Thematic Mapper Research Articles

Assessment of stream order and drainage pattern of flood prone areas of Ebonyi state, Southeastern Nigeria

This study evaluates stream order and drainage pattern of flood in Ebonyi state, Southeastern Nigeria. The drainage network within Ebonyi State was identified using the Shuttle Rader Topographic Mission (SRTM), the stream order and flood drainage pattern with the aid of the Digital Elevation Model (DEM), and the Topographic analysis of Spatial Analyst on Hydrology. The data was acquired using Geometric Positioning and the LANDSAT downloaded from the United States Geological Survey (USGS) Earth Explorer 2019 and Thematic Mapper (TM) 2009. The result shows that the map of the eastern part of Ebonyi State houses the Cross River basin as a main consequent river draining to the Atlantic Ocean. The drainage gets darker down the slope as more volumes of water move into the traversing drainage channels in their corresponding orders. As flood magnitude increases, the velocity decreases because more water from different river orders or tributaries has been added to the adjusting river channels. It proves that consequent rivers like the Enyim River of Izzi, Iyiokwu River of Abakaliki, Alcor River of Ikwo, Iyere River of Afikpo South, and Esu Umuchimaewuze River of Ohaozara Local Government areas slope down through the adjourning tributaries when the basin is saturated. The map legend shows a thick blue outcome indicating that the flood originates from the Enyim River of Izzi, Iyiokwu River of Abakaliki, Alchor River of Ikwo, Iyere River of Afikpo South, and Esu Umuchimaewueze River of Ohaozara. Therefore, urgent adjustments and construction of drainage systems are needed to accommodate more water volumes.

The Role of Man on Land Cover Dynamics in the Jakiri Area, N.W. Region of Cameroon

The Jakiri Plateau is an epitome of sub-Saharan agglomerations where man has had an untold impact on the land cover dynamics. This research thus stood to decipher and view in detail the role of man in determining landcover change. The study relies on the collection of data from secondary and primary sources. Questionnaires were administered to 310 Farmers and 84 Pastoralists in 17 villages. Information from Focus Group Discussions and Interviews was beefed up with facts based on measurements and Observations from the Field. The Topographic Map of Nkambe NB32XVII, at the scale: 1:200 000, was exploited. Data from satellite images were derived using the Multi-Spectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper plus (ETM +). ASTER images for 2007 were used. Data were treated with the use of SPSS, ArcGIS and ERDAS IMAGINE software. The study reveals that anthropogenic actions are predominant in degradation; meanwhile, physical factors only contribute to the degradation process that rendered the Jakiri plateau susceptible to soil erosion and mass movement. The Normalized Different Vegetation Index (NDVI) revealed a decrease in NDVI values from +0.92 in 1988, +0.74 in 2001 and +0.5 in 2020, showing a vegetation decrease. Meanwhile, the Land Cover Classification revealed a change in forest/land cover, from 24.4% in 1988 to 9.83% in 2020, with a decrease in forest cover of 14.5%. On the contrary, built-up areas, grassland (savannah) and bare surfaces have witnessed an increase in land cover classes from 34.4%, 10%, and 31.2% in 1988 to 37.7%, 14%, and 38% in 2020, respectively. Our study equally identified population pressure, deforestation, rudimentary agricultural activities, overgrazing and forest exploitation Practices that transformed the landscape of Jakiri. The solutions that have been proposed focus on landscape restoration through improved farming systems such as agroforestry and also the application of certain technical methods. The proposed solutions are those that will take care of the needs of the present generation without jeopardizing that of the future generation.

Analyzing Land Use/Land Cover Changes Using Google Earth Engine and Random Forest Algorithm and Their Implications to the Management of Land Degradation in the Upper Tekeze Basin, Ethiopia.

Land use and land cover change (LULCC) without appropriate management practices has been identified as a major factor contributing to land degradation, with significant impacts on ecosystem services and climate change and hence on human livelihoods. Therefore, up-to-date and accurate LULCC data and maps at different spatial scales are significant for regular monitoring of existing ecosystems, proper planning of natural resource management, and promotion of sustainable regional development. This study investigates the temporal and spatial dynamics of land use land cover (LULC) changes over 31 years (1990-2021) in the upper Tekeze River basin, Ethiopia, utilizing advanced remote sensing techniques such as Google Earth Engine (GEE) and the Random Forest (RF) algorithm. Landsat surface reflectance images from Landsat Thematic Mapper (TM) (1990, 2000, and 2010) and Landsat 8 Operational land imager (OLI) sensors (2021) were used. Besides, auxiliary data were utilized to improve the classification of LULC classes. LULC was classified using the Random Forest (RF) classification algorithm in the Google Earth Engine (GEE). The OpenLand R package was used to map the LULC transition and intensity of changes across the study period. Despite the complexity of the topographic and climatic features of the study area, the RF algorithm achieved high accuracy with 0.83 and 0.75 overall accuracy and Kappa values, respectively. The LULC change results from 1990 to 2021 showed that forest, bushland, shrubland, and bareland decreased by 12.2, 24.8, 1.2, and 15.4%, respectively. Bareland has changed to farmland, settlement, and dry riverbed and stream channels. Expansion of dry stream channels and sandy land surfaces has been observed from 1990 to 2021. Bushland has shown an increment by 17.2% from 1900 to 2010 but decreased by 19.5% from 2010 to 2021. Throughout the study period, water, farmland, dry stream channels and riverbeds, and urban settlements showed positive net gains of 484, 8.7, 82, and 26778.5%, respectively. However, forest, bush, shrub, and bareland experienced 12.17, 24.8, 1.2, and 15.37% losses. The observed changes showed the existing land degradation and the future vulnerability of the basin which would serve as an evidence to mitigate land degradation by avoiding the future conversion of forest, bushland, and shrubland to farmland, on the one hand, and by scaling up sustainable farmland management, and afforestation practices on degraded and vulnerable areas, on the other hand.

Waste Water Management in Wet Coffee Processing Mills and their Impact on the Water quality status of Gidabo River and its Tributaries, Southern Ethiopia.

The Gidabo River and its tributaries are the main sources of water for more than 1,584,646 inhabitants. It is an important source of water for the surrounding rural communities for various uses such as domestic, irrigation, livestock watering, fishing, and recreation. The river is the main tributary of Lake Abaya. The present study was designed to investigate the water quality status of the Gidabo River and its tributaries for domestic and aquatic life. To assess the water quality status, water samples were collected in monthly intervals for a period of 3 months from September to November (coffee processing time), 2022. Arc GIS 9.3, 3 DEM, and spreadsheet were used to analyze the data collected from SRTM (Shuttle Radar Thematic Mapper, 90 m) and field observation. Of all the water quality parameters analyzed; turbidity, BOD5, DO, COD, pH, Ni, Fe, NO3 -, and PO4 3- were higher than the recommended limits of national and international standards for aquatic life. Based on the Weighted Arithmetic Mean (WAM), Water Quality Index (WQI) calculations of the River, WQI value of the river ranges between 34.83 and 54.31 in different reaches of the watershed which is classified under bad category. The wet coffee processing industry which is the main sources of contamination in the watershed uses 63 L of processing water to produce 1 kg of green coffee beans. Traditional lagoons, with an average hydraulic retention time (HRT) of 1.99 days, are the most common methods of treating wastewater. The river is at higher risk from harmful anthropogenic activities in the watershed and requires urgent monitoring and mitigation to prevent further degradation.

Impact of Changes in Land use and land cover on Agricultural Productivity in Northern Nigeria

The challenges posed by changes in land use and land cover are greatly affecting agricultural productivity in the semi-arid region of Nigeria. Land use and land cover change affects ecosystem functions and services, which leads to people migrating from one place to another, predominantly rural dwellers. The research studies the impact of land use and land cover change on agricultural productivity and the factors influencing the changes. To achieve this, a remote sensing technique was employed. The Landsat Thematic Mapper Image of 1990, Landsat Enhanced Thematic Mapper Image of 2005 and Landsat Enhanced Thematic Mapper Image of 2020 were collected and analysed. Six classes of land use and land cover were obtained using Earth Resource Development Assessment System Imagery 9.1 software. The result shows that there was a decrease in the land area occupied by wetland, shrubland and water body, while there was an increase in the land area occupied by bare lands/dunes, settlement and scattered cultivation from the year 1990 to 2020. Increase in bare land/dune, scattered cultivation and settlement were mainly as a result of climate change resulting from overgrazing, a southward movement of dunes from the Sahara desert, increasing farming activities and increasing demand for shelter by the increasing population. The decrease in shrubland, water body, and wetland were also linked to climate change resulting from increasing deforestation, and increasing use of wetland for farming activities. Based on the results of the analysis, it is recommended that measures should be taken to integrate sustainable land management and climate change adaptation options for sustainable Agriculture.

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.

Remote sensing and GIS-based study of land use/cover dynamics, driving factors, and implications in southern Ethiopia, with special reference to the Legabora watershed

This paper investigates the trends, drivers, and consequences of LULC changes in Legabora watershed, Ethiopia, by utilizing remote sensing and geographic systems. Landsat Maltispectiral scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images of years 1976, 1991, 2001, and 2022, respectively, were used to study the dynamics of LULC. Essential image pre-processing steps were carefully carried out to correct distortions caused by sensor limitations. Eight main LULC categories were identified based on supervised image categorization methods and the maximum likelihood classification algorithm.The findings of change detection and cross-tabulation matrix demonstrate that there has been a significant increase in the area of cropland 345.1 ha/year, settlement 5.9 ha/year, forest 38.2 ha/year, and degraded lands 2.56 ha/year, respectively, over the period between 1976 and 2022. In contrast, considerable decreases were observed in grasslands (−248 ha/year) and shrublands (−144 ha/year), whereas other LULC categories augmented. The results revealed that the overall accuracy rates stood at 88.3 %, 88.4 %, and 85.6 % for 1976, 1991, and 2022, respectively. The overall kappa coefficient demonstrated values of 0.86 %, 0.86 %, and 0.83 % for the same period. Surveyed respondents perceived population growth, settlement, agricultural expansion, and infrastructure development as the most noticeable drivers of these LULC changes. In contrast, deforestation, land degradation, lack of livestock fodder, and biodiversity loss were identified as the main consequences of LULC changes. The factors and implications addressed in this study may be helpful tool for the formulation and implementation of evidence-based land use policies and strategies within in the study area and elsewhere.

Estimation of water quality variables based on machine learning model and cluster analysis-based empirical model using multi-source remote sensing data in inland reservoirs, South China

Reservoirs play important roles in the drinking water supply for urban residents, agricultural water provision, and the maintenance of ecosystem health. Satellite optical remote sensing of water quality variables in medium and micro-sized inland waters under oligotrophic and mesotrophic status is challenging in terms of the spatio-temporal resolution, weather conditions and frequent nutrient status changes in reservoirs, etc., especially when quantifying non-optically active components (non-OACs). This study was based on the surface reflectance products of unmanned aerial vehicle (UAV) multispectral images, Sentinel-2B Multispectral instrument (MSI) images and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) by utilizing fuzzy C-means (FCM) clustering algorithm was combined with band combination (BC) model to construct the FCM-BC empirical model, and used mixed density network (MDN), extreme gradient boosting (XGBoost), deep neural network (DNN) and support vector regression (SVR) machine learning (ML) models to invert 12 kinds of optically active components (OACs) and non-OACs. Compared with the unclustered BC (UC) model, the mean coefficient of determination (MR) of the FCM-BC models was improved by at least 46.9%. MDN model showed best accuracy (R2 in the range of 0.60–0.98) and stability (R2 decreased by up to 13.2%). The accuracy of UAV was relatively higher in both empirical methods and machine learning methods. Additionally, the spatio-temporal distribution maps of four water quality variables were mapped based on the MDN model and UAV images, all platforms showed good consistency. An inversion strategy of water quality variables in various monitoring frequencies and weather conditions were proposed finally. The purpose of introducing the UAV platform was to cooperate with the satellite to improve the monitoring response ability of OACs and non-OACs in small and micro-sized oligotrophic and mesotrophic water bodies.

GIS-based flash flooding susceptibility analysis and water management in arid mountain ranges: Safaga Region, Red Sea Mountains, Egypt

The Safaga Region (SR) is part of the Red Sea mountain range in Egypt. Catastrophic flash flooding is now an inescapable event, wreaking havoc and causing massive loss of life and property. The majority of the floodwater, however, has been wasted as runoff to the Red Sea, which, if used wisely, could meet a fraction of the water demands for a variety of applications in this area. The current work aims to use GIS techniques to integrate remote sensing data for evaluating, mitigating, and managing flash floods in SR. The data set comprised Tropical Rainfall Measuring Mission (TRMM) thematic rainfall data, 1:50,000 scale topographical map sheets, geological maps, the ASTER Digital Elevation Model (ASTER GDEM), Landsat 7 Enhanced Thematic Mapper” (ETM7+), and Landsat 8 Operational Land Imager. The flash flood risk model of SR is developed using ArcGIS-10.3 geoprocessing tools integrating all the causal factors thematic maps. The final flood risk model for the SR suggests that 57% of the total basins in the SR are at high risk of flooding. Almost 38% of all basins are at moderate flood risk. The remaining 5% of basins are less prone to flooding. Flood-prone zones were identified, suitable dam-building sites were located, and extremely probable areas for water recharge were recognized. On the basis of reliable scientific data, structural and non-structural mitigation strategies that might reduce the damage susceptibility, alleviate the sensitivity of the flash flood, and best utilize its water supply were recommended.

Available satellite data for monitoring small and seasonally flooded wetlands in semi‐arid environments of southern Africa

AbstractTime‐series monitoring of wetland eco‐hydrological dynamics using remote sensing continues to be an attractive and practical tool, mainly due to its ability to overcome challenges related to in situ data availability. However, acquiring seamless and cloud‐free data for accurate and routine wetlands monitoring remains a persistent challenge. In this study, we aimed to evaluate the availability of satellite scenes in the Google Earth Engine (GEE) catalogue that could facilitate the monitoring of eco‐hydrological dynamics in small and seasonally flooded wetlands within the semi‐arid environments of southern Africa. The study covered a 20‐year period from 2000 to 2020, with a specific focus on the Nylsvley floodplain as a case study. The study conducted a comprehensive assessment of available products on the GEE platform, including Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI), Sentinel‐1 and Sentinel‐2. The identified images underwent rigorous filtering and screening based on varying cloud‐cover percentages (0%, 1%–10%, 11%–25% and 26%–50%). The results revealed a considerable number of satellite products (1376) available for the study period. Specifically, there were 492 Landsat images, 394 Sentinel‐1 images and 490 Sentinel‐2 images. Amongst these, Sentinel‐2 and Landsat‐7 had the highest number of images (69% and 76%, respectively) with cloud‐cover percentages ranging from 0% to 20%. However, images with cloud cover exceeding 26% were excluded from the analysis. Further analysis indicated that using satellite images with 0% cloud cover resulted in an overall accuracy (OA) ranging between 69% and 72%, while 1%–10% cloud cover had an OA ranging between 68% and 70%, and 11%–25% cloud cover had an OA ranging between 69% and 80.55% for both the dry and wet seasons. Overall, the classification results demonstrated satisfactory OAs (68%–82%) for all scenes, with some inaccuracies observed for certain classes, notably bare surface and long grass. These inaccuracies were particularly evident when using Landsat‐7 scenes, attributable to the spatial resolution of the data. The findings emphasised the availability of a substantial amount of archival satellite data, capable of monitoring small and seasonally flooded wetlands, providing valuable insights into the eco‐hydrological dynamics of these ecosystems. Moreover, the study highlighted the benefits of cloud‐computing platforms like GEE in addressing challenges associated with big data filtering, processing and analytics, thereby enhancing environmental monitoring and assessments, which may have been limited by the unavailability of advanced processing tools and seamless cloud‐free data.

Forest Loss Related to Brazil Nut Production in Non-Timber Forest Product Concessions in a Micro-Watershed in the Peruvian Amazon

Madre de Dios is considered an important center of biodiversity in Peru due to its extensive Amazonian forests. However, the forests are under growing pressure due to land invasion, agricultural expansion, and gold mining. This makes support for forest management very important. This study aimed to evaluate the relationship between forest loss, land cover, land-use changes, and Brazil nut (Bertholletia excelsa Humb. & Bonpl) production in forest concessions in the Peruvian Amazon (2004–2020). Remote sensing techniques were used to classify images using the random forest algorithm, which was applied to the Landsat-5 thematic mapper, Landsat-7 enhanced thematic mapper, and Landsat-8 operational land imagery. Brazil nut production data from 2004–2020 was provided by the Regional Forest and Wildlife Service of Madre de Dios. In forest concessions, the forest area decreased continuously over the whole study period (160.11 ha/year). During the same time period, the change in forest cover in the concessions from Brazil nut to other uses was 4681 ha. At the same time, the authorization and extraction of Brazil nuts varied during the study period but did not show a downward trend. We found a significant and inverse relationship between the conversion of forest to agricultural land and Brazil nut production. However, there were insignificant relationships between forest loss, the persistence of agricultural and forest areas, and Brazil nut production. Therefore, despite the forest loss in the forest concession areas, Brazil nut production has not decreased. Production may not be affected because land pressure is higher near access roads, affecting only the areas near the roads rather than the actual areas where the Brazil nut-producing trees are located. Our results showed that nut production in non-timber forest product concessions would be negatively affected by deforestation and forest degradation, but only slightly.

The impacts of armed conflict on vegetation cover degradation in Tigray, northern Ethiopia

Efforts made to restore the degraded landscape of the Tigray region, Northern Ethiopia, over the last three decades have been relatively successful. However, an armed conflict that broke out in the region in November 2020 has significantly destroyed the restored vegetation, either directly associated with conflict (environment, pollution, fire) or indirectly (agricultural abandonment). This study aimed at assessing spatio-temporal changes in vegetation cover in a 50 km radius zone centered on Mekelle city, Tigray. Vegetation cover dynamics was evaluated using Landsat Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) datasets for the years 2000, 2020, and 2022 and analysed using ENVI 5.3 and ArcGIS 10.8.1 software. These data were analysed using the Modified Normalized Difference Vegetation Index (MNDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), and Moisture Adjusted Vegetation Index (MAVI). Based on the MNDVI, results show that vegetation cover increased in the period 2000–2020 by 179 km2 or 2% of the area, whereas in the period 2020–2022, there was a decrease in vegetation cover by 403 km2 or 5% of the area. This was accompanied by a decrease in vegetation density. These vegetation changes in 2020–2022 are attributed to the impact of armed conflict on the land surface which can include farmlands and village abandonment, spread of weeds and scrub vegetation, or failure to harvest crops. Monitoring vegetation change using Landsat data can help understand the environmental impacts of armed conflict in rural agricultural landscapes, including potential food security risks.

APPLICATION OF A HYBRID CELLULAR AUTOMATON-MARKOV MODEL IN LAND USE CHANGE DETECTION AND PREDICTION IN FLOOD-PRONE AREA, JOHOR, MALAYSIA

Changes in land use can significantly impact natural resource sustainability, socioeconomic activities, and flood risks. Cellular Automata-Markov model (CA-Markov) is utilized in this study to predict land use changes by modeling the spatial dynamics and transitions of land use categories over time in one of the flood-prone area in Segamat district, Johor. Satellite images obtained from Landsat 5 Thematic Mapper and Satellite Pour I’Observation de la Terre (SPOT) 5, 6, and 7 for years 2006, 2011, and 2016 were utilized to assess the magnitude of the land use change via unsupervised and supervised classification. Additionally, ancillary data such as residential, road, water bodies, and slopes were used as input to forecast future land use. The findings revealed that between 2006 to 2026, there was an increase in built-up areas and mixed agriculture up to 26%. The expansion of built-up areas and mixed agricultures involves the removal of forests, further exacerbating flood risks. This fundamental research can provide valuable insights for effective land management and urban planning.

Towards global long-term water transparency products from the Landsat archive

Secchi Disk Depth (Zsd) is one of the most fundamental and widely used water-quality indicators quantifiable via optical remote sensing. Despite decades of research, development, and demonstrations, currently, there is no operational model that enables the retrieval of Zsd from the rich archive of Landsat, the long-standing civilian Earth-observation program (1972 – present). Devising a robust Zsd model requires a comprehensive in situ dataset for testing and validation, enabling consistent mapping across optically varying global aquatic ecosystems. This study utilizes Mixture Density Networks (MDNs) trained with a large in situ dataset (N = 5689) from 300+ water bodies to formulate and implement a global Zsd algorithm for Landsat sensors, including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) aboard Landsat-5, -7, -8, and -9, respectively. Through an extensive Monte Carlo cross-validation with in situ data, we showed that MDNs improved Zsd retrieval when compared to other commonly used machine-learning (ML) models and recently developed semi-analytical algorithms, achieving a median symmetric accuracy (ε) of ∼29% and median bias (β) of ∼3%). A fully trained MDN model was then applied to atmospherically corrected Landsat data (i.e., remote sensing reflectance; Rrs) to both further validate our MDN-estimated Zsd products using an independent global satellite-to-in situ matchup dataset (N = 3534) and to demonstrate their utility in time-series analyses (1984 – present) via selected lakes and coastal estuaries. The quality of Rrs products rigorously assessed for the Landsat sensors indicated sensor-/band-dependent ε ranging from 8% to 37%. For our Zsd products, we found ε ∼ 39% and β ∼ 8% for the Landsat-8/OLI matchups. We observed higher errors and biases for TM and ETM+, which are explained by uncertainties in Rrs products induced by uncertainties in atmospheric correction and instrument calibration. Once these sources of uncertainty are, to the extent possible, characterized and accounted for, our developed model can then be employed to evaluate long-term trends in water transparency across unprecedented spatiotemporal scales, particularly in poorly studied regions of the world in a consistent manner.

Assessment of forest cover and forest loss using satellite images in Thua Thien Hue province, Vietnam

Deforestation in the tropics continues inexorably with severe implications for biodiversity conservation, climate regulation and ecosystem services. This study investigated variation in forest cover in Thua Thien Hue province, Vietnam, using the Landsat Thematic Mapper and Operational Land Imager satellite images over the period 1989–2021. Imageries were classified using the maximum likelihood classification technique for the years 1989, 2006, and 2021 and were evaluated for accuracy using the kappa coefficient for each year. Furthermore, forest cover losses and gains were evaluated using the Normalized Difference Vegetation Index and Soil Adjusted Vegetation Index, which were compared with the output of the supervised classification. Results showed that the forest cover of Thua Thien Hue province has drastically declined over the years. The forest cover, which was estimated at 68.88% (3461.46 km2) of the total land area in 1989, increased to 69.04% (3469.51 km2) in 2006 and subsequently decreased to 57.55% (2891.81 km2) in 2021. Severely reduced forest cover is often associated with the expansion of agriculture on the forest edge; other contributing factors include logging, illegal production land, and forest fires. Overall, our results show the necessity of forest management, rational land-use planning policy, and increased community awareness of conservation and sustainable development of forest resources in the study area in the future.

LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia.

Recurrent changes recorded in LULC in Guna Tana watershed are a long-standing problem due to the increase in urbanization and agricultural lands. This research aims at identifying and predicting frequent changes observed using support vector machines (SVM) for supervised classification and cellular automata-based artificial neural network (CA-ANN) models for prediction in the quantum geographic information systems (QGIS) plugin MOLUSCE. Multi-temporal spatial Landsat 5 Thematic Mapper (TM) imageries, Enhanced Thematic Mapper plus 7 (ETM+), and Landsat 8 Operational Land Imager (OLI) images were used to find the acute problem the watershed is facing. Accuracy was assessed using the confusion matrix in ArcGIS 10.4 produced from ground truth data and Google Earth Pro. The results acquired from kappa statistics for 1991, 2007, and 2021 were 0.78, 0.83, and 0.88 respectively. The change detection trend indicates that urban land cover has an increasing trend throughout the entire period. In the future trend, agriculture land may shoot up to 86.79% and 86.78% of land use class in 2035 and 2049. Grassland may attenuate by 0.03% but the forest land will substantially diminish by 0.01% from 2035 to 2049. The increase of land specifically was observed in agriculture from 3128.4 to 3130 km2. Judicious planning and proper execution may resolve the water management issues incurred in the basin to secure the watershed.

Spectral Mixture Analysis (SMA) Model for Extracting Urban Fractions from Landsat and Sentinel-2A Images in the Al-Ahsa Oasis, Eastern Region of Saudi Arabia

The rapid expansion of urban areas is a major driver of deforestation and other associated damage to the local ecosystem and environment in arid and semi-arid oases, especially in the eastern region of Saudi Arabia. It is therefore necessary to accurately map and monitor urban areas to maintain the ecosystem services in these oases. In this study, built-up areas were mapped using a spectral mixture analysis (SMA) model in the Al-Ahsa Oasis in the eastern region of Saudi Arabia by analyzing Landsat images, including Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI), and Sentinel-2A images, acquired between 1990 and 2020. Principle component analysis (PCA) was used to build and select endmembers, and then SMA was applied to each image to extract urban/built-up fractions. In addition, this study also discusses the possible driving forces of the urban dynamics. SMA classification performance was assessed using fraction error maps and a confusion matrix. The results show that the Al-Ahsa Oasis’ urban area had been rapidly expanding during 2010–2020 with an expansion rate of nearly 9%. The results also indicated that the SMA model provides high precisions (overall accuracy = ~95% to 100%) for an oasis urban mapping in an arid and semi-arid region that is disturbed by the mixed-pixel problem, such as the Al-Ahsa Oasis in eastern Saudi Arabia.

Predicting land use/land cover changes in the Santchou Wildlife Reserve (Santchou, West-Cameroon) using a CA-Markov model

Over the last three decades, the Santchou Wildlife Reserve (SWR) located in the West region of Cameroon has experienced rapid degradation. Land reclamation and population growth have significantly contributed to meeting the increasing demands for foods and urban development. In this study, Landsat 4–5 Thematic Mapper (TM) images of 1988 and 2005, and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) image of 2022 are firstly used for calculating and analysing area changes and then spatial distribution of land use/land cover changes (LULCC) in the SWR for a time span 1988–2005–2022. Secondly, the spatial distribution of LULCC from 2022 to 2027 and from 2027 to 2032 was simulated using a Cellular Automata (CA)-Markov model that allows to predict future changes and identify the possible patterns of evolution. Results exhibit six major LULC classes including Mountain Forest, Degraded Lowland Forest, Lowland Forest, Degraded Mountain Forest, Built-up Area and Cultivated Land. The overall accuracies of LULC classification reached 89.71‒92.24 %, with Kappa coefficients of 0.87‒0.92. However, the prediction model was validated with an overall good agreement of 90.51 %. In general, two significant patterns of change were evidenced and characterized by the rapid expansion of Cultivated Land from 158.6 to 736.4 ha between 1988 and 2022, and could reach 809.0 ha in 2032, while Built-up Area displays a surface change from 12.4 to 29.3 ha, which is expected to attend 67.3 ha in 2032. Change patterns from the past to the future are also marked by a decline of Mountain Forest (2107.4 to 1594.4 ha), Degraded Lowland Forest (1450.1 to 1024.5 ha) and Degraded Mountain Forest (954.6 to 860.6 ha) at the expense of Lowland Forest (2332.5 to 2659.8 ha). Human activities influenced by socio-economic issues remain the main factor of changes in the SWR. Despite the limitations of the CA-Markov model due to the non-consideration of socio-economic factors, this study appears as a new opportunity to develop effective strategies for managing LULCC and protecting ecological biodiversity in the SWR.

Assessment of shoreline changes and associated erosion and accretion pattern in coastal watersheds of Tamil Nadu, India

The coastal region is not a constant and depends on several physical elements. Analogous to the world trend, the coastal areas of Tamil Nadu are encountering sea level rise and associated shoreline changes. However, the rate of change is not uniform and varies considerably. The study revealed such differences in shoreline changes during the past 30 years and identified erosion and accretion patterns trends in Tamil Nadu's coastal watersheds, from Pulicat (Thiruvallur district) to Kodyar (Kanyakumari district). The study used remotely sensed Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) datasets to analyse shoreline changes from 1988 to 2018. The Digital Shoreline Analysis System (DSAS) tool is used to derive trends of shoreline changes by End Point Rate (EPR) and Net Shoreline Movement (NSM). The mean EPR is- 0.26 ​m/yr and NSM is-8.03 ​m/yr, suggesting the overall shoreline of the Tamil Nadu coast is shifting landward. However, the annual rates of EPR and NSM vary considerably from watershed to watershed, and therefore, these indices were used to categorise the coastal watersheds based on erosions and accretions. The results identified a high erosion rate in the watersheds of Coleroon, Arasalar (Nagapattinam), Gundar (Ramanathapuram), Vembar, Lower Vaippar (Thoothukkudi), Nambiyar, Hanuma (Tirunelveli), and Putian, Kodyar (Kanyakumari). Field surveys were conducted to verify ground conditions at 139 random locations along the 1000 ​km shoreline stretch. About 75% of respondents said they experience a very high to high risk of coastal erosion. The rates estimated by the study and categorisation of the coastal watersheds could be most helpful in evaluating the cumulative impact of coastal hazards and preparing sustainable development plans. The outcomes may also help to create awareness in more susceptible areas.

Glacier Change in the West Kunlun Main Peak Area from 2000 to 2020

Glaciers are sensitive indicators of climate change, and investigation of their dynamics is crucial for ensuring regional ecological security as well as disaster prevention and mitigation measures. Based on Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) imagery, the outlines and length of glaciers in the West Kunlun Main Peak Area (WKMPA) during 2000–2020 were obtained by combining a band ratio method with manual interpretation and an automatic extraction method for the glacier centerline, respectively. There were 440 glaciers in the WKMPA in 2020, covering an area of 2964.59 ± 54.87 km2, with an average length of 2916 ± 60 m. The glacier count increased due to division, while the area and length all exhibited a declining trend from 2000 to 2020, at rates of −0.04%·a−1 (24.83 km2) and −0.11%·a−1 (66 m), respectively. Glacier retreat was primarily observed during the early period (2000–2005). Except for glaciers located above an elevation of 6250 m, the glacier area decreased with each altitude interval from 2000 to 2020, and the rate of relative change in glacier area generally decreased with increasing altitude. Moreover, except for a slight increase in north-facing glaciers, the area of glaciers facing other orientations decreased during 2000–2020. The accuracy of the empirical formula fit for glacier length was highly dependent on glacier class, with greater precision observed for smaller glaciers and lower precision for larger valley-basin glaciers due to their complex morphological structures being neglected and only a single quantitative relationship being considered. There was a time lag of 12 years between temperature changes and glacier area response in this region. The mechanism by which glacier division affects glacier change is complex, requiring dissection of multiple factors such as area, length, and terminal elevation before and after division.