Maximum Likelihood Classification Algorithm Research Articles

Estimation of Rice Crop Acreage in Kuttanad Region, Kerala Using Landsat 8 OLI IMAGES and GIS Techniques

Aims: The study aimed to delineate rice accurately (Oryza sativa L.) cultivation areas in the Kuttanad region, Kerala, during the Puncha season of 2023-24 using medium- to high-resolution optical satellite data, particularly Landsat 8 Operational Land Imager (OLI), to aid in preharvest prediction of agricultural production and policy making. Study Design: This study used a remote sensing-based approach for rice area estimation, focusing on supervised classification methods. Place and Duration of Study: The study was conducted in Kuttanad, Kerala, a low-lying agroecosystem, during the Puncha season of 2023-24. Methodology: The study utilised two cloud-free Landsat 8 OLI images to delineate rice-growing areas. The images were pre-processed, mosaicked, and analysed using ArcGIS software. A supervised classification approach was employed using the Maximum Likelihood Classification algorithm. The study area was classified into five categories: rice fields, other crops, low vegetation, built-up areas, and water bodies. Ground-truth data was used to validate the classification accuracy. Results: The total rice area delineated during the Puncha season was 43,550.28 hectares. The classification achieved an accuracy of 93.33%, with a kappa coefficient of 0.87, indicating high reliability. Conclusion: The accurate delineation of rice-growing areas using satellite imagery provides valuable information for assessing production levels and planning for food security. This methodology can aid in agricultural planning and contingency strategies, particularly in regions like Kuttanad, which face challenges such as flooding and soil toxicity.

A GIRS-based analysis of urban green space losses with land-use changes and its relationship with surface urban heat island in the city of Tabriz

This research investigates the relationship between urbanization, land-use land-cover (LULC) changes, and surface urban heat islands (SUHIs) in Tabriz, Iran. Using Landsat satellite data from 2000 to 2021, the study analyzed changes in urban green space (UGS) and their impact on SUHIs. The maximum likelihood classification algorithm was employed to assess LULC changes, while the perceptron neural network model and cellular automaton Markov chain model were used to simulate and predict future LULC dynamics. Various indices, including the UGS Change Intensity Index (CII), Land Use Dynamic Degree Index (LUDD), and UGS Land Index, were used to quantify urban expansion, green space changes, and per capita green space (PCG) availability. The findings revealed a significant increase in built-up area and a subsequent decline in green space. While green space initially increased, it later decreased, leading to a reduction in both UGS area and PCG. Surprisingly, the analysis of heat islands associated with green space and built-up areas showed that built-up areas had a more pronounced mitigating effect on SUHIs compared to green space. Consequently, the green space heat island effect intensified, while the built-up area heat island effect decreased over time.

Assessment of land cover degradation due to mining activities using remote sensing and digital photogrammetry

Appropriate environment management requires an understanding of how mining activity alters environmental characteristics and how those changes affect an area. Therefore, to reduce the adverse effects of mining activity on the land, it becomes crucial to have relevant information about responses to environmental degradation. This study aims to assess the impact of semi-mechanised and artisanal mining activities on the land cover using remote sensing data and photogrammetric analysis, in the Mbale locality, Northern Cameroon. For this purpose, the maximum likelihood classification algorithm of the supervised classification method combined with field surveys was used to map environmental changes, based on Sentinel-2 images of 2019, 2021, and 2023. Normalized Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Brithness index (BI), and Soil crust index (SCI), were calculated to assess changes in vegetation, bare soil, water body, and exploited area. The orthophoto obtained from photogrammetric processing was performed to outline river network change through visual interpretation techniques and to calculate the volume of pits created by mining. The result of classified images indicated that vegetation cover decreased by 11.74% over the studied years. However, bare soil and exploited areas increased by 9.2% and 5.4% respectively. The calculated spectral indices show that between 2019 and 2023 the locality of Mbale considerably lost its vegetation cover, in favor of bare soil. The color of the soil and the granulometric size of the topsoil have also changed. The photogrammetry analysis highlighted the deviation of the main river and estimated the volume of pits created by mining activity to 22188.7 m3. The mining activities caused a loss of the vegetation cover, generated big pits, and multiple deviations of the Lom River from its natural course, which have a substantial negative influence on the ecosystem. Such data can be used for long-term environmental management, reclamation and rehabilitation monitoring, and mining area restoration.

Using Nighttime Light Data to Explore the Extent of Power Outages in the Florida Panhandle after 2018 Hurricane Michael

The destructive forces of tropical cyclones can have significant impacts on the land, contributing to degradation through various mechanisms such as erosion, debris, loss of vegetation, and widespread damage to infrastructure. Storm surge and flooding can wash away buildings and other structures, deposit debris and sediments, and contaminate freshwater resources, making them unsuitable for both human use and agriculture. High winds and flooding often damage electrical disubstations and transformers, leading to disruptions in electricity supply. Restoration can take days or even weeks, depending on the extent of the damage and the resources available. In the meantime, communities affected by power outages may experience difficulties accessing essential services and maintaining communication. In this study, we used a weighted maximum likelihood classification algorithm to reclassify NOAA’s National Geodetic Survey Emergency Response Imagery scenes into debris, sand, water, trees, and roofs to assess the extent of the damage around Mexico Beach, Florida, following the 2018 Hurricane Michael. NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) was processed to estimate power outage duration and rate of restoration in the Florida Panhandle based on the 7-day moving averages. Percent loss of electrical service at a neighborhood level was estimated using the 2013–2017 American Community Survey block group data. Spatial lag models were employed to examine the association between restoration rates and socioeconomic factors. The analysis revealed notable differences in power-restoration rates between urbanized and rural areas and between disadvantaged and more affluent communities. The findings indicated that block groups with higher proportions of minorities, multi-family housing units, rural locations, and households receiving public assistance experienced slower restoration of power compared to urban and more affluent neighborhoods. These results underscore the importance of integrating socioeconomic factors into disaster preparedness and recovery-planning efforts, emphasizing the need for targeted interventions to mitigate disparities in recovery times following natural disasters.

LAND USE MAPPING USING MAXIMUM LIKELIHOOD CLASSIFICATION AND REMOTE SENSING INDICES: CASE STUDY AIN-ABID CONSTANTINE (ALGERIA)

Land use is an essential theme in monitoring environmental phenomena. The supervised maximum likelihood classification algorithm has been shown to provide the best results from remotely sensed data. This work is aimed at the application of a supervised classification (maximum likelihood) based on a priori knowledge of the terrain under study and information extracted from the two remote sensing indices NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-up Index) for mapping land use of the area of Ain Abid Constantine (located in eastern Algeria) for the year 2020. The obtained result showed that this city is an agricultural area with a percentage of 68.49% of agricultural land and a low percentage of 2.02% of Buildings.

Land use-land cover dynamics in Boyo wetland using remote sensing and GIS for the period 1992–2022

ABSTRACT Land use changes are one of the main factors contributing to the degradation of wetland ecosystems. The objective of this study was to describe land use and land cover (LULC) change in the Boyo Wetland from 1992–2022. We employed remote sensing and GIS to obtain data for this study. As the main data source, Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager (OLI) images were used. We downloaded Landsat images from the USGS website that were cloud-free. A supervised maximum likelihood classifier algorithm was used for the classification process. Five LULCs are identified, which include farmland, built-up areas, bare land, grazing land, and wetland. For the duration of the research period, the rate of increase in the percent of farmland, built-up, and barren land was observed to be 18.24%, 9.48%, and 4.52%, respectively, while a decreasing pattern was observed in grazing land and wetland by − 19.44% and − 12.79%, respectively. The expansion of agriculture and built-up areas owing to population growth are the primary factors influencing change in the study area. Therefore, a rapid protective measure should be taken to rehabilitate the study wetland.

Assessing forest cover changes in Dak Lak province (Central Highlands of Vietnam) from multi-temporal Landsat data and machine learning techniques

Dak Lak is a province in the Central Highlands region of Vietnam, with a large area of forests and forestry land. However, the forest cover has changed dramatically in recent times due to the influence of human activities and climate change. This article presents the results of assessing forest cover changes in Dak Lak province from Landsat satellite image data for the period 2000 – 2020. Three Landsat satellite image scenes, including Landsat 5 TM images taken in March 2000 and February 2010 and Landsat 8 OLI image taken in February 2020 are used to classify forest cover. Three common machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART) and the traditional maximum likelihood classification algorithm are used to classify forest cover in the study area, thereby choosing the method with the highest accuracy. The results show that the RF algorithm has the highest accuracy in classifying forest cover from multi-temporal Landsat images by comparing the overall accuracy value and the Kappa coefficient. The obtained results are used to build forest cover change maps in the period 2000 - 2010, 2010 - 2020 and 2000 - 2020. The results received in the study provide information to help managers in monitoring and protecting forest resources.

Benthic Habitat Classification Using Sentinel-2a Image With And Without Water Column Correction In Pengudang Village, Bintan Regency

Research on the mapping of benthic habitats in the waters of Pengudang Village, Bintan Regency, uses Sentinel-2A imagery. This mapping process has the potential to display the results of classification of benthic habitats in shallow waters. The purpose of this study was to assess the Maximum Likelihood Classification (MLH) algorithm model for mapping benthic habitat cover through Sentinel-2A satellite imagery using the lyzenga water column correction and without water column correction and to calculate the accuracy of the MLH classification results for benthic habitats in waters Pengudang Village. This classification uses a pixel-based image classification which is grouped into a cover class for each pixel. This classification groups into 4 benthic habitat classes, namely dead coral sand (KMP), sea grass (LM), live coral sand (PKH) and sand (PS). The results showed that the application of the lyzenga correction using the MLH algorithm in mapping benthic habitats resulted in a significant accuracy rate (80,00%), while the application without lyzenga correction using the MLH algorithm resulted in an accuracy rate (76,00%). These findings support the use of the lyzenga correction method which can increase the level of accuracy by (4,00%) with the MLH algorithm as a guide in better mapping of benthic habitat cover in Pengudang Village.

High-Accuracy Mapping of Soil Parent Material Types in Hilly Areas at the County Scale Using Machine Learning Algorithms

Conventional maps of soil parent material (SPM) types obtained by field survey and manual mapping or predictions from other map data have limited accuracy. Digital soil mapping of SPM types necessitates accurate acquisition of SPM distribution information, which is still a challenge in hilly areas. This study developed a high-accuracy method for SPM identification in hilly areas at the county scale. Based on geographic information system technology, seven feature variables were extracted from the geological map, geomorphic map, digital elevation model, and remote sensing image data of Shanggao County, Jiangxi Province, China. Different feature combination schemes were designed to develop SPM identification models based on random forest (RF), support vector machine (SVM), and maximum likelihood classification (MLC) algorithms. The best SPM identification results were obtained from the RF algorithm using the combination of geological type, geomorphic type, elevation, and slope. Confusion matrices were constructed based on a field survey of 586 validation samples, and the results were evaluated in terms of overall accuracy, precision, recall, F1 score, and Kappa coefficient. The overall accuracy and Kappa coefficient of the results from the optimal RF model were 83.11% and 0.79, respectively, which were 26.11% and 0.31 higher than those of the conventional map, respectively. Its precision and recall for various SPM types were greater than 75%. A comprehensive comparison of the accuracy, uncertainty, and plotting performance of the SPM recognition results reveals that the RF algorithm outperforms the SVM algorithm and the MLC algorithm. Geological type was the largest contributor to SPM identification, followed by geomorphic type, elevation, and slope. The importance of different feature variables varied for distinct SPM types. The accuracy of SPM identification was not improved by selecting more feature variables, such as land use type, normalised difference vegetation index, and topographic wetness index. This study demonstrates the feasibility of high-accuracy county-level SPM mapping in hilly areas based on the RF algorithm using geological type, geomorphic type, elevation, and slope as feature variables. As hilly areas have typical topographic features and SPM types, the proposed method of SPM mapping can be useful for application in other similar areas. There are a few limitations in this study with regard to data quality and resolution, feature variable selection, classification algorithm generalisation, and study area representativeness, which may affect the outcomes and need to be solved.

The Trend of Peri-Urban Land Use Change in Small and Medium Sized Urban Areas. A Case of Kimilili Town, Bungoma County, Kenya

The purpose of this study was to determine patterns in land use change in Kimilili peri-urban areas between 1990 and 2020. We got Landsat data for the years 1990, 2000, 2010, and 2020 from the USGS website. Arc GIS version 10 and ERDAS Imagine version 2018 were used for digital image processing and GIS analysis. Imageries for the following land use groups were obtained using supervised classification using the Maximum Likelihood classification algorithm: built-up area, agricultural land, dense vegetation, and bare terrain. Ground truthing was done in order to get the required reference information. An accuracy assessment was conducted using Google maps at various periods of the year for the LULC maps from 1990, 2000, 2010, and 2020. Change detection analysis was used to carry out land use conversions between the research intervals. The land use types were categorized based on their respective areas, measured in hectares. Over a 30-year period, a notable 16.6% growth in built-up area was seen overall. Additional findings revealed a 16.7% decrease in agricultural land. According to the study, there were notable changes in land usage between 1990 and 2020. Enforcing management standards is necessary in order to maintain control over urban expansion.

LANDSAT 9 IMAGERY BASED MANGROVE FOREST MAPPING USING MAXIMUM LIKELIHOOD CLASSIFIER AND SUPPORT VECTOR MACHINE ALGORITHM

The successful launch of Landsat 9 in 2021 emphasizes the commitment to the continuity of the Landsat mission to provide global medium resolution satellite records. The benefit of the availability of the Landsat dataset is the extraction of information related to mangrove forest cover through mapping, especially in Gili Lawang. The main challenge in mangrove mapping is selecting a classification method that provides the most accurate results. This article aims to explore the use of the Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) algorithms in the context of mangrove mapping using Landsat 9 imagery on Gili Lawang. The two algorithms show differences in detecting the area of mangrove, non-mangrove and water bodies, respectively for SVM 371.89 ha, 58.11 ha, 3.73 ha; while the MLC algorithm is 328.88 ha, 98.41 ha, and 6.45 ha. The MLC algorithm identifies non-mangrove objects at the outer boundary of Gili Lawang, while SVM identifies the same area as mangrove objects due to the influence of the training area created and low separability. The SVM algorithm has better accuracy with a Kappa statistic of 0.85 compared to MLC with a value of 0.73.
 
 Keywords: Gili Lawang, Landsat 9, Mangrove, Support Vector Machine, Maximum Likelihood Classifier

BUILT-UP AREA DYNAMICS IN “PRE- AND DURING BOKO HARAM CONFLICTS” IN MONGUNO LOCAL GOVERNMENT AREA OF BORNO STATE, NIGERIA

Abstract. The Boko Haram insurgency has had a significant impact on the Monguno Local Government Area (LGA) in Borno State, Nigeria, for more than ten years. This study analysed how the Boko Haram insurgency affected the built-up areas of Monguno LGA. The focus of this study was to map, analyse, and detect the spectral and spatial changes of the earth's surface using remotely sensed images and geospatial techniques, focusing in particular on the built-up areas in the study area, in order to provide sufficient information on the status of built-up areas for effective planning and good governance. Employing a combination of the pixel-based Supervised Maximum Likelihood classification algorithm and the Object Based Image analysis, the study used Landsat 7 ETM+ satellite imageries for the years 2004 and 2007 and Landsat 8 OLT/TIRS imageries for the years 2014 and 2021 to determine the rate of change in the built-up areas over a period of seventeen (17) years (from 2004 to 2021). The classified Land Use and Land Cover (LULC) maps were grouped into four classes: water body, built-up areas, vegetation, and bare land, even though the study was more concerned about the changes in the built-up area. The results showed that from 2004, the built-up area occupied 0.12% with a total land area of 2.00km2 and increased in 2007 by 0.21%. From 2007 to 2014 the built-up area was seen to have increased by 0.31% with a built-up area of 6.00km2. Similarly, there was an increase in the built-up area from 0.31% in 2014 to 0.63% in 2021. Generally, the built-up area has increased by 0.51% from 2004–2021 and the largest percentage increase was noticed from 2014 to 2021 where there was an increase of 0.31% in the built-up area. This increase signifies that there has been an inflow of people into Monguno from neighbouring LGA. It was recommended that future research should incorporate other parameters such as population, literacy level, and socioeconomic well-being of the people.

Assessment of herbaceous vegetation classification using orthophotos produced from the image acquired with unmanned aerial systems

Arguably the most popular remote-sensing products are classified images. However, there are no definitive procedures to assess classification accuracy that simultaneously consider resources available and field efforts. The explosive usage of unmanned aerial systems (UAS) in land surveys adds new challenges to classification assessment, as orthorectified images usually contain significant artifacts. This study aims to identify the optimal ratio between training and validation sample size within a supervised classification approach applied to UAS orthophotos. As a case study, we used a wetland area west of Portland, OR, USA, treated with various glyphosate formulations to control Phalaris arundinacea, commonly known as reed canary grass. A completely randomized design with five replications and six glyphosate formulations was used to assess P. arundinacea vigor following repeated herbicide applications. The change in P. arundinacea vitality was monitored with high-resolution four-band imagery acquired with a SlantRange 3PX camera installed on a DJI Matrice 210. The orthophotos created from images were produced with Pix4D, which was subsequently preprocessed with ERDAS Imagine 2020 to reduce the noise, shadows, and artifacts. All images were classified with the maximum likelihood classification algorithm. Simple random and stratified random sampling methods were applied to collect training and validation samples, evaluating eight ratios of training to validation samples to assess their classification accuracy. We found that increasing the training-to-validation sample size ratio enhances accuracy, with the 3:1 ratio being the most reliable in classifying P. arundinacea vigor. Our study provides evidence that image preprocessing and enhancement are essential for UAS-based imagery.

Large-scale stresses, decay of River Jamuna and resilience strategies in the Anthropocene

In the recent decades, many worlds' large rivers have been exceptionally modified by human interference. The principal objective of this study is to investigate the causes and consequences of decay of river Jamuna (distributary of river Bhagirathi-Hooghly). The entire investigation was conducted by the data of historical maps like Rennel's map of Bengal, Hunter's map, digital version of US army map, landsat satellite imageries (1980-MSS, 1990-TM, 2000-TM, 2010-ETM+ and 2020-OLI) and fluvio-hydrological study. Maximum likelihood classification algorithm was used to delineate the land use and land cover classes and Kappa based accuracy assessment indices were also used for the validation. There is no scientific work on this historically important river and afterward, millions of people are also underprivileged from this river ecosystem services due to severe degeneration. In this milieu, it is a genuine research gap. Novelty of the work is exposed the authentic causes of river decay and anthropogenic stressors. Result showed that the average width of river Jamuna has been significantly decreased whereas maximum average width of the channel was recorded in the year 1956 (319.81 m) and the present channel is flowing comparatively within narrow path (69.27 m). Rate of siltation (average rate of siltation 0.23 m/year) on channel bed has been increased due to impacts of channelization. Channel bed, natural levee and proximity floodplain have been encroached by large scale human intervention. LULC patterns showed that the urban built-up area is rapidly amplified from 2000 (22.26%) to 2020 (31.50%) whereas, agricultural and swampy land turned into built up area. Off-take of Jamuna is completely detached from the parent river Bhagirathi-Hooghly in the early 1956 and gradual anthropogenic interferences within river corridor accelerate river decaying processes. River restoration is highly required for sustainable river ecosystem health management and environmental development. Some management strategies have been recommended such as reconnecting of river Jamuna with Bhagirathi-Hooghly, dredging, decommissioning of culvert and sluice gate, cleaning of fishing pool and water hyacinth and sustainable land-use planning.

Automatic Monitoring of Water Pollution from Sentinal-2 Satellite Imagery A Case Study of Mediterranean Coast, Egypt

Water is the most important source among the natural sources on the earth's surface for the health of marine and near-shore ecosystems. Assessing water pollution in coastal areas is an essential process for sustainable development. El Gharbia coast, Egypt is one of the most important coasts of Egypt. The main objective of this work is to predict the future changes of water pollution in this coast using Sentinel-2 satellite imagery of three consequent times. First, three Sentinel-2 satellite imagery of consequent dates were acquired and processed for further classification process. The maximum likelihood classification algorithm was then used to prepare the base maps for: time 1, time 2 and time 3, with ten major classes (pollutants). The classified images of time 1 and time 2 were then used to predict the time 3 water pollution map using a Markov Chain Model. After that, the final predicted water pollution map for time 3 was validated with the classified one of the same time. Finally, and compared with the water pollution map of time 3, the future ratios of all types of pollutants have been predicted. The results showed that the proposed model can simulate water pollution changes with reliable results. Based on the simulated water pollution map and by 2030, the ratios of all pollutants will increase. Accordingly, El Gharbia coast and surrounding activities can be saved from more pollution in the future.

Spatio-Temporal Variability Analysis of Vegetation Dynamics in China from 2000 to 2022 Based on Leaf Area Index: A Multi-Temporal Image Classification Perspective

Leaf Area Index (LAI) is one of the most important biophysical parameters of vegetation, and its dynamic changes can be used as a reflective indicator and differentiation basis of vegetation function. In this study, a VCA–MLC (Vertex Component Analysis–Maximum Likelihood Classification) algorithm is proposed from the perspective of multi-temporal satellite LAI image classification to monitor and quantify the spatial and temporal variability of vegetation dynamics in China since 2000. The algorithm extracts the vegetation endmembers from 46 multi-temporal images of MODIS LAI in 2011 without the aid of other a priori knowledge and uses the maximum likelihood classification method to select the categories that satisfy the requirements of the number of missing periods, absolute distance, and relative distance for the rest pixels to be classified, ultimately dividing the vegetation area of China into 10 vegetation zones called China Vegetation Functional Zones (CVFZ). CVFZ outperforms MCD12Q1 and CLCD land cover datasets in the overall differentiation of vegetation functions and can be used synergistically with other land cover datasets. In this study, CVFZ is used to cut the constant vegetation-type pixels of MCD12Q1 during 2001–2022. The results of the LAI mean time series decomposition of each subregion using the STL (Seasonal-Trend Decomposition based on Loess) method show that the rate of vegetation greening ranges from 9.02 × 10−4 m2m−2yr−1 in shrubland subregions to 2.34 × 10−2 m2m−2yr−1 in savanna subregions. In relative terms, the average greening speed of forests is moderate, and savannas tend to have the fastest average greening speed. The greening speed of grasslands and croplands in different zones varies widely. In contrast, the average greening speed of shrublands is the slowest. In addition, CVFZ detected grasslands with one or two phenological cycles, broadleaf croplands with one or two phenological cycles, and shrublands with no apparent or one phenological cycle.

Remote sensing approaches for land use/land cover change in coastal areas and oceanic islands: an open science-based systematic review

In the current climate change context, detecting and monitoring relevant land use/land cover (LULC) changes in insular and coastal areas is critical as soon as they occur. This research consists of a systematic literature review of 167 open-access articles from January 2010 to June 2022, based on several parameters, namely year of publication, journals, geographic location of the study area, time range of the studies, data source, data type, sensors, remote sensing-based approach, data processing algorithms, accuracy assessment approach, and spatial resolution, using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) declaration as a guideline. The results revealed that the years 2020 and 2021 showed the highest number of studies published, namely 33 for each year (20%). The continent with the most case studies was Asia (48%), with China being the most productive country in this field (23%). The most analyzed time range was superior to 20 years (37% of the studies). Satellite imagery was the most applied data source (77%), followed by relevant historical data (e.g., land cover maps). The multispectral data was used in 77% of the studies, and the Landsat Mission represents three of five of the most used sensors. Normalized Difference Vegetation Index was the most applied remote sensing-based approach (10%), and the Maximum Likelihood Classifier Algorithm was the most widely used data processing algorithm (10%). The Overall Accuracy is the most applied accuracy assessment approach used in 85 papers (51%). Many articles used a 30-meter spatial resolution (69%), and higher resolutions completed the top 5 approaches. This study contributes to perceiving the main current approaches for monitoring LULC changes in insular and coastal environments to identify research gaps for future developments.

Spatio-Temporal Change Detection Analysis of Land Use Land Cover of Bathinda District, Punjab, India

ABSTRACT: Due to rapid industrialization and urban sprawl in the last few decades, the land use pattern and its consumption takes place at a large scale that could lead to problems such as over-exploitation of land resources, food insecurity and pollution. It becomes imperative to carry out monitoring and subsequent modelling of land use land cover (LULC) changes. An attempt was made to study the changes in the LULC pattern of district of Bathinda, Punjab, India. Remote sensing (RS) and geographical information system (GIS) were used to perform the analysis of satellite data using image processing and classification procedures. For preparing LULC maps, supervised classification was carried out using maximum likelihood classification (MLC) algorithm, aided with Earth Resources Data Analysis System (ERDAS) Imagine 2014 and ArcGIS 10.3 software. Further, change detection study was done using multi-temporal Linear Imaging Self Scanning Sensor-III (LISS-III) data sets of the year 2006 and 2018 to analyze the temporal changes. It was observed that the region is occupied by various ground features such as water, built-up area, agricultural land, vegetation/trees and fallow land. The results revealed that the area under water bodies have increased by 0.413km2 in 2018. The built-up areas including human settlements, commercial infrastructures, roads and other pavements, have increased from 584.448km2 to 852.140km2 between 2006 and 2018, whereas the agricultural land has reduced from 2686.121km2 to 2398.384km2 during the period. The area under vegetation (trees) indicated that there was an increasing trend from 28.490km2 to 54.678km2 during 12years of time span whereas, the fallow land/barren land showed a decreasing trend from 26.361km2 to 18.367km2. It is suggested that the LULC change detection studies are very significant to conserve the land resources and to avoid further degradation.

Spatial analysis of landscape rehabilitation in degraded land along topographic variabilities in the Kiliso sub-watershed, Southern Ethiopia

In Ethiopia, particularly in Southern Oromia and Eastern Halaba, land degradation is a common challenge for the pastoral community and farmers. As a result, the sub-watershed has closed for the past five years. However, little is known about the effects of topography on the rehabilitation of various land uses and land covers (LULCs). This study determined how the main topographic variables affect the rate of rehabilitation. Sentinel-2 images were used to quantify land use and land cover for the years 2017 and 2021. The aspect, altitude, and slope were calculated using the SRTM of 1 Arc-Second Global. The Maximum Likelihood classifier algorithm was used for supervised image classification. Changes in LULCs were analyzed using the post-classification technique. The LULC maps were overlaid with the topographic variabilities. The topographic variability’s influences were clearly observed on the rehabilitation rate of area enclosure. In the slope class of 0 to 5%, agriculture is the dominant cover. The bushland was largely detected in the slope classes of 0 to 10 in 2017. Agriculture and barren land were influenced as altitude and slope increased. Forest and bushland were found to be highly restored at an altitude of 1841 to 1880, an aspect of the south, and slope classes of 0 to 5 percent.

Assessment of land use land cover dynamics and its impact on springs water in Ritung Khola Sub-Watershed, Myagdi district, Nepal

Land Use Land Cover (LULC) Change has emerged as a significant environmental issue and a worry for the sustainable use of natural resources. This study was performed to determine the rate in change of land cover and its significant impact on springs water in the Ritung Khola sub-watershed of Myagdi district, Nepal, between 2010 and 2020. This study analyzes LULC dynamics and it’s impact on springs water using satellite imageries (Landsat 5 TM and Landsat 8 OLI/TIRS) and focus group discussions with the inhabitants. We used Supervised Maximum Likelihood Classification algorithm to classify attributes of the LULC changes. The results demonstrated a significant change in LULC during those ten years (2010-2020). The area covered by agricultural land and human settlements significantly increased by 313.54% and 367.14%, respectively. On the contrary, barren land, water bodies and forest cover have been reduced by 37.52%, 13.16% and 5.26%, respectively. The number of active springs followed decreasing trend as many of them were completely displaced or dried due to erosions and frequent landslides. The findings from this study are expected to facilitate the planning process adopted to prevent springs under the threat of extension and mitigate the water scarcity problem.