis for Taipei City demonstrated a balance between urbanization and urban greening, with the number of urbanization events becoming comparable to urban greening events when the spatial extent of LULCC events exceeds 1000 m2. Besides, small-scale urban greening events are frequently discovered and distributed throughout the metropolitan area of Taipei City, emphasizing the localized nature of urban greening events.

Accuracy Assessment of Land Use/Land Cover Indices for Al-Rusafa in Baghdad Governorate by Remote Sensing Technology and GIS

The management and planning of natural and artificial resources depend on accurately monitoring land cover changes. Land cover change mapping and monitoring used to require expensive field surveys. Remote sensing is cheaper and more practical for mapping land use and cover changes. The Tigris River divides the Iraqi capital, Baghdad, into two parts: Karkh and Rusafa. Al-Rusafa was selected as a study area for current research, which has had rapid population and urban growth in recent decades. The current research applies the support vector machine technique to supervised LU/LC maps’ classification into barren regions, water bodies, vegetation cover and built-up regions. Spectral indicators were calculated: Enhanced Vegetation Index, Modified Normalized Difference Water Index, Normalized Built-Up Area Index, Dry Bareness Index in addition to calculating the accuracy assessment and Kappa coefficient. Using the Landsat 9 satellite image, ArcGIS 10.8 and Envi5.3 software were used to analyze and evaluate the results and field points observed by GPS devices. The results showed that the SVM classification algorithm accurately revealed the categories of LU/LC, where the classification accuracy reached 95%, and that the arid lands covered most of the study area 848.864 km2 and water bodies 76.747 km2, the vegetation and the built-up regions 466.459 km2 and 439.077 km2, respectively. The spectral indices showed slightly different areas of barren lands (DBSI 752.589 km2, 93% accuracy), vegetation (EVI 423.651 km2, 96% accuracy), and water bodies (MNDWI 73.187 km2, 98% accuracy) and built-up areas (NBAI 501,731 km2, 90%accuracy). The Support Vector Machine method outperforms other classification methods, and the spectral indicators employed in this work are useful and dependable for extracting each LU/LC category. In conclusion, Landsat 9 satellite data can reliably and swiftly detect ground cover.

Soil erosion management using The Soil and Water Assessment Tool (SWAT) model in Semantok Watershed

The land use characteristics in the Semantok watershed have a significant impact on the reservoir's usable age. According to the land use patterns, including teak woods, rice fields, moorlands, dry fields, and residential areas, the Semantok watershed has the capacity to carry sediment when it rains. One of the models to analyze surface runoff and sediment is the Soil and Water Assessment Tool (SWAT). SWAT was developed to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions over long periods of time. The purpose of this research is to quantify the amount of erosion and sedimentation that results from land use changes in the Semantok watershed in 2015 and 2021 and to determine management scenarios that can reduce the value of erosion and sedimentation that can implemented upstream of the Semantok dam using SWAT. The data used for the research are climatology data, DEM map, landuse map, and river data. The amount of erosion at the Semantok watershed outlet increased from 7.00 mm/year in 2015 to 14.18 mm/year in 2021. Based on Forestry Ministerial Regulation No. P.61/Menhut-II/2014, the Semantok watershed's carrying capacity is 3.95 falling into the pretty bad category (3.5 < DDD < 4.3). Three scenarios were compared in order to carry out control efforts: revegetation, check dams, and combining revegetation and check dams. The most efficient way to reduce erosion by up to 30.51% is to combine revegetation and check dam.

Changes in water surface area of the Middle Atlas-Morocco lakes: A response to climate and human effects

The lakes represent crucial surface water resources and an integral part of wetlands. The most concerning aspect of the degradation of these areas is the complete drying up of the lakes. In the Mediterranean region, successive changes in land use practices in the context of climate change have strongly influenced wetland areas. In this study, we used Landsat TM, OLI, and OLI-2 satellite images to monitor the water surface area in two representative lakes (Aoua and Ifrah) of the Tabular Middle Atlas and to map land use across the entire study area. To extract information related to lakes and land use, we employed the Support Vector Machine machine learning algorithm, widely used in remote sensing studies. However, we identified drought periods from precipitation data using the Standardized Precipitation Index (SPI) recommended by the World Meteorological Organization (WMO). The results obtained from the processing of Landsat satellite images indicate a significant reduction in the surface area of the lakes, with periods of drying for Aoua lake, endangering their fragile ecosystems and biodiversity. The critical situation of the two lakes is attributed to a combination of natural and anthropogenic factors. The analysis of climatic data shows a significant climate change from the 1980s, with long periods of drought. In parallel, the study area has undergone remarkable modifications in land use patterns, mainly characterized by a significant extension of irrigated agricultural surfaces to the detriment of grazing and rainfed lands. In three decades, the area of irrigated crops has increased from approximately 1300 hectares in 1985 to 7070 hectares in 2022, representing an increase of 542%. The findings presented in this study reveal the extent of lake degradation in the TMA and reflect the alarming decline in groundwater levels. This situation indicates the necessity of formulating a strategy to protect water resources and wetlands in the Middle Atlas.

Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery

Land Use and Land Cover (LULC) maps are vital prerequisites for weather prediction models. This study proposes a framework to generate LULC maps based on the U.S. Geological Survey (USGS) 24-category scheme using Google Earth Engine. To realize a precise LULC map, a fusion of pixel-based and object-based classification strategies was implemented using various machine learning techniques across different seasons. For this purpose, feature importance analysis was conducted on the top classifiers considering the dynamic (seasonal) behavior of LULC. The results showed that ensemble approaches such as Random Forest and Gradient Tree Boosting outperformed other algorithms. The results also demonstrated that the object-based approach had better performance due to the consideration of contextual features. Finally, the proposed fusion framework produced a LULC map with higher accuracy (overall accuracy = 94.92% and kappa coefficient = 94.19%). Furthermore, the performance of the generated LULC map was assessed by applying it to the Weather Research and Forecasting (WRF) model for downscaling wind speed and 2-m air temperature (T2). The assessment indicated that the generated LULC map effectively reflected real-world conditions, thereby impacting the estimation of wind speed and T2 fields by WRF. Statistical assessments demonstrated enhancements in RMSE by 0.02 °C, MAE by 1 °C, and Bias by 0.03 °C for T2. Additionally, there was an improvement of 0.06 m/s in MAE for wind speed. Consequently, the framework can be implemented to produce accurate and up-to-date high-resolution LULC maps in various geographical areas worldwide. The source codes corresponding to this research paper are available on GitHub via https://github.com/Mganjirad/GEE-LULC-WRF.

Estudo de suscetibilidade a movimentos gravitacionais de massa e inundações na região urbana de Patos de Minas, Minas Gerais

Every year Brazil registers thousands of accidents considered natural disasters, where the State of Minas Gerais contributes yearly, mainly accidents linked to the intense rains. Thus, this study aims to indicate areas that have the propensity to develop natural physical processes linked to mass movement and flooding in the urban perimeter of the city of Patos de Minas. From maps of land use and occupation, soil classes, hypsometry, average annual precipitation, slope, road mesh and NDVI, map algebra and analytical hierarchy process was applied. As the result, the susceptibility classification of the area was obtained. It was concluded that the urban perimeter has medium to high susceptibility for mass movements and flooding, preventive measures should be taken to avoid major accidents.

Mapping the diversity of land uses following deforestation across Africa

African forest are increasingly in decline as a result of land-use conversion due to human activities. However, a consistent and detailed characterization and mapping of land-use change that results in forest loss is not available at the spatial-temporal resolution and thematic levels suitable for decision-making at the local and regional scales; so far they have only been provided on coarser scales and restricted to humid forests. Here we present the first high-resolution (5 m) and continental-scale mapping of land use following deforestation in Africa, which covers an estimated 13.85% of the global forest area, including humid and dry forests. We use reference data for 15 different land-use types from 30 countries and implement an active learning framework to train a deep learning model for predicting land-use following deforestation with an F1-score of 84±0.7\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$84\\pm 0.7$$\\end{document} for the whole of Africa. Our results show that the causes of forest loss vary by region. In general, small-scale cropland is the dominant driver of forest loss in Africa, with hotspots in Madagascar and DRC. In addition, commodity crops such as cacao, oil palm, and rubber are the dominant drivers of forest loss in the humid forests of western and central Africa, forming an “arc of commodity crops” in that region. At the same time, the hotspots for cashew are found to increasingly dominate in the dry forests of both western and south-eastern Africa, while larger hotspots for large-scale croplands were found in Nigeria and Zambia. The increased expansion of cacao, cashew, oil palm, rubber, and large-scale croplands observed in humid and dry forests of western and south-eastern Africa suggests they are vulnerable to future land-use changes by commodity crops, thus creating challenges for achieving the zero deforestation supply chains, support REDD+ initiatives, and towards sustainable development goals.

Hydroclimatic projection: statistical learning and downscaling model for rainfall and runoff forecasting

Abstract The study is carried out to investigate the surface runoff depth with changing precipitation due to climate change in the study area where sandy loam and loamy soil are dominant. In this study, future rainfall is projected by a statistical downscaling model (SDSM) using a set of predictors derived from a Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) [the Norwegian Earth System Model (NorESM)] with updated scenarios SSP 4.5 and SSP 8.5. Daily rainfall values for the observed period (1981 to 2014) are validated using statistical learning and evaluated with matrices, namely, root mean square error (RMSE), coefficient of correlation, and Nash–Sutcliffe efficiency (NSE), which are found to be valid for further predictions. Rainfall projections show a decrease in rainfall trend of 50% from 2030 to 2040 for scenario SSP 4.5 and an increase of 7% from 2040 to 2050. Predicted rainfall for scenario SSP 8.5 shows a similar trend of decreasing rainfall of 24% for the period 2030–2040 and an increase of 19% in the period 2040–2050. Furthermore, these rainfall values are spatially modelled in a geographic information system (GIS) and rainfall maps are obtained. The obtained rainfall map, land-use map, and soil map are overlaid to compute curve numbers and runoff depths. A similar trend of decrease in runoff is observed for the period 2030–2050. The overall trend of climate change shows a water-stressed scenario.

Predictive map of soil texture classes using decision tree model and neural network with features of geomorphology level

This study aims to compare decision tree (DT) and artificial neural network (ANN) models, in addition, the efficiency of geomorphic surface attributes in predicting soil texture classes. The study area is located in the north of Chaharmahal and Bakhtiari province, central west Iran, and covers 6875 ha. Ninety-six pedons were excavated on separated geoforms. Soil samples of top soil (A horizon) were analyzed for clay, sand, and silt contents. Totally 57 auxiliary variables, including the derivatives of digital elevation model (DEM), Landsat 8 images, geomorphic surface map, geology map, and land-use map, were used to predict both soil texture classes and soil particle size fractions. Root-mean-square error (RMSE), R² or the coefficient of determination ( R_square), overall accuracy, and Kappa coefficient were selected as criteria for evaluating model performance. The R-square coefficients of clay, silt, and sand fractions for both models, respectively, were 0.41, 0.25, and 0.63 for ANN and 0.52, 0.62, and 0.75 for DT. According to RMSE, R-square, overall accuracy, and Kapa coefficient of validation data, the DT model produced better prediction fits to the both soil particle-size fraction and soil texture classes and was the most accurate classifier model. The parameters were 0.59, 0.09, 0.66, and 0.24 for ANN and 0.41, 0.75, 0.76, and 0.60 for DT models, respectively. The accuracy of each individual soil texture class was generally dependent upon the number of soil texture observations in each texture class. According to this fact, both models had better prediction for silty clay loam and clay loam texture classes.

Forest management and former land use have no effect on soil fungal diversity in uneven-aged mountain high forests

Key messageMetabarcoding analysis of soil fungal communities in French mountain forests revealed that harvesting intensity, time since last harvest and former land use had no effect on fungal community composition compared to key abiotic factors. Low-intensity management in these uneven-aged mountain forests therefore has limited effects on soil fungal community composition which is mainly driven by elevation and edaphic properties.ContextPast and current human activities are known to affect forest biodiversity. However, the effects of former land use and forest management have been studied much more extensively on higher plants than on fungi.AimsOur objectives were to assess the effects of harvesting intensity, duration since last harvest and former land use on soil fungal communities in uneven-aged mountain high forests.MethodsOn the basis of historical land-use maps drawn between 1862 and 1864 and on historical forest management archives, we selected 62 sites in the French Alps with contrasting land-use histories (ancient forests, which were already forested on historical maps vs recent forests, which have recovered following abandonment of pastures) and different durations since last harvest (from 1 to over 50 years). We carried out soil sampling and assessed fungal diversity by metabarcoding analysis. We analysed soil fungal molecular operational taxonomic units (MOTU) diversity as a whole and for the main lifestyle groups (such as wood saprotrophic or ectomycorrhizal fungi) using multiple linear regressions on Shannon’s diversity index and fungal taxonomic composition using canonical correlation analysis.ResultsWe found no significant effect of harvesting intensity, time since last harvest or land-use history on total fungal MOTU diversity, fungal lifestyle diversity or taxonomic composition. In contrast, we observed significant effects of elevation, pH, organic carbon and available phosphorus content on the taxonomic and functional composition of soil fungal communities.ConclusionsThe structure of soil fungal communities (i.e. diversity and species composition) was mainly determined by elevation and edaphic factors, indicating a high-context dependency, as previously found in similar studies. Our study in mountain forests shows that recent forests established on former pastures had no legacy effect on soil conditions and fungal communities, in contrast to previous results in lowland areas, where recent forests were mainly established on former cropland. Uneven-aged forest management had no effect on fungal diversity, in contrast to previous results observed in even-aged high forests.

Land-use classification based on high-resolution remote sensing imagery and deep learning models.

High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the performance of these models. In this study, we applied four well-established and robust deep learning models (FCN-8s, SegNet, U-Net, and Swin-UNet) to an open benchmark high-resolution remote sensing dataset to compare their performance in land-use mapping. The results indicate that FCN-8s, SegNet, U-Net, and Swin-UNet achieved overall accuracies of 80.73%, 89.86%, 91.90%, and 96.01%, respectively, on the test set. Furthermore, we assessed the generalization ability of these models using two measures: intersection of union and F1 score, which highlight Swin-UNet's superior robustness compared to the other three models. In summary, our study provides a systematic analysis of the classification differences among these four deep learning models through experiments. It serves as a valuable reference for selecting models in future research, particularly in scenarios such as land-use mapping, urban functional area recognition, and natural resource management.

Incorporating landscape ecological approach in machine learning classification for agricultural land-use mapping based on a single date imagery

Land-use maps containing crop rotational information are very important in land management and physical planning. Such maps are usually generated using multitemporal data. Although recent technology allows analysts to process multitemporal information effectively, the use of single date imagery for such purpose is more efficient. This study aimed to map detailed agricultural land-use with crop rotational information based on a single date Landsat 8 imagery and SRTM-derived terrain attributes. A landscape ecological approach assuming the influence of terrain characteristics on the existence of crop and land-use types was implemented in multisource classification using random decision forest (RDF) machine learning algorithm. The use of seven optical bands and five terrain attributes could provide a land-use map at 88.03% accuracy, compared to seven optical bands only that generate 82.45% accuracy. These results are also better than those of maximum likelihood. The most influential variables in the achieved accuracy are elevation and thermal band.

Mapping of Land Use and Land Cover (LULC) Using EuroSAT and Transfer Learning

Mapping of Land Use and Land Cover (LULC) Using EuroSAT and Transfer Learning

A regional-level spatiotemporal perspective of land use and land cover change impact on forest aboveground biomass in three gorges reservoir region, China

This study used multi-platform remote sensing datasets to assess the impacts of land use and land cover (LULC) change on forest biomass density across the Three Gorges Reservoir (TGR) region along the Yangtze River. This study maps land use and land cover (LULC) change and forest aboveground biomass estimation (AGB) using Sentinel-2A and GEDI-L4B data based on observations between 2019 and 2022. The data analysis results of monitoring revealed a strong association frequency between land-use change and forest land cover since the significant relationship with forest aboveground biomass density has also been explored. The total deforestation decreased significantly in 2022. The accuracy assessment results show a classification accuracy of between 90.4% and 94.1%, with a kappa coefficient of 82.4%–92.1%. For the TGR region forest, the minimum to maximum mean above-ground biomass values are from 15.81 to 373.66 Mgha-1, respectively. Moreover, the fitting performance of the statistical analysis of aboveground biomass density of forest mean for the forest was quite decent with training (R 2: 0.92, RMSE: 12.30 Mg/ha) and testing (R 2: 0.79, RMSE: 18.07 Mg/ha) datasets. The results highlight LULC change impacts, particularly forest land class over forest AGB stands, and the effectiveness of the GEDI-L4B product in regional biomass estimation in the TGR region.

Landuse and Landcover Mapping to Assess Forest Ecosystem Change in Ihiala Lga, Southeastern Nigeria

Landuse and Landcover Mapping to Assess Forest Ecosystem Change in Ihiala Lga, Southeastern Nigeria

A Dual Phase Approach for Addressing Class Imbalance in Land-Use and Land-Cover Mapping From Remotely Sensed Images

A Dual Phase Approach for Addressing Class Imbalance in Land-Use and Land-Cover Mapping From Remotely Sensed Images

Landslide susceptibility mapping of the Main Boundary Thrust region in Mandre - Khursanibari section of Arghakhanchi and Palpa Districts, Lumbini Province, Nepal

The Mandre-Khursanibari section of Arghakhanchi and Palpa districts consists of numerous landslides, small to large. Prospecting weak geological sections like the location of the Main Boundary Thrust (MBT) and inherently weak lithological settings in addition to other factors are the main objectives of the present research. In this study, the results of the landslide susceptibility analysis using frequency ratios (FR) are assessed. The Mandre-Khursanibari segment was subjected to landslide detection using imagery (Google Earth) with a spatial resolution of 50 cm. The landslide inventory was used to construct training and testing data. Based on topographic, geological, and land-use maps, nine contributing variables were identified. The FR ratings were established using the contributing elements and training data. The map illustrating landslide risk was produced by integrating the influencing components that determined FR ratings. The ROC-AUC curve shows a validation rate of 82.2 % in the present model of LSI. The three most important elements in the occurrence of the landslide, out of the nine potential causes, are distance from the thrust (MBT), land use, and distance from the road. However, the role of adverse lithological setting is found to be a minor in many sections of this region. The MBT is one of the main causes of landslides in this area.

Mapping Pinus spp. Forestry and Land Cover Classes Using High-resolution PlanetScope Satellite Data: Experimenting Images from Different Seasons and Machine Learning Methods

The Remote Sensing and machine learning techniques are cost-effective ways of mapping land use and cover, especially forestry areas. This is essential for the management and planning of such resources. The purpose of this study was to identify which classifier (Random Forest or Support Vector Machine) reach the best accuracy in land use and cover classification and determine which is the best season of year for Pinus spp. forest mapping. PlanetScope multispectral image was used with 3.7 m of spatial resolution, collected over the coastal region of Rio Grande do Sul state. The input variables for the classifiers were the four spectral bands: RGB and NIR, and the NDVI vegetation index. In both classifiers, high accuracy values were obtained, as well as for all seasons of the year. The Random Forest classifier obtained better results in the spring and summer seasons, while in the autumn and winter seasons there was no significant difference between the classifiers for the classification of Pinus spp. forests. The results reached an adequate precision to be used for the management and monitoring of the land use and cover in the municipality of São José do Norte, RS.

Land-Use Land-Cover Change Detection Using Geospatial Techniques in Zalingei, Central Darfur, Sudan

Using remote sensing for land use and land cover (LULC) is essential for systems that help people make decisions because it gives valuable information about space and time. A study was conducted in Zalingei, Sudan, to analyze the changes in LULC over 30 years from 1991 to 2021 using multi-temporal Landsat images. Thematic Mapper (TM) and Operational Land Imager (OLI) were classified using the supervised classification method. The pictures were divided into four groups based on how the land was used: residential areas, bodies of water, vegetation cover, and bare land. Results showed that the residential area increased by 20.74% while the water body increased by 2.32%. However, the vegetation cover decreased by 0.7%, and bare land decreased by 22.37%. The changes were caused by people, which shows how vital good land management practices and involvement from the local community are for reducing LULC change. So, to reduce LULC change in the study area, proper land management practices and active participation from the local community are needed. The study concluded that remote sensing technology is an effective tool for assessing and mapping land use and land cover changes and providing valuable information for decision support systems.