Landsat TM Research Articles

Mapping anthropogenic impacts on natural resources in the northeastern Sahara region: A case study on the past 26 years over the Ziban region (Algeria)

The objective of this research was to map and analyze anthropogenic impacts on natural resources in the Ziban region of Algeria using remote sensing imagery from 1995 to 2021. Medium-resolution satellite images from Landsat TM and OLI were utilized, with image processing techniques such as colorful compositions and supervised classification to map land cover. Seven land use and land cover (LULC) classes were identified, distinguishing human-derived categories (palm, agriculture, urban, and greenhouses) from natural surfaces (nebka, natural vegetation, and bare soil). The results indicated significant increases in human-driven elements like palm, urban areas, agriculture, greenhouses, and nebka, and notable decreases in natural components like vegetation and bare soil. Specifically, the rate of change (Tc) showed increases of 0.9% for palm, 1.14% for urban, 1.55% for agriculture, 1.91% for greenhouses, and 9.37% for nebka, and decreases of -3.68% for vegetation and -11.19% for bare soil. These findings highlight the impact of agricultural policies, population growth, and natural and human-induced conditions on natural resources. Palm areas remained largely unchanged due to state policies, whereas agriculture saw significant conversions to bare soil and palm. Vegetation declined substantially due to adverse climatic conditions and agricultural expansion. Nebka and urban areas had moderate to high conversion rates, and bare soil saw notable changes due to sand movement and urban/agricultural development. The study highlights the role of remote sensing and land cover analysis in managing natural resources sustainably, considering factors like agricultural policies and population growth. It emphasizes developing data-driven strategies for effective land use and management.

Predicting Well Productivity in Cratonic Regions Using Remote Sensing Lineaments and Weathered Cover Thickness: A Case Study from Bobo Dioulasso, Burkina Faso

In western African cratonic regions, fractured crystalline bedrock, Neoproterozoic sedimentary covers, and regolith deposits constitute the most productive aquifer systems. Structural lineaments derived from remote sensing data and weathered cover thickness from borehole interpretations provide cost-effective methods for evaluating well productivity in regions with limited economic resources and hydrogeological knowledge, such as Houet province in Western Burkina Faso. Structural lineaments were interpreted using 1:200,000 Landsat TM images and 1:50,000 aerial photographs, revealing NE and NW as the most significant directions. An analysis of 101 borehole stratigraphic profiles from rural water supply program reports revealed the weathering depth and cover thickness. Borehole productivity exhibited a strong correlation with increased weathered cover thickness. Negative well results were concentrated in areas with less than 5 meters of cover. In contrast, significant differences in specific yield rates were observed with greater thicknesses, ranging from 2.5 m³/d for 20 meters of saturated thickness to 7.6 m³/d when the regolith reached a depth of 40 meters. These preliminary groundwater exploration tools effectively target successful well sites by accounting for differing lithologies, regional tectonics, and regolith development. This approach is particularly relevant for cratonic regions with limited resources and hydrogeological knowledge, aiding in sustainable groundwater development and land-use planning.

THE ROLE AND IMPACT OF VEGETATION ON THE URBAN FABRIC. CASE OF GUELMA CITY

In cities, land use changes caused by various human activities can affect how natural ecosystems function. In this context, it is increasingly important for cities to consider the role of vegetation in preserving a sustainable environment. A diachronic analysis of landscape changes was applied to assess the presence and distribution of vegetation to determine whether it was evolving or regressing. This phenomenon was studied within the current administrative boundaries of the city of Guelma (Algeria). For this purpose, as an approach based on landscape metrics, several Landsat TM and ETM+ remote sensing satellite images were used throughout the period 1987-2019. A set of landscape indices, including NP, AREA_MN, LPI, PLAND, AI and LSI, were calculated to map land cover, the mechanism of land cover and vegetation change, and their impact on the urban ecosystem. The geo-statistical procedure was carried out using a geographic information system Qgis combined by statistical software using Fragstats to calculate various landscape metrics at class level for the analysis of fragmentation . The results of the landscape metric analysis show that the decrease in average area and the increase in the number of green patches are important indicators of land degradation, meaning that the mechanism of landscape degradation and transformation is progressive. This underlines the need to give particular attention to land use and land cover in the region to ensure the sustainable allocation of natural resources.

Landslide Susceptibility Mapping in Nigeria Using Remote Sensing, GIS, and Machine Learning Models

Landslide situations remain a significant concern in Nigeria, as they pose a great risk to human lives, property, and the natural environment, particularly in regions with steep slopes, heavy rainfall, and unfavorable human interference, including deforestation and urbanization. Nigeria’s diverse geographical structure, ranging from urban areas such as Lagos and Abuja to rural regions, underscores the importance of accurate predictions for risk management and avoidance techniques. This research utilized remote sensing and GIS assessment to analyze landslide susceptibility in several regions of Nigeria. Data related to terrain, vegetation, soil moisture, and ground deformation were gathered using high-resolution satellite imagery from sources such as SPOT, ASTER, differential synthetic aperture radar interferometry (D-InSAR), and Landsat TM. GIS data layers included DEM, LULC, soil and geological maps, as well as hydrological maps and data. Methods applied in this study include logistic regression, STAT-R, frequency ratio, and the Random Forest tree-based model. The research produced detailed landslide susceptibility maps for various regions in Nigeria and identified significant factors such as slope, elevation, land use, precipitation, and access to transportation facilities. The Random Forest model demonstrated the most robust predictive capability. The integration of remote sensing with GIS was particularly significant, enhancing the precision of predictions and improving the efficacy of planning and management strategies. By incorporating remote sensing, GIS, and various machine learning algorithms, the researchers have developed a reliable tool for landslide risk prediction and management in Nigeria. Future research should focus on improving data quality and enhancing the generalizability of results to other regions.

Spatial and Temporal Variations of Total Suspended Matter Concentration during the Dry Season in Dongting Lake in the Past 35 Years

Dongting Lake is the second largest freshwater lake in China, located in the middle reaches of the Yangtze River. Since the 21st century, it has faced intensified human activities, particularly the Three Gorges Dam impoundment and sand mining. The water quality of Dongting Lake has significantly changed due to human activities and climate change. Currently, quantitative studies on the spatial–temporal variations of total suspended matter (TSM) during Dongting Lake’s dry season and the human impacts on its concentration are lacking. This study utilizes Landsat-5 TM and Landsat-8 OLI data to estimate the changes in TSM concentration during the dry season from 1986 to 2021, analyzing their spatial–temporal variations and driving mechanisms. By evaluating the atmospheric calibration accuracy and model precision metrics, we select a model based on the ratio of red to green band, achieving an R2 of 0.84, RMSE of 18.94 mg/L, and MRE of 27.32%. Applying this model to the images, we map the distribution of the TSM concentration during the dry season from 1986 to 2021, analyzing its spatial pattern and inter-annual variation, and further investigate the impacts of natural factors and human activities on the TSM concentration. Our results show the following: (1) From 1986 to 2021, the TSM concentration during the dry season ranges from 0 to 200 mg/L of Dongting Lake, with an area-wide average value between 41.61 and 75.44 mg/L. (2) The TSM concentration from 1986 to 2021 is significantly correlated with the water level. Before 2006, it correlates positively, but no significant correlation exists from 2006 onward. (3) From 2006 onward, the mean TSM concentration is notably decreased compared to that before 2006, likely due to the Three Gorges Dam, while our analysis indicates a significant positive correlation between the TSM concentration and sand mining intensity during this period. This study highlights the influence of the Three Gorges Dam and sand mining on the TSM concentration in Dongting Lake during the dry season, providing valuable insights for related research on similar lakes.

Panzara River Watershed Prioritization Based on Geomorphometric and LULC Change Analysis using Geo-Spatial Techniques

Human activities can significantly influence the quality of water flowing from a watershed, either positively or negatively. As water moves through the system, these impacts accumulate, with all land-based activities having the potential to affect the water quality and quantity experienced by downstream stakeholders. Similarly, the actions of upstream landowners impact the water that flows across others' properties. Geospatial techniques like remote sensing and geographic information systems (GIS) are invaluable tools for analysing drainage patterns within a watershed and the associated changes in land use and cover. This study focuses on the Panzara river basin, a principal tributary of the larger Tapi river basin, situated in central India between the westward-flowing Godavari and Narmada river systems, which both ultimately discharge into the Arabian Sea. The study area spans latitudes from 20°42'0" N to 21°18'0" N and longitudes from 74°06'0" E to 75°00'0" E, covering a geographical area of 2,986.05 square kilometers with a perimeter of 570.51 kilometers. The watershed delineation was carried out using Shuttle Radar Terrain Mapper (SRTM) data with a 30-meter resolution. For land use and land cover (LULC) analysis, Landsat 5 TM C2L1 and Landsat 8 OLI/TIRS C2L1 datasets, both with 30-meter resolution, were utilized. The present study conducts a morphometric analysis and assesses LULC changes within the Panzara river basin between 2000 and 2021. Morphometric parameters such as linear parameters [Drainage density (Dd), Stream frequency (Fs), Mean bifurcation ratio (Rbm), Drainage texture ratio (Dt), Length of overland flow (Lo)] and areal parameters [Elongation ratio (Re), Circulatory ratio (Cr), Form factor (Rf), Compactness coefficient (Cc)] were used to prioritize sub-watersheds. Furthermore, the study classifies the observed LULC changes between satellite imagery datasets from 2000 and 2021, quantifying the percentage changes in the respective LULC classes across the sub-watersheds over the two decades. The overall accuracy of the LULC classification was 81.82% for 2000 and 88.88% for 2021, with Kappa coefficients of 0.772 and 0.85, respectively. In terms of prioritizing sub-watersheds, common sub- watersheds such as SW-1, SW-10, and SW-15 were classified under moderate priority, while SW-5, SW-8, and SW-14 were classified under the lowest priority. The results of this study, particularly the prioritization of sub-watersheds, can be instrumental for hydraulic engineers in planning and managing water resources in the Panzara river basin.

Investigation of Surface Urban Heat Island by High-Rise Buildings: Istanbul Levent Region

Although there are studies on urban climate, studies on the effects of skyscrapers in these areas are quite limited. This study aims to determine the impact of skyscrapers on the formation of surface urban heat islands (SUHIs). Accordingly, an area within a radius of one kilometer, centered on the skyscraper region in Levent, Istanbul, was selected as the study area. This area was chosen because it shows three different urban textures. Daytime land surface temperature (LST) maps were created from Landsat TM 5 images from 1990 and 2009, and Landsat 8 (OLI-TIRS) images from 2021. Then, the differences in LST between the three years were calculated to investigate the changes in LST in different urban textures. The results of the study show a low increase in LST in the skyscraper area (2.7 °C) when looking at the 1990-2021 LST difference, while an increase in LST is observed toward the region where construction is low in density (6.9 °C) and the area with unplanned development (10.1 °C). The factors causing the formation of SUHI in different urban textures will be discussed with the findings of this study.

Spatiotemporal Analysis of Jorgo Wato Forest Cover Changes, West Wallagga, Oromia, Ethiopi

A study was conducted in the Jorgo Wato forest, located in the West Wallagga zone and extending to the Buno Bedelle zone of Oromia National Regional State, with the objective of assessing the dynamics of forest cover change in the Jorgo Wato forest over the last three decades (1986–2016). In order to assess forest cover changes, the whole study period was categorized into three periods: 1986–1995, 1995–2006, and 2006–2016. Satellite images from Landsat TM, ETM+, and OLI/TIRS were used in this study. Support Vector Machines for supervised classification and post-classification were used for image classification, with results showing an overall accuracy of up to 99.49%, maximum producer’s accuracies of up to 99.80, and User’s accuracies of up to 100%, while the Kappa coefficient ranged between 96.98% and 99.17%. The result of the change analysis revealed that forests changed from 67.62% in 1986 to 74.77% in 2016, with an average rate of change of 20.13ha/year from 1986 to 2016, whereas farmland changed from 19.60% in 1986 to 12.28% in 2016, with a variation through 30 years and an average rate of change of -20.604ha/year from 1986-2016. Changes in forest cover increment have come from the expansion of coffee plantations and plantations of different tree species by the communities and by Oromia Forest and Wildlife Enterprise till now. Monoculture plantations cannot take the place of a natural forest and cannot support a lot of biodiversity, which is crucial for the management of natural resources. Future maintenance of the region's environmental equilibrium will require a method of combining both systems and concentrating more diverse species.

Effects of disturbances on the spatiotemporal patterns and dynamics of coastal wetland vegetation

The coastal wetlands represented by mangroves are vulnerable to disturbances. To clarify the effects of disturbances quantitatively on mangroves, spatiotemporal changes in land use and land cover (LULC) were compared between the inside and outside of mangroves during 1988 and 2020 in Bangladesh by remote sensing. The mangrove region, which had diverse surface greenness due to natural and anthropogenic disturbances, were surveyed. Changes in greenness were estimated by five vegetation indices (VIs). To increase in the accuracy of land use classification, the five VIs were combined under a random forest (RF) classifier. The five VIs were: normalized difference vegetation index (NDVI), green NDVI (GNDVI), soil-adjusted VI (SAVI), green–red VI (GRVI) and enhanced VI (EVI), using data obtained from Landsat TM (30 m resolution) and Sentinel-2A (10 m) via Google Earth Engine. A Moran’s I analysis showed a random pattern of training samples of LULC classes for integrated RF classification. The classification accuracy was evaluated using the reference point data with multiple comparisons of VIs and κ coefficient. The area was classified into eight LULC classes. Although the respective VIs depicted unique characteristics, the accuracies of them were less than 89 %. The integrated classifier improved the accuracy of 96–97 % with κ of 0.96 in 1988 and 0.97 in 2020. The VI-integrated RF was the most effective classifier for LULC, particularly for separating mangroves from homestead vegetation. The changes for 32 years showed that the dense mangrove occupied 42 % of surveyed area in 1988 but declined ca 622 km2 in 2020, whereas sparse mangrove and aquaculture increased by 487 km2 and 464 km2, respectively, out of the total area of 8,360 km2. The changes in LULC classes were mostly from dense mangroves to sparse mangroves, sparse mangroves to dense mangroves and from cropland to bare land and aquaculture. Inaccessible mangrove degradation should result from natural disturbances, i.e., tropical cyclones, while areas near forest boundaries were distorted by anthropogenic disturbances such as aquaculture development. The restoration of habitat quality, mangrove afforestation and sustainable aquaculture practices were effective indicators for improving the mangrove ecosystems. Since natural and anthropogenic disturbances induce substantial loss of mangrove vegetation, the VI-integrated RF classifier should be applied for monitoring LULC and for developing the sustainable management strategies.

Enhanced Pansharpening Using Curvelet Transform Optimized by Multi Population Based Differential Evolution

In this study, a pansharpening process was conducted to merge the color information of low-resolution RGB images with the details of high-resolution panchromatic images to obtain higher quality images. During this process, weight optimization was performed using the Curvelet Transform method and the Multi Population Based Differential Evolution (MDE) algorithm. The proposed method was tested on Landsat ETM satellite image. For Landsat ETM data, the RGB images have a resolution of 30m, while the panchromatic images have a resolution of 15m. To evaluate the performance of the study, the proposed MDE-optimized Curvelet Transform-based pansharpening method was compared with classical IHS, Brovey, PCA, Gram-Schmidt and Simple Mean methods. The comparison process employed metrics such as RMSE, SAM, COC, RASE, QAVE, SID, and ERGAS. The results indicate that the proposed method outperforms classical methods in terms of both visual quality and numerical accuracy.

Harmonized Landsat and Sentinel-2 Data with Google Earth Engine

Continuous and dense time series of satellite remote sensing data are needed for several land monitoring applications, including vegetation phenology, in-season crop assessments, and improving land use and land cover classification. Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an ‘all-in-one’ Google Earth Engine (GEE) web-based workflow to produce harmonized surface reflectance data from Landsat-7 (L7) ETM+, Landsat-8 (L8) OLI, and Sentinel-2 (S2) MSI top of atmosphere (TOA) reflectance data. Six major processing steps to generate a new source of near-daily Harmonized Landsat and Sentinel (HLS) reflectance observations at 30 m spatial resolution are proposed and described: band adjustment, atmospheric correction, cloud and cloud shadow masking, view and illumination angle adjustment, co-registration, and reprojection and resampling. The HLS is applied to six equivalent spectral bands, resulting in a surface nadir BRDF-adjusted reflectance (NBAR) time series gridded to a common pixel resolution, map projection, and spatial extent. The spectrally corresponding bands and derived Normalized Difference Vegetation Index (NDVI) were compared, and their sensor differences were quantified by regression analyses. Examples of HLS time series are presented for two potential applications: agricultural and forest phenology. The HLS product is also validated against ground measurements of NDVI, achieving very similar temporal trajectories and magnitude of values (R2 = 0.98). The workflow and script presented in this work may be useful for the scientific community aiming at taking advantage of multi-sensor harmonized time series of optical data.

Assessment of groundwater resources from geophysical and remote sensing data in a basement complex environment using fuzzy-topsis algorithm

This study addresses the pressing global water challenge by focusing on a typical basement complex area experiencing acute water shortage. Indiscriminate well siting without reliable hydrogeological maps has resulted in failed attempts to address water shortages. To overcome these challenges, this research aims to enhance the accuracy and reliability of groundwater potential assessments by incorporating crucial groundwater-related factors.The Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method was adopted due to its ability to improve multi-criteria decision-making (MCDM) techniques for effective groundwater resource management. Unlike TOPSIS, FTOPSIS better reflects decision-makers' intentions, especially concerning geological boundaries and natural phenomena. To achieve the objectives of the study, geophysical and remote sensing datasets were utilized. The study employed the electrical resistivity method, utilising the Vertical Electrical Sounding (VES) technique with the Schlumberger array while the remote sensing data used were the Digital Elevation Model (DEM) image and Landsat ETM image which were processed to generate key groundwater conditioning factors. These factors were integrated using the FTOPSIS algorithm. This algorithm facilitated the calculation of the groundwater potential indices by assigning weights based on their level of significance to influencing groundwater potential in order to generate the groundwater potential map (GPM). The GPM was classified into five zones of varying groundwater potential, with very low and low potential occupying 74 % of the total area. Validation with well data yielded an impressive 79 % accuracy, showcasing the model's enhanced precision. Beyond improved accuracy, the study's implications extend to practical applications in groundwater resource management. By providing a clearer understanding of groundwater potentiality, the research can inform more robust decision-making frameworks, promoting sustainable water use not only in the study area but also in similar geological settings of the world.

Evidence of climate change - Investigating glacial terminus and lake inventory using earth observation data for mountainous Bhutan

The mapping and monitoring of different types of Glacial lakes through the Geospatial techniques is vital to show the impact of climate changes on the Glacier and alleviate hazards that result from the bursting of Glacial Lakes and cause catastrophic consequences to human lives. The main goal of the present work was to map and analyze different types of glacial lakes in Bhutan during the years 1990, 2000, and 2017. Several sets of satellite images, Landsat-TM for 1990, Landsat ETM + for 2000, and Landsat 8-OLI satellite image for 2017, were used to estimate the changes in the glacial lakes and the inventory study. Several glacial lakes, i.e., moraine-dammed lake, supra glacial lake, lateral moraine lake, erosional lake, medial moraine lake, and end moraine lake, were mapped within these periods. It was found that there was a rapid increase in glacial lakes from 1990 to 2017. The number of glacial lakes in 1990 was increased from 213 to 436 in 2017. It was also observed that the spatial dimensions of some of the glacial lakes increased. The study revealed five end moraine lakes, 40 lateral moraine lakes, 50 supra glacial lakes, 239 erosional lakes, and 15 other moraines dammed lakes in 2017.

Wetland Distribution Prediction Based on CA–Markov Model under Current Land Use and Protection Policy in Sanjiang Plain

The conflict between grain production and wetland resource protection in plain wetland is prominent. Understanding the future impacts of current land use policies on wetlands is the key to rationally evaluating and adjusting these policies. Therefore, the objective of the research was to predict the potential distribution of Sanjiang plain wetland under the current land use and protection policy using remote sensing images and CA Markov models. Methodologically, Landsat TM remote sensing images of the Sanjiang Plain (2010–2020) were used to extract wetland distribution based on object-oriented methods, and the characteristics and patterns of wetland change caused by the land use and protection policies during this period were analyzed. A CA–Markov model was used to predict the potential distribution of Sanjiang Plain wetland in 2030, 2040, 2050, and 2060. Then, we summarized the advantages and disadvantages of current land use policies and put forward adjustment measures. The results indicate that during 2010 and 2020, the wetland area of Sanjiang Plain decreased by 22.34%. The conversion ratio of wetland to non-wetland type (mainly farmland) in the first half and the second half of the decade was 46.41% and 15.31%, respectively, and the decrease in wetland showed an obvious slowing trend. The spatial distribution prediction in future showed that the wetland area will continue to decline in 2030, and the decline will basically stop in 2040. Finally, the proportion of wetland area will remain at 8.68% of the total area of Sanjiang Plain, with that of some counties and cities less than 5%. It is concluded that, although the current land use policies in Sanjiang Plain can effectively slow down the wetland area shrinking and stabilize the spatial pattern, a very low proportion of wetland area in some areas will make it difficult for the wetland ecosystem to exert ecological functions and ensure regional ecological security. The wetland conservation managers should adjust the current land use policies according to relevant requirements of farmland protection policies and restore the areal proportion and spatial pattern of wetland in order to help with regional sustainable development.

Monitoring Changes in Parts of the Guinea-Savannah Woodlands

Land use and land cover (LULC) in Ghana has undergone a lot of changes over the past years emanating from natural and anthropogenic activities. This study is a comprehensive analysis of LULC changes in parts of the Guinea-Savannah through an integrated approach of geospatial procedures. Multi-temporal satellite imagery data sets of four different years, 1990 (Landsat TM), 2000 (Landsat ETM+), 2010 (Landsat ETM+) and 2020 (Landsat ETM+) were analyzed. Built-up area, Agricultural land, Closed savannah vegetation, Open savannah vegetation and Water bodies were LULC categories delineated for Jirapa municipality. The Cellular Automata-Markov (CA-Markov) model was applied to predict the likely changes in LULC in 2030. The study revealed that the most dominant land cover type in the municipality is the Open savannah vegetation as it occupied averagely 45% of the total surface area. Built-up area increased in area coverage by 93% between 1990 and 2020. Agricultural activities, bushfires, deforestation, infrastructural development, and population growth are the main drivers of changes in Agricultural land, Open savannah vegetation, Closed savannah vegetation and Water bodies. The LULC prediction for 2030 showed that the Built-up areas would increase significantly in 2030 leading to a 6% reduction in Agricultural land in 2030.

Monitoring Changes in Parts of the Guinea-Savannah Woodlands

Land use and land cover (LULC) in Ghana has undergone a lot of changes over the past years emanating from natural and anthropogenic activities. This study is a comprehensive analysis of LULC changes in parts of the Guinea-Savannah through an integrated approach of geospatial procedures. Multi-temporal satellite imagery data sets of four different years, 1990 (Landsat TM), 2000 (Landsat ETM+), 2010 (Landsat ETM+) and 2020 (Landsat ETM+) were analyzed. Built-up area, Agricultural land, Closed savannah vegetation, Open savannah vegetation and Water bodies were LULC categories delineated for Jirapa municipality. The Cellular Automata-Markov (CA-Markov) model was applied to predict the likely changes in LULC in 2030. The study revealed that the most dominant land cover type in the municipality is the Open savannah vegetation as it occupied averagely 45% of the total surface area. Built-up area increased in area coverage by 93% between 1990 and 2020. Agricultural activities, bushfires, deforestation, infrastructural development, and population growth are the main drivers of changes in Agricultural land, Open savannah vegetation, Closed savannah vegetation and Water bodies. The LULC prediction for 2030 showed that the Built-up areas would increase significantly in 2030 leading to a 6% reduction in Agricultural land in 2030.

Urban land use, land cover change and urban microclimate dynamics in Addis Ababa, Ethiopia

Land surface temperature (LST) increases and urban heat island (UHI) variability are the major urban climatology problems arising in urban development. This study attempts to assess the effects of urban land use and land cover change on microclimate dynamics in Addis Ababa city. Three different sets of remotely sensed data from Landsat 5 TM (1990), Landsat 7 ETM+ (2005) and Landsat 8 OLI/TIRS (2021) were used for the study. LSTs were retrieved from Landsat5 TM and Landsat7 ETM+ using a mono window,and the thermal infrared band (TR-10) of Landsat–8 was used to retrieve LST. Regression and correlation analyses of the LST, normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) were performed in SPSS V23. The study also examined the different residential urban morphology types (UMTs) of the LST and NDVI. The selected built-up blocks of UMTs included apartments, villas and mud houses. These UMTs are extracted by digitizing them from the Google Earth explorer. The results from this study showed that the proportion of urban green space (UGS) to other LULC types decreased from 120.4 km2 in 1990 to 76.26 km2 in 2021. However, the built-up area increased at a rate of 216.5 km2 (39.03%) from 1990 to 2021. The rapid expansion of built-up land in the study area was the main factor influencing the increase in LST. The residential UMTs exhibited significant differences in mean LSTs and NDVIs. The results indicate that UMT inhibited by Villia had the highest mean NDVI value and that the highest mean LST was observed in Apartment. The results of multiple linear regression analysis clearly indicate that built-up and green vegetation contributed 92.2% of the LST variations with R2 = 0.92 and VIF ≤ 10 in Addis Ababa city. The results of the study indicate that strengthening public participation in urban greening and optimizing the NDVI and NDBI are important strategies for mitigating the effects of microclimate change and that sustaining urban development and providing better quality of life for the urban population are important.

Integrating remote sensing and field investigation for lithological mapping of Per-Eonile to Neonile sequences west of Sohag city, Egypt: Impact on urban development

In the domains of geological study, natural resource exploitation, geological hazards, sustainable development, and environmental management, lithological mapping holds significant importance. Conventional approaches to lithological mapping sometimes entail considerable effort and difficulties, especially in geographically isolated or inaccessible regions. Incorporating geological surveys and satellite data is a powerful approach that can be effectively employed for lithological mapping. During this process, contemporary RS-enhancing methodologies demonstrate a remarkable proficiency in identifying complex patterns and attributes within the data, hence facilitating the classification of diverse lithological entities. The primary objective of this study is to ascertain the lithological units present in the western section of the Sohag region. This objective will be achieved by integrated Landsat ETM+ satellite imagery and field observations. To achieve our objectives, we employed many methodologies, including the true and false color composition (FCC&TCC), the minimal noise fraction (MNF), principal component analysis (PCA), decoration stretch (DS), and independent component analysis (ICA). Our findings from the field investigation and the data presented offer compelling evidence that the distinct lithological units can be effectively distinguished. A recently introduced geology map has been incorporated within the research area. The sequence of formations depicted in this map is as follows: Thebes, Drunka, Katkut, Abu Retag, Issawia, Armant, Qena, Abbassia, and Dandara. Implementing this integrated technique enhances our comprehension of geological units and their impacts on urban development in the area. Based on the new geologic map of the study area, geologists can improve urban development in the regions by detecting building materials “aggregates”. This underscores the significance and potential of our research in the context of urban development.

The impact of land use and cover changes on river flows in Wundanyi Catchment of Taita Hills, Kenya (1970–2030)

<p>Taita Hills are one of the most important biodiversity hotspots of Kenya but are experiencing a high rate of deforestation due to the to the conversion of its original forestland to agriculture and settlement during the last century. These landscape dynamics, coupled with rainfall fluctuations in these critical ecosystems, may significantly affect water resource distribution and food security in Taita Taveta County and its environs. This study aimed to establish the trends of land use/cover change (LUCC) in the Wundanyi catchment from 1970 to 2030 and predict their specific and combined effects on surface runoff and stream flow in the same period. The analysis was based on statistical trend analysis and dynamic landscape modeling using both historical and primary data from the Wundanyi catchment and Landsat TM and ETM+ imagery of Taita Hills for 1990, 2000, and 2010. Results show highly variable mean seasonal and annual values of discharge in Wundanyi catchment, probably attributed to environmental changes affecting Taita Hills in general and Wundanyi catchment in particular. Compared to 1990, major land use/cover changes in 2010 were featured by the expansion of built-up area (250%), plantation forest (23.7%), broadleaved forest (17.4%), and thicket (15.9%). It was also notable the decrease in woodland (−30.3%), cropland (−21.6%), and shrubland (−0.8%). Dynamic spatial trends by the year 2030 will be evidenced by increased thicket by 0.41% per annum (<em>R</em><sup>2</sup> = 81.6%) and by decreased plantation forests (−0.13%; <em>R</em><sup>2</sup> = 91.3%), woodland (−0.10%; <em>R</em><sup>2</sup> = 77.6%), shrubland (−0.11%; <em>R</em><sup>2</sup> = 85.2%), broadleaved forests (−0.03%; <em>R</em><sup>2</sup> = 56.6%) and cropland (−0.09%; <em>R</em><sup>2</sup> of 84.4). These changes will shape the catchment landscape and influence its hydrology, unless the existing forest and agricultural policy interventions are enforced. Hence, crop diversification, agroforestry, and soil and water conservation structures are recommended to maintain effective control of LUCC on hydrological processes going on in the Wundanyi catchment.</p>

Variation of groundwater level due to land use, precipitation, and earthquake in Yogyakarta City from 2005 to 2020

Yogyakarta City not only experiences rapid land use conversion and precipitation decline but also experiences frequent earthquake events. These factors can have a significant impact on groundwater variation dynamics in this city. Therefore, the aim of this research is to analyze the factors that can influence groundwater oscillation based on precipitation, land use, earthquake, and river discharge changes. The method used to analyze spatio-temporal groundwater level and precipitation are by applying space-time Bayesian statistics since Bayesian has lower root mean square error compared to the space-time variogram and inverse distance methods. Land use is classified with false color composite for Landsat ETM and infrared color composite for Landsat 8 since these color composites well classify built-up and non-built-up areas. River discharge is analyzed as a primary indication of earthquake event impact on groundwater level variation.There is no significant variation of groundwater level from 2005 to 2020. Groundwater fluctuation is classified as stable since the magnitude of change is approximately less than 1.5 m from dry to wet period. It is also probably because there is abundances of groundwater resources in Yogyakarta City that shallow aquifer have very high hydraulic conductivity. The conversion speed from non-built-up area into built-up area is 26% from 2005 to 2015, while 8% from 2015 to 2020. At least 78% of Yogyakarta City is built-up areas in 2020. Abrupt changes in groundwater level is due to: a) declining trends of precipitation on May 2009 to July 2016 with average accumulative monthly residual rainfall ranging from 500 mm month−1 to 1300 mm month−1, and b) high magnitude earthquake occurrences on May 2006, November 2010 and 2015 when groundwater patterns and levels including discharge rivers showed distinct behavior. This study shows that precipitation and earthquake are the major contribution to groundwater level variation compared to land use.