Using Remote Sensing and GIS Techniques to Study Soil Degradation Processes in North Shaanxi Province, China

Satellite image data and thematic map data were used to provide comprehensive views of surface-bound conditions such as soil and vegetation degradation. The current work applies a computerized parametric methodology, developed by FAO, UNEP and UNESCO to assess and evaluate soil degradation at 1 : 250 000 mapping scale. The study area is located in the arid and semi-arid zone of the northern part of Shaanxi Province in China, a region with considerable agricultural potential; Landsat TM images were utilized to provide recent data on land cover and use of the area. ARC/INFO and Arc-View softwares were used to manage and manipulate thematic data, to process satellite images, and tabular data source. ER mapper software is utilized to derive the normalized difference vegetation index (ND VI) values while field data to estimate soil erodibility (SE) factor. A system is established for rating soil parameters, slope, climate factor and human factor activity. The rating values serve as inputs into a modified universal soil loss equation (USLE) to calculate the present state and risk for soil degradation processes, namely soil wind erosion. The produced maps and tabular data show the risk and the present status of different soil degradation processes. The study area, in general, is exposed to high risk of wind erosion and high hazards of water erosion. Several desertification maps were produced, which reflect the desertification types persisting in the study area. Wind erosion, water erosion, vegetation degradation,physical degradation and salinization are the basic desertification maps, and others are combinations of these basic maps. In terms of statistic analysis, 33.75 % of the total land area (120. 330 0 ha) is considered as sand or sand dune, and not included in our analysis of desertification. About 29. 41% of the total land area has slight or moderate desertification and 37. 465 % is facing severe desertification.     

松嫩平原荒漠化的EOS-MODIS 数据研究*

文章应用EOS-MODIS数据对松嫩平原的荒漠化问题进行了研究。首先计算出标准化植被指数(NDVI)和植被覆盖指数(VCI),反演求得荒漠化指数(DI),得到荒漠化面积;采用多种方法和复杂步骤,首次获得了松嫩平原碱质荒漠化、沙质荒漠化的面积,分别为160.30×10⁴hm²和50.56×10⁴hm²,分别占该区面积的24.35 ％ 和7.84 ％ ;应用数字高程模型(DEM)对荒漠化的分布规律和成因进行了分析和讨论,指出松嫩平原以碱质荒漠化为主, 当前荒漠化的发展程度已处于临界状态,对区域可持续发展构成了严重威胁,亟需采取行之有效的防治措施。     

Geospatial tools for assessing land degradation in Budgam district,Kashmir Himalaya,India

Land degradation reduces the ability of the land to perform many biophysical and chemical functions. The main aim of this study was to determine the status of land degradation in the Budgam area of Kashmir Himalaya using remote sensing and geographic information system. The satellite data together with other geospatial datasets were used to quantify different categories of land degradation. The results were validated in the field and an accuracy of 85% was observed. Land use/land cover of the study area was determined in order to know the effect of land use on the rate of land degradation. Normalized differential vegetation index (NDVI) and slope of the area were determined using LANDSAT-enhanced thematic mapper plus (ETM+) data, advanced space borne thermal emission and reflection radiometer, and digital elevation model along with other secondary data were analysed to create various thematic maps, viz., land use/land cover, geology, NDVI and slopes used in modelling land degradation in the Kashmir Himalayan region. The vegetation condition, elevation and land use/land cover information of the area were integrated to assess the land degradation scenario in the area using the ArcGIS ‘Spatial Analyst Module’. The results reveal that about 13.19% of the study area has undergone moderate to high degradation, whereas about 44.12% of the area has undergone slight degradation.     

内蒙古中部地区土地荒漠化遥感调查及环境质量评价

选择内蒙古中部地区作为典型荒漠化区域,根据荒漠化土地分类体系,确定决策树的结构以及各类地物在树形中的位置。基于各类地物的光谱反射特性和图像数据反映的综合特征,采取相应的识别和提取方法,以最大限度地利用遥感数据源。在研究过程中,采用从多光谱及多时相TM和ETM⁺遥感数据中提取出的光谱反射特性、土壤调节植被指数、土壤亮度指数以及土壤湿度指数等复合指数进行不同地物的分类和提取。结果表明:利用决策树分层提取法可以有效地排除和避免提取地物时多余信息的干扰及影响,目标明确。然后,通过图像代数检测法及目视解译法确定1990-2000年土地荒漠化的变化信息,对内蒙古中部地区的荒漠化演变规律做出分析。最后,结合研究区气候数据,在GIS支持下,通过对研究区生态环境质量变化和气候因子变化的相关性分析,表明研究区影响环境变化的主要因子是降水量与蒸发量(干燥度)。     

Extraction of impervious features from spectral indices using artificial neural network

An urban area comprises a complex mix of diverse land cover types and materials. Urban ecology and environment is significantly influenced by the proportion of impervious cover that is increasing considerably with time due to the continuous influx of people into urban areas. Therefore, it is of vital importance to determine the spatiotemporal pattern and magnitude of urbanization. In the present study, we have employed a supervised backpropagation neural network in order to extract the impervious features using five spectral indices, such as one vegetation index—Soil-Adjusted Vegetation Index (SAVI), one water index—Modified Normalized Water Index (MNDWI), and three urban indices—Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Index-Based Built-up Index (IBI). The study has been performed using Landsat Thematic Mapper data of November, 2011, of the rapidly urbanizing city of Ranchi, capital of Jharkhand state, India. Using different combinations of these spectral indices while keeping SAVI and MNDWI constant, seven composite images were built, and from each of these composites, impervious features were classified and its accuracy assessed with reference to high-resolution images provided by Microsoft Bing Imagery and adequate ground truthing. It was observed that along with SAVI and MNDWI, whenever IBI was used in any combination, it decreased the classification efficiency. On the other hand, NDBI and BUI, individually or when used together, discriminated the impervious features from the others with high accuracy with the combination of SAVI, MNDWI, and BUI achieving the highest accuracy of 90.14 %.     

Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province,Iran)

As drought occurs in different climates, assessment of drought impacts on parameters such as vegetation cover is of utmost importance. Satellite remote sensing images with various spectral and spatial resolutions represent information about different land covers such as vegetation cover. Hence, the purpose of this study was to investigate the performance of satellite vegetation indices to monitor the agricultural drought on a local scale. In this regard, satellite images including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) data were used to evaluate vegetation cover and their gradual changes effects on agricultural drought. Fars province in Iran with relatively low precipitation values was selected as the study area. Modified Perpendicular Drought Index (MPDI), MPDI1, Vegetation Condition Index (VCI), Normalized Difference Vegetation Index Anomalies (NDVIA), and Standardized Vegetation Index (SVI), were evaluated to select the remote sensing based index with the best performance in drought monitoring. The performance of such indices were investigated during 13 years (2000–2013) for MODIS and 29 years (1985–2013) for AVHRR. To assess the efficiency of the satellite indices in drought investigation, Standardized Precipitation Index (SPI) data of five selected stations were used for 3, 6, and 9 month periods on August. The results showed that NDVI-based vegetation indices had the highest correlation with SPI in cold climate and long-term timescale (6 and 9 month). The highest correlation values between remote sensing based indices and SPI were acquired, respectively, in 9-month and 6-month time-scales, with the values of 43.5% and 40%. Moreover, VCI showed the highest capability for agricultural drought investigating in different climate regions of the study area. Overall, the results proved that NDVI-based indices can be used for drought monitoring and assessment in a long-term timescale on a local time-scale.

     

Mapping coastal erosion at the Nile Delta western promontory using Landsat imagery

A set of six Landsat satellite images with 5–9 years apart was used in a post-classification analysis to map changes occurred at Rosetta promontory between 1973 and 2008 due to coastal erosion. Spectral information were extracted from two multi-spectral scanner (MSS) images (1973 and 1978), three thematic mapper (TM) images (1984, 1990, and 1999), and one enhanced thematic mapper plus (ETM+) image (2008). To estimate the quantity of land loss in terms of coastal erosion, a supervised classification scheme was applied to each image to highlight only two classes: seawater and land. The area of each class was then estimated from the number of pixels pertaining to this class in every image. In addition, the shoreline position was digitized to address retreat/advance pattern throughout the study period. Results showed that Rosetta promontory had lost 12.29 km² of land between 1973 and 2008 and the shoreline withdrew southward about 3.5 km due to coastal erosion. Most land loss and shoreline retreat occurred between 1973 and 1978 (0.55 km²/year and 132 m/year, respectively). Coastal protection structures were constructed successively at the promontory. These structures have considerably contributed to reduce coastal erosion; however, they promoted downdrift erosion.     

Spatial assessment of desertification in north Sinai using modified MEDLAUS model

This study aims to use spatial analyses and a geographic information system (GIS) to assess the environmental sensitivity for desertification in the north Sinai Peninsula, Egypt. Based on the Mediterranean Desertification and Land Use (MEDALUS) approach and the characteristics of the study area, a regional model was developed using GIS. Five main indicators of desertification including soil, climate, erosion, plant cover, and management were considered for estimating the environmental sensitivity to desertification. A spatial analyst extension Arc-GIS 10 software was used for matching the thematic layers and assessing the desertification index, of which the map of environmentally sensitive areas of the north Sinai Peninsula is produced. The obtained data reveals that 65 % of north Sinai is characterized by very severe sensitivity to desertification while the low sensitive one exhibits only 1.2 %. The moderately sensitive area occupies approximately 23 % of the study area. ETM+ and SPOT images are recommended to monitor sensitivity. The MEDALUS model was developed under the Egyptians to assess desertification sensitivity.     

Application of ASTER and Landsat TM Data for Geological Mapping of Esfandagheh Ophiolite Complex,Southern Iran

This research developed an approach to enable the discrimination of lithological units and detection of host rock of chromitite bodies within ophiolitic complexes using the advanced spaceborne thermal emission and reflection radiometer (ASTER) and Landsat thematic mapper (TM) satellite data. Three main ophiolite complexes located in southern Iran were selected for the study. A specialized band ratio (4/1, 4/5, 4/7) of ASTER, minimum noise fraction (MNF) components and spectral angle mapper (SAM) on ASTER and Landsat TM data were used to distinguish ophiolitic rock units. Results show that the specialized band ratio was able to identify different rock units and serpentinized dunite as host rock of chromitites within ophiolitic complexes. Minimum noise fraction components of ASTER and Landsat TM data are suitable for distinguishing ophiolitic rock complexes at a regional scale. The integration of SAM and feature level fusion used in this investigation discriminated the ophiolitic rock units and provided geological map for the study area, including identification of high potential areas (serpentinite dunite) for chromite exploration targets.     

The relationship between land surface temperature and land use/land cover in Guangzhou,China

The integration of remote sensing, geographic information system, landscape ecology and statistical analysis methods was applied to study the urban thermal environment in Guangzhou. Normalized Difference Vegetation Index (NDVI), Normalized Difference Build-up Index (NDBI), Normalized Difference Barren Index (NDBaI) and Modified Normalized Difference Water Index (MNDWI) were used to analyze the relationships between land surface temperature (LST) and land use/land cover (LULC) qualitatively. The result revealed that, most urban built-up lands were located in the middle part, and high LST areas mostly and were in the middle and southern parts. Therefore, the urbanization and thermal environment in the middle and southern parts need to be determined. Land surface temperature increased with the density of urban built-up and barren land, but decreased with vegetation cover. The relationship between MNDWI and LST was found to be negative, which implied that pure water would decrease the surface temperature and the polluted water would increase the surface temperature. A multiple regression between LST and each indices as well as the elevation was created to elevate the urban thermal environment, which showed that NDVI, NDBI, NDBaI, MNDWI were effective indicators for quantifying LULC impacts on LST.     

Investigating vegetation biophysical and spectral parameters for detecting light to moderate grazing effects: a case study in mixed grass prairie

Identifying effective vegetation biophysical and spectral parameters for investigating light to moderate grazing effects on grasslands improves management practices on grasslands. Using mixed grasslands as a case study, this paper compares responses of vegetation biophysical properties and spectral parameters derived from satellite images to grazing intensity, and identifies the suitable biophysical and spectral parameters to detect grazing effects in these areas. Biophysical properties including cover, canopy height and Leaf area index (LAI) were measured in three sites with different grazing managements and one benchmark site in 2008 and 2009 in Grasslands PlaceTypeNational Park and surrounding provincial pastures, Canada. Thirteen vegetation spectral indices, calculated by statistically combining different spectral information, were evaluated. The results indicate that canopy height and the ratio of photosynthetically active vegetation cover to non-photosynthetically active vegetation cover (PV/NPV) showed significant differences between ungrazed and grazed sites. All spectral vegetation indices except the canopy index (CI) show significant differences between grazing treatments. Red-Near infrared (Red-NIR) based vegetation indices, such as Modified Triangular Vegetation Index 1 (MTVI1), Soil-adjusted Vegetation Index (SAVI), are significantly correlated to the PV/NPV. Green/Mid-infrared (Green/MIR) related vegetation indices, i.e. Plant Senescence Reflectance Index (PRSI) and Normalized Canopy Index (NCI), show significant correlation with canopy height. Models based on a linear combination of MTVI1 and SAVI were developed for PV/NPV and PRSI and NCI for canopy height. Models that simulated PV/NPV and canopy height show significant correlations with grazing intensity, suggesting the feasibility of remote sensing to quantify light to moderate grazing effects in mixed grasslands.     

Study of Sand Dunes and Their Effect on Desertification of Cultivated Lands in Shaanxi Province, China Using Remote Sensing Techniques

About half of the arid and semi-arid lands in the world are deserts that comprise various types of aeolian sand dunes deposits. In Shaanxi Province, aeolian sand dunes cover considerable areas of the Yulin desert and northern Jinbian. Sand dunes are moving in the main wind direction and converting some agricultural area to wasteland. Remote sensing of sand dunes helps in the understanding of aeolian process and desertification. Remote sensing data combined with field studies are valuable in studying sand dunes, regional aeolian depositional history. In particular, active and inactive sand dunes of the north Shaanxi Province were studied using remote sensing and geographic information system. In this study, we describe the Landsat thematic mapper (TM) images, covering north Shaanxi Province, which were used to study the distribution, shape, size, trends, density and movement of sand dunes and their effect on desertification of cultivated lands. Estimation was made depending on soil erodibility factor (Ⅰ) and local climatic factor (C) during the period (June to September). The result indicates that soil erosion caused sand drift of 8.957 5, 7.03 ton for Yulin and Jinbian, respectively. The mean sand dunes movement rate were 4.37, 3.11 m, whereas, monthly sand dune advance rate were 1.092 5, 0.777 5 m, for the two locations, respectively. The study reveals that cultivated lands extended obliquely to the direction of sand dune movement are extremely affected, while other segments that extend parallel to the direction of the movement are not affected. Accordingly the north Shaanxi Province was divided into areas of different classes of potential risk. Moreover, blown sands and sand movement from neighboring highlands also affect the area of western desert.     

Environmental degradation assessment in arid areas: a case study from Basra Province, southern Iraq

Evaluation of recent land degradation affecting Basra Province, Iraq, resulted in the identification of five prominent environmental degradation processes: desertification, secondary salinization, urbanization, vegetation degradation, and loss of wetlands. This analysis was carried out using ‘3S’ technologies [remote sensing, geographic information system (GIS), and global position system], with the layers extracted and manipulated from available topographic, climatic, and soil maps, as well as satellite image (thematic mapping in 1990 and enhanced thematic mapping in 2003) and field survey data analyses. Rates of conversion were calculated and distribution patterns were mapped with the aid of a GIS. The results revealed that land use changes have affected the wider environment and accelerated land degradation, with severe damage located in southwestern Basra Province representing 28.1 % of the total area. Areas of high to moderate degradation characterize the rest of the south, representing 52.7 % of the total area; while the north of the study region is characterized by very low and low degradation levels accounting for 8.5 and 10.7 %, respectively. Iraq faces serious environmental degradation problems that must be addressed immediately; failure to do so will greatly compound the cost and complexity of later remedial efforts, with environmental degradation beginning even now to pose a major threat to human well-being, especially among the poor.     

Land subsidence (2004–2013) in Changzhou in Central Yangtze River delta revealed by MT-InSAR

Droughts are hindrances to economic and social developments in many parts of the world, especially in developing nations like Kenya. In North Eastern Kenya (NEK), drought is very prevalent. The communities in the region are mainly dependent on animal farming, and drought occurrence leads to great socioeconomic setback. Drought indices used in most studies consider rainfall as the only parameter for assessing drought occurrences. This study analyzes drought in NEK using the Standardized Precipitation Index (SPI) and the Combined Drought Index (CDI) using rainfall and temperature values and Normalized Difference Vegetation Index values for the period 1980–2010. The results of the two indices show significant correlation. However, CDI is preferred in the analysis of the drought compared to the SPI as it gives drought in more detail, showing extreme, severe, moderate and mild. The study recommends the use of the two methods independently since they give similar results and further recommends trial in other parts of the country affected by drought.

     

基于遥感数据的内蒙古近三十年植被指数的时空分析

利用时间序列分析方法,选取归一化植被覆盖指数为评价指标,对比分析内蒙古地区1982—2012年的荒漠化发展趋势,并对荒漠化问题的原因及解决方法进行了初步探讨。结果表明:植被指数(NDVI)与荒漠化水平有一定的关联,荒漠化程度越高,植被指数越低;从时间变化来看,年内植被指数满足单峰型变化,年均值变化有轻微下降趋势;从空间变化来看,植被指数具有明显的空间分异规律,表现出一定的地域性。通过降水、气温等自然因素以及人为活动等的相关分析,认为植被指数的变化规律与自然及人为活动都有一定的关联性。     

Dynamic monitoring of landscape patterns and ecological processes using HJ-1 and SPOT satellite data over Hulunbeier grassland,China

Landscape patterns and ecological processes have been in long-term research focus in the field of landscape ecology, but how to measure their quantitative relations is still open. This work chooses the Hulunbeier grassland as the study area where ecosystem shows high vulnerability, frequent evolvement of landscape patterns and ecological processes. With remote sensing technology, the relationships between landscape patterns and ecological processes were analyzed quantitatively from multi-scale, multi-temporal and time series perspective. Firstly, the information about the current situation and change of landscape patterns and ecological processes are obtained from HJ-1 (Environmental and Disaster Small Satellite) and LANDSAT TM (Thermal Mapper) data. Secondly, SPOT NDVI (Normalized Difference Vegetation Index) data during 2000–2008 are used to analyze the dynamic changes of ecological processes, and to simulate its inter-annual variety at pixel scale. Finally, the dynamic change trends of ecological processes of grassland vegetation are described. The results indicate that the unchanged ecosystem types account for most of the study area, unused land in the central part expands continuously which results in the increase of desertification, and most ecosystem types in the eastern part are changed to grassland and woodland. Furthermore, the vegetation vulnerability is the highest in the grassland-dominated region, the second in grassland–farmland–woodland transition, and the smallest in the woodland-dominated region, where the stability is enhanced in turn. Due to the dynamic change of vegetation, it can be concluded that the study area underwent ecological processes of vegetation cover with a negative trend and a changed phenology.     

Assessing desertification by using soil indices

Desertification generally refers to land degradation in arid, semiarid, and dry semi-humid climatic zones. It involves five principal processes: vegetation degradation, water erosion, wind erosion, salinization and waterlogging, and soil crusting and compaction. The aim of this study is assessing desertification using soil criteria. For this purpose, nine indices including sodium absorption ratio (SAR), soil gypsum percentage, soil texture, the content of HCO₃ ^?, the percentage of the organic matter, electrical conductivity (EC), pH, the content of the soil sodium, and chloride were used. The soil samples were taken in the north of Zayandeh-Rood River in Isfahan province of Iran, using soil data randomly sampled in a depth of 0–20 cm. After assessing the normality of the samples using Kolmogorov-Smirnov test, indices were imported into GIS environment and interpolated with IDW and normal and discrete kriging methods for delineating soil characteristics maps based on MEDALUS model. In this model, the data were firstly changed from 100 to 200. Thus 100 and 200 are estimated as the best and worst quality, respectively. Then the final map of soil criteria has been created by geometric mean of its indicators. The results showed that the maximum area is related to the medium class of desertification and is equal to 44,746 ha. The areas of severe and very severe classes of desertification are equal to 30,949 and 351 ha, respectively. The results also revealed that the indices of the organic matter and soil gypsum percentage are the most influential indices which affect desertification phenomenon.     

Evaluation of rainfall and wetland water area variability at Thirlmere Lakes using Landsat time-series data

Thirlmere Lakes is a group of five freshwater wetlands in the southwest fringe of Sydney, Australia, that is subject to cyclic wetting and drying. The lakes are surrounded by activities that have led to increasing pressure on the local surface and groundwater supply including farming and mining. The mine has been operating for more than 30 years, and in recent times, there has been speculation that the surface subsidence and underground pumping may have some impact on surface water and groundwater hydrology. A study was undertaken using satellite imagery to examine the relation between water area changes and rainfall variability. The study utilised Landsat time-series data during the period 1982–2014 to calculate changes in the lake water area (LA), through the normalised difference water index (NDWI) threshold. High classification accuracy was achieved using NDWI against high-resolution data that are available for the years 2008 (88.4 %), 2010 (92.8 %), and 2013 (96.9 %). The LA measurement was correlated against 11 historic observations that occurred in 2009, 2010, and 2011 during drier wetland conditions. Correlation analysis of the LA with the residual rainfall mass spread across the past 30 years has found that rainfall variability is a major dominant factor associated with the wetland changes. The underground mining operations, if verified by independent investigations, probably play a minor or negligible contributor to variations in total wetland area during the study period. This study has demonstrated that remote sensing is a technique that can be used to augment limited historic data.     

工业化背景下30年来中国东北地区自然资源时空变化及其生态环境效应

【研究目的】 基于1990—2018年间的土地利用空间分布数据、数字高程模型(Digital Elevation Model,DEM)、植被、气象等数据,对位于东北地区自然资源时空变化及其区域生态环境效应进行定量分析。【研究方法】 按照东北地区自然-地理-资源分布规律,通过定量计算与定性分析相结合的区划分析手段,将研究区划分为7个二级自然资源亚区和14个三级自然资源地区,分析其近30年来（1990—2018年）自然资源时空动态特征及生态环境效应。【研究结果】 结果表明：（1）近30年,研究区最主要的变化是森林、草地转变为耕地、水体与湿地,同时大量耕地被开发为建设用地,以及草地的荒漠化现象较突出。这与当地的老工业基地振兴政策、强农惠农政策等关系紧密。（2）研究区整体生态环境质量呈下降趋势,尤其是I1、I5、I6、I7二级亚区(I1三江平原温带耕地亚区、I5小兴安岭山地温带森林亚区、I6山前平原温带耕地亚区和I7松辽平原温带草耕亚区)。（3）森林、草地转变为耕地,大量面积的草地荒漠化等变化类型,对研究区生态环境质量恶化的生态贡献率较大。【结论】 近30年,研究区大量森林、草地转变为耕地和水体与湿地,大量耕地被开发为建设用地以及草地荒漠化加剧,其I1、I5、I6、I7二级亚区生态环境质量下降趋势较显著,森林、草地转变为耕地以及草地荒漠化等变化类型对此现象的生态贡献率较大。     

Assessment of land-use change and its impact on the environment using GIS techniques: a case of Kolkata Municipal Corporation,West Bengal,India

It is axiomatically true that urbanization in India's metropolises and large cities has been exacerbated since the beginning of the millennium, consuming the natural and semi-natural ecosystem on the outskirts of the city, resulting in a zone with a distinct climate known as urban climate. Such a climate—the result of a built-up environment is distinctly different from the natural climate as the paved surface and concrete skyscrapers not only destroy the natural ecosystem, it peculiarly induce a different kind of insolation, cooling and air drainage were lacking in green space, water bodies and open space cannot accommodate with environmental rhythm properly, resulting into the accumulation of heat, ecological derangement of subsurface soil which can easily be predicted by GIS analysis. This paper is an attempt to measure urban growth and its impact on the environment in the metropolitan city Kolkata. The use of satellite data and GIS techniques to detect urban expansion is a highly scientific strategy. Using geospatial techniques, the current study attempts to examine major urban changes in Kolkata and its surroundings from 1988 to 2021. Landsat 5 TM and Landsat 8 OLI temporal data are used to identify land-use change through unsupervised classification; Spectral Radiance Model and Split Window Algorithm method are used for identifying land surface temperature change. SRTM DEM (30 m) has been used to identify flood risk zones and several spectral indices like Normalized Difference Vegetation Index and Modified Normalized Difference Water Index are a further extension for environmental assessment. By all such suitable methods, a clearer change in an urban environment is detected within the period of 33 years (1988–2021). The result shows that the population changes, vegetation cover and built-up area, and accessibility are at a rapid rate. These changes are causing major environmental degradation in the city. The classification result indicates that appropriate land use planning and environmental monitoring are required for the long-term exploitation of these resources.