Landsat Enhanced Thematic Mapper Research Articles

Land Use-Land Cover Changes and Associated Drivers: A Case of Salooni Block of Chamba

Land Use-Land Cover (LULC) changes are the anthropogenic alteration of the natural landscape. The LULC changes have been generated by a variety of factors, including natural, demographic, and economic ones, which put pressure on the land and had a variety of environmental effects. The transformation in Landscape is affecting the livelihoods of people in numerous ways. Therefore, it is crucial to understand the patterns in land use in order to determine how land should be developed, managed, and planned in order to understand the way it will be utilised in the future. In the present study, Salooni Block of Chamba district, Himachal Pradesh was subjected to a first-ever attempt at land-use change detection for the years 2002 and 2022. Satellite imageries of Landsat-7 Enhanced Thematic Mapper (ETM+) Landsat-8 Operational Land Imager (OLI) were used for landuse maps of 2002 and 2022 respectively. The classification was completed using a supervised classification technique and the USGS Land use Classification Scheme at Level I classes, including Agricultural Land, Barren Land, Built-up, Forest, Pasture Land, Snow Cover and Waterbody. Furthermore, fifty people aged 50 and above were interviewed to understand the local factors responsible for LU/LC change. The result indicates a rise in the forest cover, which now accounts for 48.2% of the total area. Since 2002, there has been a 2.3-fold rise in built-up land, which is a glaring indication of the pressure on land. The total area of agricultural and pasture land is decreasing, while the percentage of barren land has increased by 2%. To preserve the sustainability and food security of the ecologically vulnerable environment, stringent land use planning needs to be enforced in the mountainous region. The results of the LU/LC dynamics discussed above can be used to establish policies and strategies for restoring and enhancing the sustainability of the environment as well as the carrying capacity of the land.

Analysis of methods for assessing the degree of water pollution

The most important modern methods for the monitoring of water bodies, their state of pollution and the assessment of water quality are considered. Commonly accepted, standard-ized methods for assessing water quality in water bodies include ground-based sampling, which is untimely and expensive under certain conditions. Best suited for practical use in large areas is the remote approach, which combines ground and satellite measurements to classify changes and detect pollution in closed water bodies. Satellite monitoring is a reliable tool for obtaining operational information on the state of water bodies and their changes with a certain periodicity, thanks to the use of a large set of heterogeneous data. And the availabil-ity of archived data becomes a significant advantage, opening up the possibility of comparing the results obtained and analyzing the factors influencing the state of water bodies and the environment. The paper describes the main methods and datasets that can be used for the ap-plication of remote monitoring methods based on machine learning. The use of Landsat mul-tispectral sensors, including TM (Thematic Mapper), MSS (Multispectral Scanner), ETM (Enhanced Thematic Mapper), and OLI (Operational Land Imager), is proposed as the main data set. Examples of the application of the remote sensing approach to the assessment of wa-ter pollution over large areas will be analyzed. Possible machine learning methods for solv-ing an applied problem are presented.

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.

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.

Analysis of Water Yield Changes in the Johor River Basin, Peninsular Malaysia Using Remote Sensing Satellite Imagery

Changes in land-use–land-cover (LULC) affect the water balance of a region by influencing the water yield (WY) along with variations in rainfall and evapotranspiration (ET). Remote sensing satellite imagery offers a comprehensive spatiotemporal distribution of LULC to analyse changes in WY over a large area. Hence, this study mapped and analyse successive changes in LULC and WY between 2000 and 2015 in the Johor River Basin (JRB) by specifically comparing satellite-based and in-situ-derived WY and characterising changes in WY in relation to LULC change magnitudes within watersheds. The WY was calculated using the water balance equation, which determines the WY from the equilibrium of precipitation minus ET. The precipitation and ET information were derived from the Tropical Rainfall Measuring Mission (TRMM) and moderate-resolution imaging spectroradiometer (MODIS) satellite data, respectively. The LULC maps were extracted from Landsat-Enhanced Thematic Mapper Plus (ETM+) and Landsat Operational Land Imager (OLI). The results demonstrate a good agreement between satellite-based derived quantities and in situ measurements, with an average bias of ±20.04 mm and ±43 mm for precipitation and ET, respectively. LULC changes between 2000 and 2015 indicated an increase in agriculture land other than oil palm to 11.07%, reduction in forest to 32.15%, increase in oil palm to 11.88%, and increase in urban land to 9.82%, resulting in an increase of 15.76% WY. The finding can serve as a critical initiative for satellite-based WY and LULC changes to achieve targets 6.1 and 6.2 of the United Nations Sustainable Development Goal (UNSDG) 6.

The use of remote sensing technology in defining the water depth in the lakes and water bodies: Western Iraq as a case study

The study's primary purpose is to explore an appropriate way of monitoring and assessing water depths using the satellite remote sensing technique of the Al Habbaniyah Lake in Iraq. This research studied the experience-conditions (thresholds) of different bands for multi-temporal satellite image data with different satellite image sensors (Landsat 5-TM, and EO1-ALI) for the same region, to recognize regions of water depths. The threshold values are taken that to separate the Al Habbaniyah Lake to the required depths (shallow, deep, and very deep), as a supervised method. A three-dimension feature space plot had used to represent these regions. The relationship of the mean values of the three separated water regions with all TM and ALI bands is studied. Other lakes in Iraq were used to actualize the validity and accuracy of this technique to find the water depth regions; Al Qadisiyha and Al Mosul lakes are in the West and North of Iraq of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-5 Thematic Mapper (TM) satellite images respectively. This technique succeeded in determining Al Habbaniyah lake depths but failed to recognize the regions of water depths for some of the lakes which have the big depths by correct form such as; Al Qadisiyha and Al Mosul lakes.

Assessing the Impacts of Land Use/Land Cover Changes on Water Resources of the Nile River Basin, Ethiopia

Land use/land cover change and climate change have diverse impacts on the water resources of river basins. This study investigated the trends of climate change and land use/land cover change in the Nile River Basin. The climate trends were analyzed using the Mann–Kendall test, Sen’s slope estimator test and an innovative trend analysis method. Land use/land cover (LULC) change was examined using Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper (ETM+) with a resolution of 30 m during 2012–2022. The findings revealed that forestland and shrub land area decreased by 5.18 and 2.39%, respectively. On the other hand, area of grassland, cropland, settlements and water bodies increased by 1.56, 6.18, 0.05 and 0.11%, respectively. A significant increasing trend in precipitation was observed at the Gondar (Z = 1.69) and Motta (Z = 0.93) stations. However, the trend was decreasing at the Adet (Z = −0.32), Dangla (Z = −0.37) and Bahir Dar stations. The trend in temperature increased at all stations. The significant changes in land use/land cover may be caused by human-induced activities in the basin.

Investigation and Prediction of the Land Use/Land Cover (LU/LC) and Land Surface Temperature (LST) Changes for Mashhad City in Iran during 1990–2030

Studies on how cities are affected by urban heat islands (UHI) are critical nowadays for a better understanding of the connected effects and for providing helpful insights for sustainable city development planning. In this study, Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper+ (ETM+), and Landsat-8 Operational Land Imager (OLI) images were used to assess the dynamics of the spatiotemporal pattern of land use/land cover (LU/LC) and land surface temperature (LST) in the metropolitan city of Mashhad, Iran in the period between 1990 and 2019. The Markov chain model (MCM) was used to predict LU/LC and LST for 2030. In the analyzed LU/LC maps, three LU/LC classes were distinguished, including built-up land (BUL), vegetated land (VL), and bare land (BL) using the maximum likelihood (ML) classification method. The collected data showed different variations in the geographical pattern of Mashhad LST during the research period that impacted the LST in this metropolis. The study evaluated the variations in LU/LC classes and evaluated their impact on the LST. The value of the LST was positively correlated with the occurrence of the built-up land (BUL), and with the bare land areas, while it was negatively correlated with the occurrence of the VL areas. The analysis of changes observed over three decades with 10-year intervals and the prediction of the LU/LC and LST for 2030 constitute an important contribution to the delineation of the dynamics of long LU/LC and LST records. These innovative results may have an important impact on policymaking fostering environmental sustainability, such as the control and management of urban expansion of Mashhad in connection with UHI.

Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data

Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 109 m3 from July 2002 to December 2022, with a growth rate of 5.3766 × 108 m3/a. The growth rate from January 2005 to January 2015 is 4.4850 × 108 m3/a, and from January 2015 to December 2022, the growth rate is 8.9206 × 108 m3/a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years.

Exploratory Mapping of Blue Ice Regions in Antarctica Using Very High-Resolution Satellite Remote Sensing Data

Mapping spatiotemporal changes in the distribution of blue ice regions (BIRs) in Antarctica requires repeated, precise, and high-resolution baseline maps of the blue ice extent. This study demonstrated the design and application of a newly-developed semi-automatic method to map BIRs in the Antarctic environment using very high-resolution (VHR) WorldView-2 (WV-2) satellite images. We discussed the potential of VHR satellite data for the mapping of BIRs in the Antarctic environment using a customized normalized-difference blue-ice index (NDBI) method devised using yellow, green, and near-infrared spectral bands of WV-2 data. We compared the viability of the newly developed customized NDBI approach against state-of-the-art target detection (TD), spectral processing (SP) and pixel-wise supervised (PSC) feature extraction (FE) approaches. Four semi-automatic FE approaches (three existing plus one newly developed) consisting of 16 standalone FE methods (12 existing + four customized) were evaluated using an extensive quantitative and comparative assessment for mapping BIRs in the vicinity of Schirmacher Oasis, on the continental Antarctic coastline. The results suggested that the customized NDBI approach gave a superior performance and the highest statistical stability when compared with existing FE techniques. The customized NDBI generally rendered the lowest level of misclassification (average RMSE = 654.48 ± 58.26 m2), followed by TD (average RMSE = 987.81 ± 55.05 m2), SP (average RMSE = 1327.09 ± 127.83 m2) and PSC (average RMSE = 2259.43 ± 115.36 m2) for mapping BIRs. Our results indicated that the use of the customized NDBI approach can greatly improve the semi-automatic mapping of BIRs in the Antarctic environment. This study presents the first refined map of distribution of BIRs around the Schirmacher Oasis. The total area of blue ice in the study area was estimated to be 106.875 km2, approximately 61% of the study area. The WV-2 derived BIR map area presented in this study locally refined the existing BIR map derived using Landsat Enhanced Thematic Mapper Plus (ETM+) and the Moderate Resolution Imaging Spectroradiometer (MODIS)-based mosaic of Antarctica (MOA) dataset by ~31% (~33.40 km2). Finally, we discussed the practical challenges and future directions in mapping BIRs across Antarctica.

An Assessment of the Effects of Flood on Agricultural Production in Auyo Local Government Area of Jigawa State, Nigeria

Within the past decades, the impact of climate conditions is evident on crop productions across the Nigeria’s different regions. Between the years 2021 and 2022 Nigeria witnessed one of its worst floods in the last decade as hundreds of villages and urban centres were submerged in waters, displacing over 2.4 million people. These events had negative impacts on crop yield. Crop yield is the product of both growth and development. Thus, this study attempted to investigate the effects of flood on agricultural production in Auyo Local Government Areas of Jigawa State, Nigeria spanning a period of 30 years (1993-2022), with the aimed of conducting a land-use and land-cover change of the study area; analyze the temporal trends of rainfall in the area of study; examining the yields of agricultural production in the area and to establish the relationship between agricultural yields and flood occurrence in the Auyo Local Government Area. Thus, both primary and secondary sources of data were employed in this study. In view of this, Landsat 5 Thematic Mapper (TM) of 2002 and Landsat Enhanced Thematic Mapper Plus (ETM+) data of 2022 where used. In order to examine the trend in rainfall and agricultural yields series, 5-year running mean and linear trend lines was calculated and plotted using Microsoft Excel Statistical Tool. The rainfall and agricultural yields series were also sub-divided into 5-year non-overlapping sub-periods and Cramer’s test was then used to compare the means of the sub-periods (5-years) with the mean of the whole record period (30 years). The relationship between the flood and agricultural yields were tested using R statistical analysis. The study revealed that built-up increased from 14% in 2002 to 19% in 2022. Bare lands occupied about 50km2 of the total area examined in 2002, but decreased to about 44km2 in 2022. In addition, the water body decreased from about 172km2 in 2002 to 155km2 in 2022. Besides, the positive nature of relationships between rice yields and flood occurrences indicated a significant degree of relationship at 95% confidence level. In view of this, it was recommended among other measures, the need for the development of a Response Farming Technique and longitudinal approach for comprehensive measures to address the shock and destruction that comes with re-occurrence of flood.

Assessment and Mapping of Land Cover/Use Aided by Remote Sensing and GIS Techniques in Elnour Natural Forest, Blue Nile State, Sudan

The main aim of this study was to assess and mapping of land cover (LC) / land use (LU), using Remote Sensing (RS) and Geographical Information System (GIS) techniques in Elnor natural forest reserve, Blue Nile state, Sudan. The study also attempted to update some information in the study area such as LC and contour map by using different methods of data transformation and analysis such as Normalized Different Vegetation Index (NDVI), interpolation and supervised classification. Thematic Mapper (TM), Landsat Enhanced Thematic Mapper (ETM+) and Landsat8 images were used in LCLU changes and supported by field observation. The study area was affected by cutting the mother trees and overgrazing. Closed forests during 2000, 2010 and 2019 decreased from 36.9%, 28.5% and 18.7% respectively. Moreover, the total area of bare soil increased by about 14.4%, 47.2% and 39.7% in 2000, 2010 and 2019 respectively. The open forest (grassland) decreased from 48.7% in (2000) to 41.7% in (2019). A Digital land cover map was produced using LCCS software; depending on the fieldwork data, they were found in four Classes. Mixed Woodland Single Layer, Semi-Deciduous Shrub land with Open Herbaceous, Open Grassland, Single XII Layer and Hardpans. The study revealed that there are some indicators of land degradation during the last 20 years (2000, 2010 and 2019) and is expected to continue increasing unless serious measures are carried out to reduce it.

Geospatial Analysis of the Spatio -Temporal Growth of Kwali Area Council, Federal Capital Territory, Abuja

This study analyzed the spatio-temporal growth of Kwali Area Council in Federal Capital Territory Abuja, Nigeria from 1998 to 2017. The study used Landsat Thematic (TM) of 1997, Landsat Enhanced Thematic Mapper Plus (ETM+) of 2007 and Landsat 8 Operational Land Imager (OLI) of 2017 all with a spatial resolution of 30 meters. Those data sets were accessed from United States Geological Survey (USGS) website Supervised Classification-Maximum Likelihood Algorithm was used to establish the land use-land cover situations for 1997, 2007 and 2017; subsequently the extent and rate of the urban growth of the study area between 1998 and 2017 were determined. The findings revealed that urban landuse covered 265.9 hectares in 1998, 304 hectares in 2007 and 1100.3 hectares in 2017. The urban landuse increased by 38.2 hectares from 1998-2007 which represent 14.3% growth in 9 years and corresponds with 4.24 hectares increase per year. The aforementioned growth rate was the lowest recorded in the period (1998-2017) of study as 2007-2017 showed that the urban landuse increased by 796.1 hectares which represent 261.8% growth in 10 years, this gives a corresponding growth rate of 79.61 hectares per year. The study recommends that there is the need for ecosystems restoration for areas that have suffered terrible degradation especially bare lands and local ingenuity, indigenous technology and know-how, and local community-centered efforts should be made and supported to restore the environment. That Government should as a matter of urgency embark on landuse /land cover mapping in the region in order to facilitate accurate base map production of Kwali Area Council as well as digitize and update all analogue maps of the area of study for effective planning for all physical developments. Land use is a term indicating the use to which a portion of land is put. Land is used for all of man‟s economic activities, and so the use of a portion of land determines the economic productivity of the land. Therefore, it is necessary to regulate and coordinate land use for optimum productivity and benefit of all people whose livelihood depends on the land.

Potential of Multispectral Satellite Data for Superficial Iron Oxide Detection in Sulaimaniyah, Iraqi Kurdistan Region

This study primarily investigates the total (Fe) iron presence in Sulaimaniyah Governorate, the Iraqi Kurdistan Region (IKR), which has an abundance of iron mines. Spatial quantification and frequent monitoring of mineral existence in the soil are essential in the mining regions. To achieve this goal, a remote sensing technique was utilized to predict soil minerals, particularly iron, in the study area using a multispectral satellite image, Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A robust methodology was perceived and developed from image processing to estimate and map iron oxide-rich soils, and the soil’s spectral indices were obtained after algorithms were applied in processing the bands of the Landsat image. Soil samples were collected and analyzed in the laboratory to determine the chemical, physical, and mineralogical characteristics of soils. Correlation coefficients were carried out between soil properties and spectral band values retrieved from image analysis to examine the band potentials of Landsat. The statistical results showed that there was a significant relationship between the 3rd band of the ETM+ image and each of the total iron (R2 = 0.643), the free iron oxide (R2 = 0.659), and sand particles (R2 = 0.561). The predicted soil mineral maps were generated for the study area to visualize the study site's soil characterization and total iron spread. This study's results could help primarily identify the spatial distribution of some soil properties in Sulaimaniyah, Iraq.

Assessment of Urban Sprawl, Land Use and Land Cover Changes in Voi Town, Kenya Using Remote Sensing and Landscape Metrics


 Rapid and uncontrolled urbanization is a major issue in both developing and developed countries. Uncontrolled urbanization has resulted to unplanned expansion of residential and commercial areas, informal settlements, housing shortages, and unplanned land use. Understanding and quantifying urban sprawl spatiotemporal patterns is critical for informing the development of appropriate policies for effective and sustainable land use management. Using image classification and spatial metrics, this study examines the changes in Voi town's urban land use/land cover (LULC) between 1999 and 2019. The LULC was mapped using Landsat Thematic Mapper (TM):Landsat Enhanced Thematic Mapper Plus (ETM+):and Landsat Operational Land Imager (OLI) datasets using supervised maximum likelihood classification. A post classification approach was used to detect and assess LULC changes in the study area, while selected spatial metric indices quantified urban sprawl. The results of the change detection analysis revealed that Voi town has been rapidly expanding, with an urban expansion of 187.96 percent from 1999 to 2011, 183.40 percent from 2011 to 2019, and 716.1 percent from 1999 to 2019. In 1999, the built-up area comprised 1.29 percent of the total study area, 3.72 percent in 2011, and 10.53 percent in 2019. Based on spatial metrics analysis, the number of built-up area patches in 1999, 2011, and 2019 was 154, 278, and 526, respectively. An increase in the number of patches indicated fragmentation and the emergence of new built-up areas. 
 As a result, city planners will need to plan ahead of time and implement additional measures to deal with the city's future rapid and unprecedented growth.
 

Assessment of Land Degradation in Dambatta Local Government Area Using Remote Sensing Techniques

Kano State is one of the frontline states in northern Nigeria that have suffered land degradation. This degradation results from urbanization and anthropogenic influence. In Danbatta Local Government Area (LGA), persistent degradation of the land has adversely affected the environment and the economy and there is lack of studies on land degradation in the area. Therefore, this study assessed land degradation in Dambatta Local Government Area (LGA) of Kano State paying special attention to the causes and effects of the reduction in the lands’ actual or potential uses. The images used for the research analysis were obtained from National Space Research and Development Agency (NASRDA), Abuja. The data sets were captured by Landsat Multispectral Scanner/Thematic Mapper (MSS/TM) 1997, Landsat Enhanced Thematic Mapper Plus (ETM+) 2007 and Operational Land Imager (OLI) 2017. Post classification change detection technique was conducted in ILWIS 5.2, and later converted to shape files where it was imported to Arc Map 10.2 GIS software. The results showed Normalized Difference Vegetation Index (NDVI) ranges from −0.056 to 0.18 in 1997, −0.07 to 0.11 in 2007 and −0.128 to 0.217 in 2017. This depicts that there has been a progressive loss in vegetation cover in Dambatta LGA over a period of 20 years with corresponding acceleration in bare lands and developed areas. The Land Surface Temperature (LST) results generally show a continuous and constant increase in surface temperature from the developed and urban areas to the undeveloped and rural areas. The LST results also show that no area under consideration in the study area experienced an extreme temperature (≥44˚C) during the period of study. In 2017, a large part of the study area fell within the higher temperature zones (≥40˚C) and other areas fell into mid-temperature zones (35˚C - 40˚C). This high surface temperature resulted from increase in bare land, high insolation, urbanization, anthropogenic influences and large distribution of imperviousness or geographical relief of the area. It was suggested that afforestation and sustainable development should be encouraged and strengthened in the area. In addition, studies should be carried out on the appropriate solution to the land degradation problem in Dambatta LGA, Kano State. Furthermore, Local Government Areas neighboring Dambatta LGA should also be analyzed on the issue of land degradation.

Retrieval of Tidal Flat Elevation Based on Remotely Sensed Moisture Approach

Due to challenging conditions of field survey techniques, it is difficult to measure the topography of tidal flats, an important parameter to understanding the evolution and dynamics of the constantly changing zone. This study used remotely sensed sediment moisture estimates to retrieve tidal flat elevation. The method is based on the observation that the intertidal zone is gradually exposed from land to sea at low tide, meaning that higher elevations contain less moisture. Here, we investigate the nature of the relationship between reflectance and moisture content from Landsat Enhanced Thematic Mapper Plus images and the study areas as a proxy for mapping the elevation of an exposed tidal flat surface. Statistical analysis confirmed a negative correlation between moisture and elevation; however, the correlation coefficient was relatively weak, and the slope of the intersecting tidal creek was found to be a crucial factor affecting this relationship. After segmenting the slope to correspond to areas of tidal flat and nontidal flat surfaces, the correlation coefficient of the moisture and elevation increased significantly. A retrieval model was then developed to generate the tidal flat elevations of different slope grades. After verification, the retrieval accuracy of the model was up to 17.3 cm. This research study demonstrated that the remotely sensed moisture method is suitable for monitoring the surface elevation of tidal flats.

Augmenting Landsat time series with Harmonized Landsat Sentinel-2 data products: Assessment of spectral correspondence

An increase in the temporal revisit of satellite data is often sought to increase the likelihood of obtaining cloud- and shadow-free observations as well as to improve mapping of rapidly- or seasonally-changing features. Currently, as a tandem, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and −8 Operational Land Imager (OLI) provide an acquisition opportunity on an 8-day revisit interval. Sentinel-2A and -2B MultiSpectral Instrument (MSI), with a wider swath, have a 5-day revisit interval at the equator. Due to robust pre- and post-launch cross-calibration, it has been possible for NASA to produce the Harmonized Landsat Sentinel-2 (HLS) data product from Landsat-8 OLI and Sentinel-2 MSI: L30 and S30, respectively. Knowledge of the agreement of HLS outputs (especially S30) with historic Landsat surface reflectance products will inform the ability to integrate historic time-series information with new and more frequent measures as delivered by HLS. In this research, we control for acquisition date and data source to cross-compare the HLS data (L30, S30) with established Landsat-8 OLI surface-reflectance measures as delivered by the USGS (hereafter BAP, Best Available Pixel). S30 and L30 were found to have high agreement (R = 0.87–0.96) for spectral channels and an r = 0.99 for Normalized Burn Ratio (NBR) with low relative root-mean-square difference values (1.7%–3.3%). Agreement between L30 and BAP was lower, with R values ranging from 0.85 to 0.92 for spectral channels and R = 0.94 for NBR. S30 and BAP had the lowest agreement, with R values ranging from 0.71 to 0.85 for spectral channels and r = 0.90 for NBR. Comparisons indicated a stronger agreement at latitudes above 55° N. Some dependency between spectral agreement and land cover was found, with stronger correspondence for non-vegetated cover types. The level of agreement between S30 and BAP reported herein would enable integration of HLS outputs with historic Landsat data. The resulting increased temporal frequency of data allows for improvements to current cloud screening practices and increases data density and the likelihood of temporal proximity to target date for pixel compositing approaches. Furthermore, additional within-year observations will enable change products with a higher temporal fidelity and allow for the incorporation of phenological trends into land cover classification algorithms.

Monitoring Land Use and Land Cover Change of Forest Ecosystems of Shendurney Wildlife Sanctuary, Western Ghats, India

Understanding forest degradation due to human and natural phenomena is crucial to conserving and managing remnant forest resources. However, forest ecosystem assessment over a large and remote area is usually complex and arduous. The present study on land use and land cover change detection of the Shendurney Wildlife Sanctuary forest ecosystems was carried out to utilize the potential application of remote sensing (RS) and geographic information system (GIS). Moreover, to understand the trend in the forest ecosystem changes. The supervised classification with Maximum Likelihood Algorithm and change detection comparison approach was employed to study the land use and land cover changes, using the Landsat Enhanced Thematic Mapper (ETM±) and Landsat 8 OLI-TIRS using data captured on July 01, 2001, and January 14, 2018. The study indicated the rigorous land cover changes. It showed a significant increase in the proportion of degraded forest with negligible gain in the proportion of evergreen forest from 21.31% in 2001 to 22.97% in 2018. A substantial loss was also observed in moist deciduous from 27.11 % in 2001 to 17.23 % in 2018. The result of the current study indicated the degree of impacts on forests from the various activities of their surroundings. This study provides baseline information for planning and sustainable management decisions.

Temporal dynamics change of land use/land cover in Jhansi district of Uttar Pradesh over past 20 years using LANDSAT TM, ETM+ and OLI sensors

Mapping and monitoring land use land cover (LULC) dynamics is vital for sustainable land development, planning and management. Using advanced techniques of remote sensing (RS) and geographic information system (GIS), the present study is an effort to monitor the changes in LULC patterns of Jhansi District of Uttar Pradesh for the periods 2000–2010 and 2010–2020. Satellite images from Landsat-5 Thematic Mapper (TM) for 2001; Landsat-7 Enhanced Thematic Mapper (ETM+) for 2010 and Landsat 8 (OLI) for 2020 were used to extract Land use/Land cover classes. Supervised classification using Maximum Likelihood Classifier (MLC) was applied to monitor LULC of the study area. Six major LULC classes viz; cropland, fallow/barren land, built-up area, water bodies and forest have been identified and showed that major land use in the district is crop land. The accuracy assessment of the classified map was done using 256 random points distributed all over the image. Results indicated that LULC in Jhansi district has undergone a series of changes over the past two decades. Results showed that crop land, built-up area and water bodies have increased by 27.16% (1367.26 Km2), 0.58% (29.4 Km2) and 0.3% (15.26 Km2), respectively while fallow/barren land and forest have decreased by27.55% (1386.58 Km2), and 0.5% (25.34 Km2), respectively. The research revealed that substantial shift from fallow/barren land (27.55%) into crop land. The analysis and findings of this study facilitate researchers, policy makers, land use planners and managers to implement the appropriate land-use management plan for Jhansi district.