Landsat TM Research Articles

Multiscale spatiotemporal dynamics analysis of urban green space: Implications for green space planning in the rapid urbanizing Hefei City, China

Under the pressure of rapid urbanization, the spatiotemporal dynamics of urban green spaces (UGS) have enormous impacts on the local ecological system and environment at different scales. In this study, UGS in Hefei City, which has experienced rapid urbanization from 1995 to 2015, were extracted based on time-series Landsat-5 TM and Landsat-8 OLI images, and different types of parks were mapped based on GF-2 images combined with multi-source metadata. Dynamic patterns of green space were examined by drawing the spatial variations of green space at the city, inner city, and park scales. Results revealed that: (1) At the city level, UGS decreased with the rapid urbanization, especially farmland sharply lost in areas around existing urban areas and along the transportation corridors. (2) At the inner-city level, concentric analyses showed that UGS changes in different rings had unique trends, and the dynamic changes were the most significant in Rings 2 and 3. Comparative analysis of old and new districts showed that the common characteristics of UGS changes were the transfer-out of farmland and the transfer-in of built-up land, and the newer the region, the more intense the changes. (3) At the park level, the number and area of urban parks were increasing from the center to the periphery, and the type of park gradually changed from single to rich. Significant spatial heterogeneity was identified in the landscape patterns of urban parks based on fishnet cell analysis. It is fundamental to assign urban construction land for socio-economic development, while planning UGS at different scales; moreover, integrating different green space-related policies could protect the UGS and maintain its stability. Only in this way can UGS combine economic, social, and ecological benefits under the background of rapid urbanization.

Improving remote sensing estimation of Secchi disk depth for global lakes and reservoirs using machine learning methods

ABSTRACT Secchi disk depth (SDD) is a simple but particularly important indicator for characterizing the overall water quality status and assessing the long-term dynamics of water quality for diverse global waters. For this reason, countless efforts have been made to collect SDD data from the field and through remote sensing systems. Many empirical and semianalytical algorithms have been proposed to estimate SDD from different satellite images for a specific or regional water. However, the construction of a robust global SDD estimation model is still challenging due to the nonlinear response of SDD to optical properties and the complex physical and biogeochemical processes of different waters. Therefore, machine learning methods to better interpret nonlinear processes were used to improve remote sensing estimations of SDD for global lakes and reservoirs based on a global matchup dataset from Landsat TM (N = 4099), ETM+ (N = 2420), and OLI (N = 1249) covering in situ SDD from 0.01 m to over 18 m. Overall, extreme gradient boosting (XGBoost) and random forest (RF) had better SDD retrievals than back propagation neural network, support vector regression, empirical and quasi-analytical models showing high precision with mean relative error of approximately 30% and good agreements with the long-term in situ SDD in different waters with various optical properties. Our results can support long-term global-level water quality evaluation and thus making informed decisions about development policy.

Probabilistic Cellular Automata modelling and simulation of land-use changes in Okomu National Park

Land use, land cover (LULC) change technique is essential for measuring ecological quality, environmental sustainability, and uncontrolled development at various spatiotemporal scales. To construct effective land use management plans, the probable future scenario of LULC changes can be easily detected utilizing a simulation technique.This study monitors and models spatiotemporal land-use changes in Okomu National Park over two decades (2000 – 2020) to project forest cover changes for the near future. A probabilistic cellular automata (CA) model was created and used to simulate land-use changes with the aim of predicting future land-use scenarios. Landsat7 ETM+ satellite images for years 2000, 2005, 2010, 2015, and 2020 were classified into Forest and Non–Forest using a maximum likelihood supervised classification algorithm. A probabilistic cellular automata model using Moore’s neighborhood with a Von Neumann extension was used to simulate land-use changes for years 2005, 2010, 2015, and 2020 with the year 2000 as the base year.The overall classification accuracy for the years under study was 98.18%, 97.52%, 96.33%, 91.67%, and 94.61%with overall kappa coefficients of 0.97, 0.96, 0.95, 0.86, and 0.91 respectively. State transition probabilities for 2000–2005, 2005–2010, 2010–2015, and 2015–2020 were calculated from the classified images. Simulation accuracywas 77.46%, 74.1%, 70.98%, and 78.27% for the year 2005, 2010, 2015, and 2020 respectively. Projections were made for years 2025 and 2030 and it shows a 27.41% decline from the base year by 2025 anda 29.90% decline by 2030.The amount of forest cover in the actual and simulated land-use changes shows a gradual drop from 185.15 km2 in the base year 2000 to 136.07 and 135.30 km2 in the year 2020, respectively.Spatial simulation models, which provide a scientific basis for supporting sustainable forest management based on different simulation scenarios alsocontribute significantly to the implementation framework for the United Nations' Reducing Emissions from Deforestation and Forest Degradation (REDD/REDD+) program, as well as reference scenarios for REDD/REDD+ incentive payments.

Assessment of tropical cyclone-induced shoreline and riverbank changes at the Rufiji Delta using satellite remote sensing methods

The study aimed at quantifying changes in shoreline and riverbanks caused by tropical cyclones (TCs) and associated rainfall in the Rufiji Delta, southern Tanzania. Long term changes of the shoreline and riverbanks were analysed using medium resolution (Landsat TM and ETM) satellite imagery (1991, 1997 and 2007), while short-term changes (2013 to 2014) were analysed using high resolution (Pleiades) satellite imagery. Delineation of the shoreline and riverbank changes were accomplished through the analysis of appropriate coloured image composites, Sobel filtering and maximum likelihood classification of land cover. Analysis of Landsat data showed a relatively higher magnitude of erosion between 1991 and 2007, followed by minor changes between 1997 and 2007. Simbauranga was the most severely eroding site, with an estimated magnitude of erosion of 83 to 100 m during the study period. The maximum magnitude of short-term changes of the riverbanks were estimated at about 31 m2. Apart from the erosion of the riverbanks, other changes were the conversion of water to vegetation covered areas (amounting to approximately 200 m2). Short-term shoreline changes were up to 206 m with higher magnitude of accretion (142 m) than erosion (-4 m). The study conclusively calls for further detailed research on shoreline and riverbank changes based on the impacts of TCs on land cover.

FOREST COVER CHANGE DETECTION IN PAKTIA PROVINCE OF AFGHANISTAN USING REMOTE SENSING AND GIS: 1998-2018

Monitoring changes in forest cover is important to address issues like biodiversity conservation, sustainable management, and climate change. The study has been conducted in the Paktia province of Afghanistan to assess the changes in different forest classes which occurred together with political changes by using Remote Sensing and Geographic Information System (GIS). The change was analyzed for a period of two decades, i.e., 1998 to 2018. Landsat TM and OLI satellite images of 30m resolution for the years 1998 and 2018 were used respectively. The overall classification accuracy of the mapping was estimated as 82.67% and the kappa coefficient was estimated as 0.8081. The study area was delineated via visual image interpretation technique into 11 LULC classes’ viz., closed forest, open forest, forest scrub, grassland other classes (Agriculture, Agroforestry, horticulture, habitation, waterbody, wasteland, and snow). The comparison of maps 1998 and 2018 revealed that the total area under closed forest, open forest, showed an increase of 0.43%, 0.73%, respectively. While the areas under forest scrub, showed a decline of 0.30%, during the study period (1998-2018).

An Improved QAA-Based Method for Monitoring Water Clarity of Honghu Lake Using Landsat TM, ETM+ and OLI Data

Secchi disk depth (ZSD) is used to quantify water clarity as an important water-quality parameter, and one of the most used mechanistic models for ZSD is the quasi-analytical algorithm (QAA), of which the latest version is QAA_v6. There are two models in QAA for clear and turbid waters (referred to as QAA_clear and QAA_turbid). QAA_v6 switches between the two models by setting a threshold value for the remote sensing reflectance (Rrs, sr−1) at the selected reference band of 656 nm. However, some researchers found that this reference band or the threshold value does not apply to many turbid inland lakes. In Honghu Lake, the Rrs (656) (Rrs at 656 nm) in the whole lake is less than 0.0015 sr−1; therefore, only QAA_turbid can be applied. Moreover, we found that QAA_clear resulted in overestimation while QAA_turbid resulted in significant underestimations. The waters of inland lakes usually continuously vary between clear and turbid water. We proposed a hypothesis that QAA_turbid and QAA_clear transition evenly, rather than being distinguished by one threshold value, and we developed a model that combined QAA_clear and QAA_turbid according to our assumption. This model simulated the process of continuous change in water clarity. The results showed that our model had a better performance with an RMSE that reduced from 0.5 to 0.28, an MAE that reduced from 0.43 to 0.21, and bias that reduced from −0.4 to −0.05 m compared with QAA_v6. We applied QAA_Honghu to Landsat TM, ETM+, and OLI data and obtained 205 ZSD maps with high spatial resolution in Honghu Lake. The results were consistent with the existing in situ measurements. From 1987–2020, the ZSD results of Honghu Lake showed an overall downward trend and a distinct seasonal pattern.

Integrated Landsat multispectral imagery and airborne geophysical data for enhanced litho-structural mapping: A case study of Adamawa Massif, north-eastern Nigeria

Landsat-7 ETM + satellite imagery integrated with corrected airborne geophysical data (radiometric, gravity, and aeromagnetic) anomalies maps, and field investigations were used to discriminate lithologies and related structures in Mayo-Belwa and Jada-Ganye-Toungo areas (Adamawa Massif), northeastern Nigeria. Field investigations portray the granite-gneisses as the oldest rock units, and have sequentially been invaded by the syn-, late-to post-collisional Older granites. Subsurface linear structures in the area display dominant NE-SW, NW-SE, NNE-SSW, and ENE-WSW trends, reflective of variations in pre-existing structural orientations, and near-surface expressions of reactivated zones of weakness in the basement crust. The geodynamic systems revealed that most elongated lineaments are expressed as axial part of valleys trending NE-SW, ENE and NNW, forming diagonal grid to the NW-SE stresses. Abnormally high total radioactivity gravitates to areas of deflection (alluvium), and late- and post-tectonic granites. The gravity intensity provides a spectrum of anomalous responses including abnormally high positive gravity (volcanic rocks), moderately high positive gravity (granite-gneisses and late-to post-tectonic and syn-tectonic granites), background gravity and low negative gravity (granite-gneisses, alluvium and proluvium). Aeromagnetic anomalies discriminate the Older granites (intense positive anomalies), granite-gneisses, sediments (moderate positive, near background, and moderate-intensive negative anomalies), syn-tectonic granites (moderate positive anomalies), banded-gneiss and amphibolite (intensive negative anomalies), volcanic rocks (intense negative anomaly), and alluvium/proluvium (low positive anomalies). This present study led to the development of up-to-date and accurate litho-structural and geophysical maps of the geologically-rugged and hardly accessible Adamawa Massif, north-eastern Nigeria and further constrain the geochemical (High-K calc-alkaline, peraluminous) and tectonic (syn-to post-collisional) characteristics of typical Pan-African granitoids.

Utilizing multi-temporal thermal data to assess environmental land degradation impacts: example from Suez Canal region, Egypt

Land surface temperature (LST) analysis of satellite data is critical for studying the environmental land degradation impacts. However, challenges arise to correlate the LST and field data due to the constant development in land use and land cover (LULC). This study aims to monitor, analyze, assess, and map the environmental land degradation impacts utilizing image processing and GIS tools of satellite data and fieldwork. Two thermal and optical sets of Landsat TM + 5 and TIRS + 8 data dated 1984 and 2018 were used to map the thermal and LULC changes in the Suez Canal region (SCR). The LULC classification was categorized into water bodies, urban areas, vegetation, baren areas, wetland, clay, and salt. LULC and LST change detection results revealed that vegetation and urban areas increased in their areas in 34 years. Moreover, 97% of the SCR witnessed LST rise during this period with an average rise rate of 0.352 °C per year. The most effective LULC class changes on LST were the conversions from or to baren areas, where baren areas were converted to 630.5 km2 vegetation and 104 km2 urban areas rising the LST to 43.57 °C and 45 °C, respectively. The spectral reflectance (LSR), LST profiles, and statistical analyses examined the association between LST and LULC deriving factors. In combination with field observations, five hotspots were chosen to detect and monitor natural and human land degradation impacts on LST of the SCR environment. Land degradations detected include water pollution, groundwater rising, salinity increase, sand dune migration, and seismic activity.

Is aquaculture development responsible for mangrove conversion in India? - A geospatial study to assess the influence of natural and anthropogenic factors on mangroves in the last three decades

Aquaculture was cited as the primary factor for the reduction of mangrove area worldwide. But, there is no data available on the extent of conversion of mangroves for aquaculture in India, the second largest country in global aquaculture production. The present study assessed the factors of changes in mangroves area between the pre aquaculture period and recent years. Landsat TM images of 1988, Sentinel 2 A images of 2018, geospatial analysis, ground truth verification, post-classification approach, and accuracy assessment were used for the assessment. Comparing the extent of mangroves area in 1988 with 2018, the gain and loss was 278% and 30% in Kerala, 859% and 19% in Karnataka, 204% and 18% in Maharashtra, and 4100 and 20% in Daman and Diu respectively. Mangrove extent has increased due to restoration activities, while lost due to coastal erosion and other developmental activities. Combining mangrove statistics drawn from this study with available data from earlier study, indicate that the aquaculture has converted 2052 ha of mangroves, which represents 0.66% of India's mangrove area. The extent of mangroves increased by 20.72% in India between 1988 and 2018. There is a possibility that part of the mangrove - converted lands are privately owned. Besides, Coastal Aquaculture Authority Act of 2005 prohibits the use of mangrove lands to aquaculture. The present study proves that shrimp aquaculture is not accountable for the large-scale changes in the mangrove lands of India, except in Andhra Pradesh State. However, it demands the land use policy to protect the mangrove lands in private owned lands by allocating alternate resource space, and monitoring periodically.

Pemetaan dan analisis tingkat kerusakan hutan mangrove di Taman Nasional Kutai berdasarkan data satelit landsat ETM dan kerapatan vegetasi

Along with the increase in population in the Kutai National Park (Kutai NP) has raised the land issue of due to the need for living space and the exploitation of natural resources. This study is part of an effort to evaluate the condition of mangrove forests as a first step effort to protect mangrove forests as critical habitat for coastal Kutai NP. The research was conducted in March to April 2012, with the goal of evaluating and measuring vegetation cover and land use (Land use) mangrove forests in coastal Kutai NP. For this purpose, the data is used Landsat ETM + image with 7 bands that recorded in 2008. Process analysis using NDVI (Normalizet Difference Vegetation Index) composite channel by IR (bands 4 and 3), while the extent of the damage was analyzed using standard from Decree of Ministry of environment No. 201, 2004. Base on the research, Kutai National Park has the potential of ± 5192.54 ha of mangroves, mangrove forest ecological conditions in Kutai NP generally be in the grade is still good with vegetation density ranging between 967-1567 ind / ha, Kutai NP In some areas such as the Estuary Sangatta, bay and estuary Sangkima Lombok are areas that have experienced enough land conversion, overall mangrove forest that has been opened by ± 1845.85 ha or 26.2%. Mangrove forests are located close to residential vulnerable to land-use changes.

Estimating soil available water capacity within a Mediterranean vineyard watershed using satellite imagery and crop model inversion

Soil available water capacity (SAWC) is a key factor to be considered when assessing soil capability to provide ecosystem services. The current study deepens the use of remotely sensed data for mapping SAWC and its components from crop model inversion. The inversion was conducted using the STICS (Simulateur mulTIdiscplinaire pour les Cultures Standard) crop model along with the GLUE (Generalized Likelihood Uncertainty Estimation) algorithm on a panel of 14 sites within a rainfed vineyard catchment located in Southern France. Several constraint variables derived from Landsat7 ETM + satellite imagery (leaf area index - LAI - and evapotranspiration - ET) or in-situ measurements (surface soil moisture - SSM), were used in the inversion process alone or in combination.Three main outcomes could be reported when comparing retrievals of both SAWC and its components against field estimates. First, retrievals were significantly correlated with ground estimates for some SAWC components and some scenarios of constraint variables, although overall retrieving performances were quite poor. Second, poor retrieving performances for two scenarios of constraint variables were related to few sites for which specific processes were disregarded by the modelling framework, namely allochthonous water supply and waterlogging during wet autumn and summer. Third, we could identify some promising combinations of constraint variables, after the removal of the aforementioned sites with specific processes. These promising combinations were (LAI, ET) and even more (LAI, ET, SSM) for estimating SAWC and root zone thickness, as well as SSM for estimating soil moistures at field capacity and wilting point of the topsoil layer. Provided we can avoid site-specific processes, our approach may further provide spatial sampling of SAWC and related components, to be used as surrogate input data for DSM models.

Mineralogical Studies and Radioactivity on Stream Sediments Around Wadi Aidib Area, South Eastern Desert, Egypt

Wadi Aidib lie at southern part of the Eastern Desert of Egypt, between 22° 15’ - 22° 21’ N and Longitudes 36° 20’ - 36° 30’ E, covering an area of about 30km2. It is covered by basement rocks dissected by several wadies, Wadi Aidib as a main wadi with several tributaries. Constant of channel maintenance showed the dissection of the studied basin by lineaments, fractures and joints, dissection is minimized in wadi Aidib. Landsat ETM+ false color composite image (bands 3, 2, 1) The rock types wide spread out at Wadi Aidib area contains metavolcanics, alkali feldspar granites, quartz diorites, biotite granites metasediments. Heavy liquid separation and magnetic separation techniques in addition to microscopic examination for the studied stream sediment samples were applied. The mineral resource tonnage for each concerned economic mineral 171.315 ilmenite; 93.150, magnetite; 25.110, zircon; 9.720, total rutile; 7.695, garnet; 1.215, monazite and 810.000 green silicates, in thousands of tons. The studied stream sediments are measured radiometrically using gamma-ray spectrometer for determination the radionuclides contents. U concentration varies between 1 and 2 ppm with 1.69 as an average, Th from 3 to 7 ppm with an average 4.92 ppm.

Impact of Al-Ahdeb Oil Field on the Surrounding Environment Using Remote Sensing and GIS Techniques, Wasit Governorate, Iraq

Remote sensing and Geographical Information System (GIS) are used in this study to detect environmental changes in the vicinity of Al-Ahdeb oil field, southwest of Kut City, Wasit Governorate. Different image indices, such as Normalized Differences Vegetation Index (NDVI), Normalized Differences Soil Index (NDSoI), and Normalized Differences Salinity Index (NDSI) are used. Two LANDSAT images with acquired data of September 2007 and 2016 are used to detect the environmental changes and to detect the effect of Al-Ahdeb oil field before and after construction and industrial operation. The results of change detection show there is a high decrease in the vegetation cover during the year 2016 compared with 2007, where the area of vegetation cover has decreased from (165.85 Km 2 ) in 2007 to (119.62 Km 2 ) in 2016. The change detection results from NDSI show that the saline soil in (2016) is higher than those in (2007). The NDSI derived from Landsat TM (2007) image confirms that there is significant increase of salinity in the study area, where the calculated area of the salinity in 2007 is (5.65 Km 2 ) while in 2016 it is (21.951 Km 2 ). Change detection, using NDSoI, show that the land in the study area is going toward desertification and soil degradation. The decrease in the vegetation cover, which in turn led to soil erosion in addition to water shortage and the pollution by the waste of the oil field, could be the main reasons of the desertification in the area. Keywords: Environmental change detection; Image Indices; NDVI; NDSI; NDSoI; Al Ahdeb oil field. DOI: 10.7176/JEES/12-8-03 Publication date: August 31 st 2022

Changing nature of Land surface temperature and transformation of vegetation cover and water bodies in the 2nd largest urban agglomeration of West Bengal, Eastern India

Land surface temperature (LST), vegetation and water are very crucial for maintaining a healthy urban ecosystem. Asansol is one of the fast growing urban industrial agglomerations of West Bengal in Eastern India. In the last few decades the city has experienced a rapid urban industrial expansion along with the population growth which is associated with the continuous increase of LST. Considering this the present study has been designed to explain the nature of LST changes and transformation of land cover over the last thirty years utilizing the Landsat imageries in GIS platforms. In this study changing nature of land surface temperature, vegetation cover and surface water bodies and their interrelationship over the last thirty years have been analysed. Landsat 5TM images of May and October for the year 1990, 2000 and 2010 and Landsat 8 OLI images of May and October for 2020 have been utilized in GIS platform. LST and different indices NDVI, MNDWI and NDBI have been extracted for analyzing the nature of change along with image classification. The results of the study depict that LST is increasing continuously. In the first decade (1990–2000) LST increased at 0.24 °C/year followed by an increasing rate of 0.33 °C/year in the second decade (2000–2010) and 0.37 °C/year in the last decade (2010–2020). Same trend is also perceived for built up area, where it increased at 0.96 km2/year, 1.71 km2/year, and 2.49 km2/year in corresponding decades. On the other hand, the vegetation cover is continuously decreasing (1.59 km2/year, 0.75 km2/year and 0.49 km2/year in consecutive decades) as well as the surface water bodies (0.62 km2/year in, 0.41 km2/year, and 0.25 km2/year in corresponding three decades) over the last thirty years. Correlation and regression analysis of land surface temperature and various indices shows positive relation between LST and NDBI, negative relation between LST and MNDWI, and also between LST and NDVI. Urban-industrial expansion is the important factor for increasing temperature and transforming vegetation and surface water bodies.

Quantification of the effect of host patch configuration on the abundance of Bemisia tabaci in central Argentina: a multimodel inference approach.

Bemisia tabaci is a complex of species, which is considered the most common and important pest of a wide range of crops belonging to many different botanical families. In Argentina, this species is recognized as a vector of geminiviruses, and Middle East-Asia Minor 1, Mediterranean, New World and New World 2 have been found to coexist in the same area. Landscape elements, like habitat patch area and isolation, define the habitat configuration and have a direct effect on insect populations between and within host patches. In this paper, we analyse the effect of potato patch configuration on the distribution and abundance of B. tabaci. Potato patches were identified using Landsat TM5 and TM7 images, and a supervised classification was performed to quantify the spatial distribution of the patches in the whole study area. Potato patch metrics were estimated using Fragstats 4.4. Generalized linear mixed models were employed to analyse the relationship between whiteflies and landscape configuration, through a multimodel inference approach, finding that B. tabaci abundance and landscape metrics were very variable. After a multimodel selection process, we found that perimeter-to-area ratio and Euclidean distance between patches were the variables that best explained whitefly abundance in potato patches. Implications of these findings are discussed.

Satellite-Derived Trends in Inundation Frequency Reveal the Fate of Saltmarshes

Some of the world’s coastal saltmarshes experience loss in area due to environmental changes, such as relative sea level rise and limited sediment supply. We use satellite remote sensing to assess changes in inundation (flooding) frequency in tidal basins and investigate the bio-physical interactions with saltmarshes. We apply a simple automated method to retrieve time series of inundation frequency change and seaward habitat change of saltmarshes and tidal flats from Landsat-5 TM satellite imagery between 1985 and 2011, for a number of contrasting tidal basins (estuaries, deltas) globally. We evaluated the satellite-derived information on inundation frequency with such information obtained from elevation and tide gauge data for the Western Scheldt estuary, showing good agreement. Application of the method on all study sites reveal which tidal basins are stable or net emerging and which tidal basins are net drowning, but also show large spatial variation in the changes in inundation frequency within each basin. Tidal basins experiencing an overall significant increase in inundation frequency (Mississippi Delta and Venice Lagoon) were associated with an overall loss of saltmarsh area. Satellite-derived temporal and spatial information on inundation frequency helps to assess the fate of saltmarshes in light of sea level change, changes in sediment supply and subsidence.

Vegetation Coverage Monitoring Model Design Based on Deep Learning

Based on the medium-resolution Landsat TM and OLI satellite images in the study area, the deep learning ENVINet-5 model is adopted for vegetation coverage monitoring. By referring to the fusion image and Google Earth high-resolution satellite image, the training samples and verification samples are manually labeled, and the labels of four types of ground objects (desert, water body, cultivated land, and construction land) are made. Through the ENVI deep learning binary classification model, the labeled training samples are trained, and a large number of samples of desert, water, and cultivated land are extracted and transformed into corresponding label images. Then, a large number of training sample labels extracted from the model are combined with the manually made construction land sample labels and both of them are used as the training samples of the ENVI deep learning multiclassification model. According to the classification process of the deep learning model (creating label image, initializing training model, and training model and model classification), through the adjustment of various parameters, the four types of ground objects in the study area are finally classified. Finally, the classification results that meet the accuracy requirements are statistically analyzed. It is proved that the model classification results can meet the use requirements.

Time-series analysis of landcover dynamics and their relation with coastline migration along Kuakata coast, Bangladesh using remote sensing techniques

ABSTRACT Time-series analysis of satellite imageries is useful in studying changes in coastlines and the nature of the landcover dynamics in coastal environments. This study aimed to investigate the relationship between coastal erosion-accretion and landcover changes in Kuakata. Landsat TM and Landsat OLI/TIRS satellite imageries at a nearly 5-year interval between the years 1989 and 2020 were used to compare changes within five major landcover classes in the study area – 1) mangrove vegetation, 2) settlements, 3) agricultural land, 4) waterbody and 5) beach. Net land loss over the past 31 years was estimated to be 1.73 km2 within the 93.05 km2 study area. Linear regression rates were calculated to identify the area most prone to erosion. The average erosion rate along the coastline was estimated to be 2.09 m/year. Most erosion occurred along the western part of the coast while the highest accretion was limited to an area in the east. Study findings also suggested that changes in beach and mangrove vegetation classes have a significant spatial and statistical correlation with coastal erosion-accretion processes. These findings can help the policymakers implement coastal zonation, and preventive and rehabilitative measures to save the tourism industry and agriculture in Kuakata.

Study of Fluctuations in Surface Area of Lake Haramaya using NDWI and MNDWI Methods

Lakes are of great value to human beings and important for various reasons like regulating the flow of river water, to maintain the eco-system and storage of water during the dry seasons. Lake Haramaya, which is situated at 14 km Northwest of Harer town (UNESCO Site) is one of the famous and beautiful lakes of Ethiopia. It acts as a source of life for human beings and animals. The over exploitation of the lake haramaya for water supply and agricultural purposes has led to its extinction in the last two decades. This study attempts to identify the fluctuations in surface area of Lake Haramaya between 1995 to 2020 by using multi-temporal satellite data. The Landsat 5TM images of 1995, 2000 and 2010, Landsat 7ETM+ image of 2005 and Landsat 8 OLI TIRS images of 2015 and 2020 are analyzed using the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) methods. These methods are used to quantify the changes in surface area and compared to each other for identifying the suitable method for detecting water bodies. The present study shows that, between 1995 to 2010 the lake lost up to 2.3238 sq.km and almost dried. But, during 2011 to 2020 the lake surface area increased by 2.6946 sq.km. The study states that the lake surface area is fluctuating and MNDWI method is highly reliable in extracting water bodies.

A SPATIO-TEMPORAL STUDY OF LAND USE LAND COVER ANALYSIS IN MANIYARI BASIN USING GIS AND REMOTE SENSING TECHNIQUES

Land is the only valuable resource of the earth which separate the earth other planet. Land is the base of life, the changing pattern of uses of land has been seen over time from the Stone Age to modern time. On that way the present study area of Maniyari basin has also been noticed remarkable changes. Digital change detection methods used to identifying changes related with land use and land cover features using multi temporal remote sensing data. The objectives of the paper analyse the changes of land uses over time as well as the factor which is responsible for the transformation of the land. LANDSAT ETM+ for the time frame of 2010 and Sentinal 2 data for the time frame of 2020 has been used for this present study. Total area of the study region approximately 3700 sq. km. LULC Mapping has consisted of basically four steps: Data acquisition, Data processing, interpretation/classification, post verification through ground truth. NRSC LULC Classification schema 2019 has been used for Land use Land cover mapping. Agriculture Cropland. LULC classes which has been identified in this present study are Barren Rocky, Built up, Rural Built up, Urban, Canal, Dense scrub, Forest, Forest Plantation, Gullied/Ravines, Industrial/Mining, Lake/Pond, Open Scrub, Reservoir/Tank, River/Stream, Water logged. It has been found that 145 ha. Open scrub and 98 ha forest land converted in to agricultural land.70 ha. of outer reservoir land converted in to agricultural land. Significant changes in the outer forest ecosystem have occurred in the study region, and it must be reconstructed for tribal community who were relying heavily on natural resources.