AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

We live in an urbanizing world. Since 2008, more than half of humanity lives in cities, both large and small, and old and new.[...]

This study aimed to monitor the change in land cover in Garabulli regionduring the period (1992 - 2010) by using remote sensing technique. The satellite imagesused in this study were obtained from the satellite Spot 4 for each of years of 1992 and 2000 and Spot 5 for year 2010. The supervised classification was performed on the Spot images using maximum likelihood classification. The land cover changes were detected during different times by land cover maps which were produced using ENVI software. The results have revealed clear changes in the land cover whereas barren land, agricultural land, and urban area have been increased by 37.6%, 35.1%, and 28% between 1992 and 2010, respectively. Meanwhile, forest and rangeland were decreased between 1992 and 2010 by 65% and 41%, respectively. The results showed that declining of forest and rangeland may lead to rapidly increase of desertification. Additionally, the present study revealed that the remote sensing techniques can be used effectively in monitoring and interpreting the changes which may occur in the land cover.

Objective: This research proposes a change detection in the satellite images and land cover analysis using a Bidirectional Long Short-Term Memory (BiLSTM) model and vegetation index-based feature maps for future map generation. Change detection complements land cover analysis by identifying and quantifying alterations in land cover over time. Theoretical framework: Land cover analysis and land cover change detection plays crucial role in understanding and monitoring the Earth's surface.Despite its importance, land cover analysis and change detection face several challenges. One major challenge is obtaining accurate and up-to-date information on land cover and change detection across large areas can be complex and costly. Inconsistent data sources and limited access to historical records can hinder the accuracy and reliability of change detection analyses.These challenges requires a combination of technological advancements, improved data availability, refined algorithms, robust validation approaches, and interdisciplinary collaborations. Method: BiLSTM method is used for implementation which is powerful for land cover classification, as it allowing the integration of spatial and temporal information and capturing complex patterns in satellite imagery data. BiLSTM, tend to be more complex but they often offer higher accuracy and the ability to learn intricate patterns and representations. Results and conclusion: The six vegetation index-based feature maps are considered.Therefore, the resulting accuracy is also determined using the Flamingo-Hyena optimization (FHO), and the experimental outcomes disclosed that the proposed model is superior to the existing model in terms of accuracy with 0.95%, MSE with 0.05%, precision with 0.94%, Recall with 0.94%, and F1 measure with 0.94% respectively. vegetation index-based feature maps are essential for land cover analysis and change detection as they provide valuable information on vegetation dynamics, ecological processes, land management, and climate change impacts. Research implications: This process land cover analysis and change detection helps to detect deforestation, urban expansion, agricultural expansion, and other land cover changes. By harnessing these techniques, policy makers can address emerging issues such as deforestation, loss of biodiversity, and encroachment on natural habitats.

With the development of society and economy, the rapid urban sprawl in land use is being witnessed of late in urban districts. As a result, many problems in society and environment have appeared in urban districts, such as population expansion, air pollution, etc. Assessment and inventory on urban sprawl is essential for city planning and sustainable development. The objective of this study was to detect the urban sprawl of Chengdu city (1992-2006) following the RS (remote sensing) and GIS technique. RS data provided valuable information for understanding and monitoring the process of urban land cover changes. RS and GIS is gaining importance as vital tool in the detecting on urban sprawl of cities based on RS data. The urban sprawl area extracted from RS data of Chengdu city is 9882.808hm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 2.686 times 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> hm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in 1992, 2006, respectively. The result shows that the Chengdu city is expanding in different directions resulting in large-scale urban sprawl and changes in urban land use, the spatial pattern of such changes is clearly noticed on the urban fringes than in the city centre.

&lt;p&gt;The study area lies to the east of the Nile (Sharg Elneel), Khartoum State (latitudes 15&lt;sup&gt;o&lt;/sup&gt; 25̎ 1̍ and 16° 19̎ 1̍ N and longitudes 33° 19̎ 8̍ and 33°02̎ 9̍ E). Using remote sensing techniques and geographic information system (GIS), the changes in land cover/land use have been estimated using two methods: supervised and unsupervised classification. the images were those of the years 1973, 2001, and 2015 MSS, ETM, ETM+, respectively(173/49 &amp;amp; 173/48 path/ row). The study area was classified into the following nine LU/LC types: water bodies, vegetation, rocky area, sandy soil, sandy sheet, clayey soil, bare soil, sand dunes and settlement areas. The individual areas covered by each type of land use/ land cover were calculated for each image using supervised and unsupervised classification. Then the areas were compared among the different years (images). The results indicated a decrease in areas of sandy soil, water bodies, vegetation cover, sand dunes, clay soil, and bare soil for years 1973-2001 and 1973-2015. That was associated with significant increase in settlement area, sand sheet for the same period. As for the period 2001 and 2015 was an increase in the areas of vegetation, sandy soil, dunes, clay soil, and settlement. While there was a decrease in water bodies, rocky area, sand sheet and bar soil. A striking result of his study was an increase of 50% in the settlement area for the period 1973 – 2015. This indicated that more drift of people towards the Capital took place during this period possibly due to drought and civil strife. Also people come to Khartoum to have better living conditions, education, health care and to work and may be they look at Khartoum as a spring board for going abroad. This study recommended the use of remote sensing techniques and geographic information system in the follow up of desertification and land degradation by following changes in land cover and land use. It also recommended that sand movement (sand encroachment) shall be retarded possibly through increasing vegetation cover through seed broadcasting of pasture and range plants during the rainy season and to exploit the ground water of the NSS aquifer for irrigation.&lt;/p&gt;

Remote sensing technology has emerged as a vital tool for monitoring and sustainably managing the environment. This paper reviews recent advances in remote sensing and their applications for environmental sustainability. A comprehensive literature review was conducted focusing on high-resolution analysis, temporal change detection, and hyper-spectral monitoring. Applications highlighted include detailed urban habitat mapping, assessing shoreline erosion, tracking forest disturbances, monitoring crop health, detecting pollution, and mapping coral reef degradation. The results showcase the quantitative insights remote sensing provides across diverse sustainability issues like climate change, urban planning, conservation, and disaster response. The paper emphasizes how ongoing improvements in remote sensing are enhancing environmental modelling capabilities and information availability, playing a key role in evidence-based decision-making for sustainable resource management. 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Derivation of more sensitive spectral features from the satellite data is immensely important for better retrieving land cover information and change monitoring, such as changes in snow covered area, forests, and barren lands as some examples from local to the global scale. The major objectives of this paper are to present the potential of water-resistant snow index (WSI) for the detection of snow cover changes in the Himalayas, extant two composite images, biophysical image composite (BIC) and forest cover composite (FCC) for the detection of changes in barren lands and forested areas respectively, and two newly designed composite images, water cover composite (WCC) and urban cover composite (UCC) for the detection of changes in water and urban areas respectively. This research implemented the image compositing technique for the detection and visualization of land cover changes (water, forest, barren, and urban) with respect to local administrative areas where a significant land cover change occurred from 2001 to 2016. A case study was also conducted in the Himalayan region to identify snow cover changes from 2001 to 2015 using the WSI. Analysis of the annual variation of the snow cover in the Himalayas indicated a decreasing trend of the snow cover. Consequently, the downstream areas are more likely to suffer from snow related hazards such as glacial outbursts, avalanches, landslides and floods. The changes in snow cover in the Himalayas may bring significant hydrophysical and livelihood changes in the downstream area including the Mekong Delta. Therefore, the countries sharing the Himalayan region should focus on adapting the severe impacts of snow cover changes. The image compositing approach presented in the research demonstrated promising performance for the detection and visualization of other land cover changes as well.

Land use and land cover changes can have detrimental effects on the ecology, if they are not properly aligned with the characteristics of the land. This study aims to evaluate the temporal changes in land use and land cover of Bafra Delta plain, situated in the east of Samsun province. The region is one of the most significant plains within the Black Sea area. Remote sensing technique was utilized in this research which made use of Landsat images from 1990, 2000, 2010, and 2020. Supervised classification was applied in ENVI 5.3v software to perform calculations, resulting in six main classes. Field work was applied to classify the unclassified classes. The resulting six land use-land cover classes were agriculture lands, forest, dune, marshy, water surface, and artificial areas. To determine land use efficiency, analogue data was digitised and transferred to a GIS database. The agricultural areas occupy the largest portion of the plain, followed by hazelnut and artificial areas. The changes over the last decade, notably the growth of artificial areas and water surfaces, and the reduction of arable lands, highlight significant variations in size across the areas. Furthermore, the study indicated that remote sensing and geographic information system techniques play a crucial role in identifying and monitoring land cover and land use trends on a large-scale to produce accurate and timely data. Poorly adapted land use changes can cause major ecological damage. The aim of this study is to identify the changes over time in land use and land cover of Bafra Delta plain, located to the east of Samsun city and one of the most significant plains in the Black Sea region, using remote sensing techniques. To this end, Landsat images from 1990, 2000, 2010 and 2020 are utilized. To perform the calculations, ENVI 5.3v software was employed, applying a supervised classification technique that resulted in forming six main classes. Fieldwork was conducted to classify the unclassified classes. The resulting land-use and land-cover classes were agricultural land, forest, dunes, marshland, water surface, and artificial areas. To evaluate land-use efficiency, analogue data were digitalised and imported into a GIS database. The plain's most extensive land-use areas consist of agricultural lands, followed by hazelnut and artificial areas. In the last decade, the rise in artificial and water surfaces and the decline in agricultural areas highlights significant changes in the region's size. This study also emphasises the crucial role of remote sensing and geographic information system techniques in generating fast and consistent data for monitoring large-scale land cover and land use trends.

In the current study, remote sensing techniques and geographic information systems were used to detect changes in land use / land cover (LULC) in the city of Al Hillah, central Iraq for the period from 1990 - 2022. Landsat 5 TM and Landsat 8 OLI visualizations, correction and georeferencing of satellite visuals were used. And then make the necessary classifications to show the changes in LULC in the city of Al Hillah. Through the study, the results showed that there is a clear expansion in the urban area from 20.5 km2 in 1990 to about 57 km2 in 2022. On the other hand, the results showed that there is a slight increase in agricultural areas and water. While the arid (empty) area decreased from 168.7 km 2 to 122 km 2 in 2022. Long-term urban planning, which is based on LULC analysis, is an effective tool for decision makers to study future patterns in urban expansion in parallel with the expected rise of the population in Iraq in the coming years. . Therefore, it is necessary to use equivalent systematic plans to take into account the theory of urban and population expansion of cities by providing the best places for the population to be concentrated in appropriate ways in the future.

The present study analyses the land use change caused by the construction of Tehri dam in Bhagirathi river. Geospatial techniques like Geographic Information System (GIS) and remote sensing have been used to make land use map using Landsat satellite image of 2000 and 2014. In Tehri district, change in land use and new developments (industrial, urban and commercial) were observed. Land use land cover change was done using two satellite images, classifying them via supervised classification and applying change detection in the classified images. Classified images had an overall accuracy of 88.57 and 88.31%. The results were validated using the ground truth points distributed all over the study. Seven main classes were identified in the study area as water, open forest, dense forest, river bed, agriculture, urban and others (which includes scrub and barren land). The increase was observed in built-up class from 2000–2014. The decrease was observed in the open, dense forest and river bed. The present study showed that the construction of the hydropower and associated construction activities had caused changes in the Tehri valley. Keywords: Change detection, land use, land cover, hydropower, Tehri, supervised classification Cite this Article Disha Punetha, Archana Sharma, Pooja Panwar. Monitoring of Tehri Hydroelectric Plant Induced Land Use Land Cover Change Detection in Garhwal District of Uttarakhand. Journal of Remote Sensing & GIS. 2018; 9(2): 1–9p.

Floods are one of the most widely distributed hazards around the world and their management is an important issue of concern among all the stakeholders. The aim of this review is to synthesize the state of art literature in the application of Geographical Information Systems (GIS) and Remote Sensing (RS) techniques in all the flood management stages (pre-flood, during flood and post-flood stages). Flood types and common concepts in flood management are precisely explained. Case studies of flood management using GIS and RS are summarized. Current challenges in using GIS and RS techniques for flood management are also given. One lesson we learn from this review is that flood management is very diverse and it requires multidisciplinary involvement. It can also be deduced that RS techniques offer cheaper and faster options of accessing spatial data about the flood event even in the physically inaccessible areas. GIS techniques on the other hand facilitate hydrological models in data collection, analysis, querying and presentation of information in a more simplified format. The present review is expected to contribute to an improved understanding of the potential applications of RS and GIS techniques in flood management, especially among scientists in the developing countries where the use of these techniques particularly in flood management has generally been limited.

The term Land Use relates tothe human activity associated with specific of land for example areas under settlement, agriculture, forest, vegetation etc. The cumulative pressure on land for food and wood, the natural balance of soil and environment is affected by deforestation activities which causes serious problem of land degradation. The present study is focused on the changing Land Use / Land Cover practices. “These land use and land cover data are very important for land resource management, planners, and decision makers” (Ndukwe, 1997). In this study has been studied Land use / Land cover change detection of Jhansi district over a period of 12 years. Landsat TM and Landsat ETM + data from U S Geological Survey (1996, 2008) and Survey of India toposheets (2006) have been used for the delineation of classes and district boundary. A study of spatial and temporal changes in Land use / Land cover (LULC) is conducted using Remote Sensing and GIS techniques. ArcGIS 9.3 and ERDAS Imagine 9.2 interface were used for preparing LULC and change detection. The study area is categorized into eight foremost LULC classes viz., built-up land, cropland, fallow land, dense forest and open forest, barren land, sandbar, water bodies. Land use / land cover is very important aspects of land management and development planning of any region. There for, it has broadly been explained and considered as a very imperative element of physical input for agricultural development and land use sustainability.

The present study assessed the changes in land use and land cover to correlate the variations in the land surface temperature of Chattogram City. To analyze land use land cover (LULC) change and determine its effects on land surface temperature in the city area, temporal Landsat (5,7 ETM+ and 8,9 OLI) imageries from four time periods (2007, 2012, 2017, and 2022) were used. To assess the correctness of the picked random pixels, current ground truth data gathered from several sources was applied. Raster data has been utilized to identify the places that are influenced year-round in the green space (i.e., vegetation cover) and to examine the remote sensing image categorization for the green area using satellite images. These enable the study to explain the causes of the degradation and alteration of green space throughout time. The study identified that urbanization has resulted in a significant rise (about 2840 hectares, 16.74%) in urban land between 2007 and 2022, causing a loss of vegetative land (about 656 hectares, 3.85%). Additionally, the research concentrated on the actual affected area and attempted to forecast the cities’ land use in 2037, which revealed a large loss of vegetation by that year. The research has the potential to be utilized as a reference in the future.

An ensemble method for monitoring land cover changes in urban areas using dense Landsat time series data

The increase in the urban heat island is caused by the replacement of vegetation cover by impervious surfaces. As the population of Addis Ababa City has increased dramatically, the vegetation cover and other land cover classes have been converted into built-up areas. This study attempted to examine the relationship between urban heat islands and urban thermal comfort (UTCL) and land use and land cover (LULC) change using geospatial technologies in Addis Ababa City, Ethiopia. Landsat TM 1991, Landsat ETM + 2005, and Landsat OLI/TIRS 2021 data were used in this study. During the study period, LULC change, land surface temperature (LST), and urban heat island were calculated using the multispectral and thermal infrared bands (1991-2021). Results revealed that the built-up area in 1991 was 96.6 km2 (18.3%), and increased to 165.4 km2 (31.4%) and 277.2 km2 (52.6%) by 2005 and 2021, respectively. In contrast, agriculture and vegetation land cover classes were declined by 66.8 km2 and 25.7 km2, respectively between 1991 and 2021. Rapid conversion of LULC change increases the mean LST of Addis Ababa City by 8.3°C over the last three decades. According to the results, a high LST was recorded over built-up regions and areas with little vegetative cover. Furthermore, the central areas of the study area suffered a greater UHI effect than the surrounding areas. The results of the urban thermal field variance index (UTFVI) revealed that the UHI varies greatly across the city. Strong, stronger, and strongest urban heat islands dominated the central, southwestern, and southeastern suburbans of the study area, respectively. The excellent comfort level has declined from 16.3 km2 (3.1%) in 1991 to 12.1 km2 (2.3%) in 2021. The study proposed that local community awareness needs to be raised for environmental conservation through the establishment of urban green spaces that reduce UHI and increase comfort in Addis Ababa City.