LULC Categories Research Articles

Machine learning for urban land use/ cover mapping: Comparison of artificial neural network, random forest and support vector machine, a case study of Dilla town

The ability to accurately classify land use/cover (LULC) is critical for environmental monitoring and land use planning. This study compares three machine learning algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF) for LULC classification using Google Earth images from the years 2006, 2014, and 2022. The objective of this study is to evaluate and identify the best classifier for LULC classification and change detection. Four LULC categories (Built-up, Open area, Farmland, and Agroforestry) were identified. The evaluation criteria included overall accuracy, kappa coefficient, producer's accuracy, user's accuracy, computing time, algorithm stability, and visual quality. The results showed that the RF algorithm outperformed both SVM and ANN algorithms with an average overall accuracy of 0.97, kappa coefficient of 0.98, producer's accuracy of 0.99, and user's accuracy of 0.97, surpassing the accuracies achieved by SVM (0.96, 0.97, 0.98, and 0.97) and ANN (0.89, 0.81, 0.94, and 0.88), with corresponding computing times of 6.33, 15, and 30 s. All classifiers performed stably with different training sizes. Visual quality assessment revealed that RF had the highest precision. Consequently, the built-up change detection result shows, the net change in built-up area between 2006 and 2022 was increased by 0.74 Km2for ANN, 1.74 Km2 for SVM, and 1.66 Km2for RF. The comparison reveals that the RF algorithm showcasing high precision in detecting change, consistent with the data (increased by 1.65 Km2) obtained from Dilla town land administration office. To validate the results, the study considered field surveys, reference images, local experts, and previous studies. Based on the findings, the study concludes that using RF classifier with an object-based approach is an effective way to map LULC and detect changes in the study area over time. Future researchers are recommended to utilize this effective algorithm for addressing LULC related problems in the study area.

Spatio-Temporal Changes in the World Largest Mangrove Forest and Human Perceptions in Bangladesh: A Study Using GIS-RS and Social Survey Techniques

Mangrove forests are disappearing at a faster rate than any other forests in many places around the world due to natural and anthropogenic causes; as a result, natural resources are decreasing. Considering these issues, present study focuses on the changing pattern of the world’s largest mangrove forest which locally known as Sundarbans, and utilized Landsat satellite imagery during the periods 2000 to 2021 as the primary data source. Using Google Earth Engine, the Normalized Difference Vegetation Index (NDVI) was applied to classify the images into 4 LULC categories (water bodies, sediment, forest, and others). The accuracies of the image classifications were between 89.76 and 92.21 percent. Classified images for the years 2000, 2010, and 2021 were then used to train and validate the ANN-CA (artificial neural network-based cellular automaton) model applied to produce the LULC scenario for the year 2031. On the other hand, a questionnaire survey was conducted in Sarankhola, and Shyamnagar Upazila to identify locals’ perceptions of the causes of LULC changes in the study area. The findings reveal a great change from 4036.92 to 3969.33 km2 of forest cover between 2000 and 2021, and further degradation of 3913.03 km2 by the projected year (2031). The results show that all the 13 identified causes are driving the LULC changes in Sundarbans. The study also suggests if the appropriate management practices are not implemented, the study area's forest land degradation will likely continue in the years to come.
 The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(1), 2023, P 89-105

Remote sensing and GIS-based study of land use/cover dynamics, driving factors, and implications in southern Ethiopia, with special reference to the Legabora watershed

This paper investigates the trends, drivers, and consequences of LULC changes in Legabora watershed, Ethiopia, by utilizing remote sensing and geographic systems. Landsat Maltispectiral scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images of years 1976, 1991, 2001, and 2022, respectively, were used to study the dynamics of LULC. Essential image pre-processing steps were carefully carried out to correct distortions caused by sensor limitations. Eight main LULC categories were identified based on supervised image categorization methods and the maximum likelihood classification algorithm.The findings of change detection and cross-tabulation matrix demonstrate that there has been a significant increase in the area of cropland 345.1 ha/year, settlement 5.9 ha/year, forest 38.2 ha/year, and degraded lands 2.56 ha/year, respectively, over the period between 1976 and 2022. In contrast, considerable decreases were observed in grasslands (−248 ha/year) and shrublands (−144 ha/year), whereas other LULC categories augmented. The results revealed that the overall accuracy rates stood at 88.3 %, 88.4 %, and 85.6 % for 1976, 1991, and 2022, respectively. The overall kappa coefficient demonstrated values of 0.86 %, 0.86 %, and 0.83 % for the same period. Surveyed respondents perceived population growth, settlement, agricultural expansion, and infrastructure development as the most noticeable drivers of these LULC changes. In contrast, deforestation, land degradation, lack of livestock fodder, and biodiversity loss were identified as the main consequences of LULC changes. The factors and implications addressed in this study may be helpful tool for the formulation and implementation of evidence-based land use policies and strategies within in the study area and elsewhere.

Temporal Analysis of Land Use and Land Cover Changes in Vizianagaram District, Andhra Pradesh, India using Remote Sensing and GIS Techniques

Land use and land cover change (LULCC) has become a significant global concern due to its wide-ranging environmental, social and economic impacts. This literature review aims to provide a comprehensive overview of the key ideas, drivers, consequences and approaches to studying LULCC. By synthesizing various research articles, this review offers insights into the causes and impacts of LULCC, as well as the methods used to analyze and monitor these changes. The review also highlights the importance of understanding LULCC dynamics for sustainable land management and policy making. Between 2017 and 2022, the LULC categories underwent several changes. Data acquisition process for satellite imagery combining Sentinel-2 digital remote sensing data digital remote sensing data through the Copernicus Open Access Hub. The spectral resolution is 10, 20, and 30 meters respectively, while the spatial resolution is 10 meters which was used for the LULC analysis of the study area. This analysis underscores the importance of LULCC monitoring to inform sustainable land management practices and conservation efforts. The trends identified provide a basis for further investigation into the underlying drivers of these changes and their potential impacts on ecosystems, water resources and human well-being. Continued monitoring and proactive measures are essential to mitigate adverse impacts and promote sustainable land use in the future.

Land use dynamics and its influences on groundwater depth levels in South region of National Capital Territory (NCT) of Delhi, India.

The present study envisions the influences of land use dynamics on the spatial trend of groundwater depth levels over a period of two decades in the south region of NCT Delhi, India. The findings have inferred that among five major LULC categories, built-up has shown a sprawling trend (+2.17 km2/year) from the north, northeastern, and central portions to the confined patches observed in the south and southwest fringes of South Delhi from 2001 to 2021. Likewise, vegetation class has also witnessed significant increment (+1.91 km2/year) to the peripheral boundary, i.e., southern, southwest/eastern fringes, and central portions under recent initiatives of city forests, plantation drives, and urban green spaces programs. On the contrary, a negative rate of change has been observed in fallow land (-2.78 km2/year), agriculture (-1.22 km2/year), and water bodies (-0.07 km2/year). LULC transition matrix has also showed prominent conversion of fallow land into vegetation and built-up class, and change of vegetation into built-up, and fallow land category. Subsequently, corresponding LULC maps have been superimposed with long-term trends of groundwater depth levels (as spatial contours). For entire South Delhi region, depth to water levels ranged from 2.02 to 66.45 meters below ground level (mbgl) where shallower depths followed a negative trend and remain persistent throughout the time period in north and northeastern fringes. Higher fluctuations in groundwater depletion with positive trends directly get influenced with allied land use transitions such as a steady increase in built-up area and steeper depth levels (> 40 mbgl) as observed in central, southwestern, and southern parts. Moreover, buffer peripheries in the proximity of groundwater monitoring stations viz., Hauz Khas, Pushp Vihar, Jamali, Gadaipur, and Bhatti Kalan have observed deeper groundwater levels allied to built-up expansion. Thus, groundwater depletion trends can be ascribed to the incessant conversion of recharging areas into impervious zones along with uneven distribution of groundwater usage and supply. Conversely, expanding vegetative land has also shown improved groundwater depth levels. Therefore, land use influences must be managed in the long run for ensuring sustainable management of groundwater resources.

Leveraging NAIP Imagery for Accurate Large-Area Land Use/land Cover Mapping: A Case Study in Central Texas

Large-area land use land cover (LULC) mapping using high-resolution imagery remains challenging due to radiometric differences between scenes, the low spectral depth of the imagery, landscape heterogeneity, and computational limitations. Using a random forest (RF)- supervised machine-learning algorithm, we present a geographic object-based image analysis approach to classifying a large mosaic of 220 National Agriculture Imagery Program orthoimagery into lulc categories. The approach was applied in central Texas, USA, covering over 6000 km2. We generated 36 variables for each object and accounted for spatial structures of sample data to determine the distance at which samples were spatially independent. The final rf model produced 94.8% accuracy on independent stratified random samples. In addition, vegetation and water indices, the mean and standard deviation of principal components, and texture features improved classification accuracy. This study demonstrates a cost-effective way of producing an accurate multi-class land use/land cover map using high-spatial/low-spectral resolution orthoimagery.

ANALYSIS OF URBAN LAND USE CHANGE USING REMOTE SENSING AND DIFFERENT CHANGE DETECTION TECHNIQUES: THE CASE OF ANKARA PROVINCE

Abstract. This study aims to use remote sensing techniques to map the urban region of Ankara from the past to the present, assessing the nature, magnitude and direction of changes within the area, including the transformation of LULC classes and explaining the driving forces behind these transformations. The study encompasses three stages. Firstly, Landsat 7 ETM+ images from 2000 and Sentinel-2 satellite images from 2020 were obtained for Ankara city and surroundings through the Google Earth Engine (GEE) platform. Image classification was conducted for both 2000 and 2020 using 'Blue', 'Green', 'Red', 'Vegetation Red Edge1', 'Vegetation Red Edge2', 'Vegetation Red Edge3', 'NIR', 'Vegetation Red Edge4', 'Water vapour', ' SWIR1', 'SWIR2' bands, as well as 'NDWI', 'NDVI', 'NDBI' indices on the GEE platform. LULC was classified using the Random Forest (RF) classifier, which included six classes: urban area, forest, water surfaces, open areas, agricultural areas and roads. Secondly, the LULC maps of the 2000 and 2020 images were classified using RF. The study employed the 'Categorical Change, Pixel Value Change and Time Series Change' methods to determine the transformations between LULC categories. Specifically, the urban change within the study area increased by 70% between 2000 and 2020. Over the past 20 years, from 2000 to 2020, the urban areas in Ankara expanded by 170%. Consequently, accurately determining the nature, magnitude and direction of urban development using remote sensing data offers valuable baseline information for various disciplines related to spatial planning at local and national scales.

Land use and land cover changes along the Boteti-Thamalakane River system in Ngamiland District, Botswana

Analysis of the dynamics of land use and land cover change (LULCC) along rivers that supply water to settlements provides crucial information to decision-makers; it offers information for planners and water supply authorities on water management issues in general. The paper analyzed LULCC along Boteti and Thamalakane rivers and predicted the probable LULCC for the year 2050 using the artificial neural network (ANN) of the module for land use evaluation (MOLUCE) within the QGIS 2.9 software. Adopting a supervised classification method, the Maximum Likelihood Classification (MLC) algorithm was used to classify the study area into five LULC categories. Findings show an increase in a built-up area (46.5%), barren land (90.1%) and a decrease in shrubs/trees (67.4%), water/waterlogged area (48%) and hydrophytes (39.3%) between 1990 and 2020. Using 2020 as the baseline, the ANN modelling approach predicted an increase in the built-up area and a continued shrinking of water and shrubs/trees spatial coverage in the next 30 years. Findings also reveal the presence of building constructions within less than 500 m from the river channel and/or flood plains. While a strict monitoring of the construction works along the riverbank needs to be constantly carried out, the paper also recommends the creation of a buffer zone of 500 m along the rivers where construction activities are prohibited.

Geo-spatial Analysis of the Nexus between Population Growth, Environmental Transformation and Local People’s Livelihoods in Coastal Areas: The Case Study of Kigamboni Municipality, Tanzania

The world is facing an unprecedented population increase in coastal areas. This situation is mainly the result of human migration. The population increase has multiplier effects on the environment and development. In Kigamboni Municipality, land-use changes have been witnessed and new expansions, including agricultural, commercial, industrial and urban-related expansions, happen every day. Thus, this study investigates the impact of population growth on the environment and the local people’s livelihoods in Kigamboni. Land-use/land-cover (LULC) changes were quantified using satellite images. In addition, a total of 156 respondents were randomly selected from four sub-wards in the municipality. Primary data were collected using a structured questionnaire, in-depth interviews, observations and focus group discussions (FGDs). Remote sensing techniques were used to map the spatial and temporal variation of the area between 1995 and 2021 by using a Supervised Classification method in Random Forest Classifier model in R software to generate land-cover types. Seven major LULC categories were identified. The results show that the area under cultivation and the built-up area increased by 40% and 4%, respectively, and that bushlands, forests and grasslands had a declining trend of 34%, 6% and 13%, respectively. The population increase has both positive and negative effects on the local people’s livelihoods, including access to social services, improvement of infrastructure, the availability of markets, the increase in waste, pollution and the increase of the cost of land. The study recommends the formulation of appropriate policies on land-use planning in relation to population dynamics.

Evaluation of earth observation datasets for LST trends over India and its implication in global warming

Land surface temperature (LST) is crucial in surface energy balance, urban climatology, intensifying global change, ecological and environmental concerns. The present study examined the LST trends and spatio-temporal variation over India from 2002 to 2022. This includes comparison of LST for the summer and winter seasons over two decades. Secondly, the present study examined the LULC category wise LST variability during the day and night-time using MODIS (The Terra Moderate Resolution Imaging Spectroradiometer) derived products. This study explored the feasibility of cloud computing for big data analysis for LST distribution in seven landuse categories over India, providing a conceptual response to global warming. Results showed the existence of spatial LST variation due to changes in land-use patterns and MODIS derived vegetation indices- NDVI. Daytime LST for the summer and winter seasons of 2002 was found to be 45.17 °C and 39.13 °C, respectively. Outcomes illustrate declining trends in range (LSTmin-LSTmax) for winter seasons, initially, it was observed as 56.29 °C for 2002 while later on it was observed to be 20.21 °C and 20.87 °C for 2021 and 2022 respectively. The LSTmin (summer) has shown an increasing trend towards upper LST values, from −3.01 °C to 12.21 °C from 2002 TO 2022. LSTmin_winter has shown a rising trend towards the upper LST values from −17.16 °C to 9.15 °C in 2022. The maximum LST for the DRs was observed to be 61.56 °C, followed by UR as 56.24 °C. The findings demonstrate that daytime LSTmin and LSTmax are found to be 19.50 °C to 56.24 °C for UR, 29.5 °C to 61.56 °C for DR, 19.24 °C to 54.08 °C for SAR, −21.1 °C to 0.05 °C for SCR and 15 °C to 32.18 °C for FHR. NDVI-LST (daytime, nighttime and diurnal temperature range) feature space generates an obtuse triangle and depicts a negative correlation of vegetation for a few LULC categories. The outcomes indicated that desert and snow regions have highest LSTmax followed by urban and semiarid regions during daytime. During the nighttime, desert and urban regions have the highest temperature followed by semi-arid and forest regions. The outcomes support the efficacy of earth observation datasets and help to facilitate a better understanding of LULC and its impact on regional climate.

Landuse/landcover monitoring and spatiotemporal modelling using multilayer perceptron and ‘multilayer perceptron’-Markov Chain ensemble models: A case study of Dausa City, Rajasthan

The present work is an attempt to the LULC classification, monitoring, and spatiotemporal prediction using Artificial Neural Network - Multi-Layer Perceptron (MLP) and MLP-Markov Chain (MC) models. Dausa city and its surroundings of Rajasthan, India has been selected for this study for several reasons including arid climatic setting being a sensitive precursor to the climate change scenarios and the huge population pressure experienced by the area. The MLP based supervised classification for two periods 2001 and 2018 have been analyzed using Landsat 7 Thermal Mapper (TM) and Landsat 8 OLI satellite images. The images were classified into six LULC categories viz. Built-up (Settlements), Cultivated Lands (Agricultural/Cropland), Water Body, Uncultivated/Fallow Lands, Barren Lands, and Forest/Vegetation cover. The accuracy assessment for both classified images was performed using confusion matrix led Kappa Coefficient (K) technique. Reasonable accuracies, K=0.82 (2001) & K = 0.91 (2018), have been achieved for datasets selected for both periods of time. The MLP-MC model based spatiotemporal LULC prediction for the year 2045, using the trends in the classified LULC results for the period 2001-2018, prophecies that the ‘built-up land’ would increase to reach 76.10 km2 (67.60% increase) in 2045 with the reference year 2001 whereas the increase in this class of LULC would only be 39.34% during the period 2018-2045. The ‘cultivated land’ (2001-2045: -83.86%; 2018-2045: -65.20%), ‘barren land’, (2001-2045: -54.70%; 2018-2045: -4.86%), ‘water body’ (2001-2045: -96.43%; 2018-2045: -84.42%), and ‘forest/vegetation’ (2001-2045: -81.94%; 2018-2045: -20.59%), categories would experience continuous areal decline over this period, though some at faster pace and other at comparatively lower rate. The projected unprecedented exponential increase in ‘follow land/uncultivated land’ (2001-2045: +372.45%; 2018-2045: +6.39%) presents worrisome future picture of this ecologically sensitive and fragile region. The results of this study indicate and warrant intensive management and policy, and local level participation of communities to help maintain the deteriorating ecological balance in this ecologically sensitive arid ecosystem with fragile agricultural and natural vegetation traits.

Land use and land cover change, and analysis of its drivers in Ojoje watershed, Southern Ethiopia

There have been incredible changes that have taken place in the land use pattern globally over the last 50 years, which resulted from environmental degradation and climate change impacts. Quantitative analysis of the LULC dynamics helps in land-use management and ecosystem degradation at large. The study was conducted in the Doyogena district, southern Ethiopia to identify LULC change dynamics, and analyze the driving forces using combined approaches: remote sensing, field observations, in-depth household interviews, key informants, and Focus Group Discussions (FGDs). A supervised maximum likelihood image cataloging method was employed in conjunction with feature extraction of satellite images to categorize and map LULC classes of the study area. Satellite image handing out, classification technique, and remotely sensed data were processed using ArcGIS map 10.6, and ERDAS Imagine 2014. Common LULC categories were identified, and a change analysis was conducted. Accordingly, seven LULC categories were determined. The result showed a considerable decline in forestland from 1756.7 ha (38.8%) in 1973 to 71.6 ha (1.6%) in 2020. Similarly, wetlands have declined successively from 16.8 in 2000–2010 to 6.3 in 1986–2020 ha/year over the last three and half decades respectively. On the other hand, cropland has increased from 34.1% in 1986–2000 to 46.3% between 1986–2020, which is linked to population growth, settlement, and expansion of farmlands. The study watershed has experienced a considerable change in LULC change over the last >3 decades. Hence, local and national regimes should implement sustainable land planning, management strategies including integrated land- use planning, and policy reform into development projects and programs.

Complex multispatio-periodic land use and land cover change processes, and woody resources management in a forest-Savanna Ecotone, Ghana

Vegetation change discourse in tropical Africa is characterized by assumptions of generalised deforestation and degradation. Premised on the concept of the heterogeneity of the tropical African landscape, we make a multispatio-periodic analysis of land use and land cover change (LULCC), and examine its ramifications for woody resources of a forest-Savanna transition, that is characterized by assumed deforestation change discourse. Landsat data for 1985, 2000 and 2016, classified into seven (7) LULC categories were analyzed for change for 1985–2016, 1985–2000 and 2000–2016: for the whole study area, and six (6) smaller localities. In 1985–2016, all woody vegetation categories of the study area experienced reduction: ranging between 60.00% and 23.61%. Reduction in woodland was higher in 1985–2000. Dense woodland cover reduced in, practically, all localities in 1985–2000. The LULCC mechanism was a complex transfer among LULC types, resulting in different emergent LULC types. Hence, change process was not a simple linear deforestation and degradation. Dense woodland that existed at the end of each period was, predominately, regenerated from pre-existing fallow, rather than remnants of ‘virgin’ or pre-existing dense woodland. When pre-existing woody cover was higher, greater amount of woody vegetation regenerates by transformation in a shorter period, whereas limited woody regeneration of woody vegetation on pre-existing fallows dominateswhen there is limited pre-existing woodland, through a long processes of conversion and transformation. Therefore, maintaining higher woody vegetation cover will promote quicker and substantial regeneration of woody vegetation, and availability of woody resources for sustainable wood-based livelihoods, such as charcoal production.

Analysis of urban heat island characteristics and mitigation strategies for eight arid and semi-arid gulf region cities

The aim of the study is, therefore, to analyze the formation of the UHIs in eight different cities in arid and semi-arid regions. The analysis is based on land cover (LC) classification (urban, green, and bare areas). The study found that bare areas had the highest mean LST values compared to the urban and green areas. The results show that the difference in temperatures between the bare areas and the urban areas ranges between 1 and 2 °C, between the bare areas and green areas ranges between 1 and 7 °C, and between the urban areas and green areas ranges between 1 and 5 °C. Furthermore, the LST values varied for each of the LULC categories, and hence some areas in the three categories had lower or higher LST values than in other categories. Hence, one category may not always have the highest LST value compared to other categories. The outcomes of this study may, therefore, have critical implications for urban planners who seek to mitigate UHI effects in arid and semi-arid urban areas.

Application of Remote Sensing and GIS to Detect Land Use and Land Cover Changes in Jos East Local Government Area, Plateau State, Nigeria.

This study examines the potentials of Remote Sensing techniques and GIS in land resources management with particular reference to detect land use and land cover changes in Jos East L.G.A, between 1995 to 2015. In this study, administrative maps, remotely sensed data (Landsat and Nigeriasat-1 satellite imageries) and GIS techniques were used in the image analysis. All these were done using Ilwis 3.3 Academic, ERDAS 9.3, IDIRISI 17.0 and ArcGIS 10.1. Digital camera was also used for ground truthing. The results were presented using classified imageries. Between the years 1995 to 2015, there was consistent change in the land use land cover of Jos east with different LULC categories. Throughout the study years, vegetation was observed to have the highest percentage of the total land coverage with 57544.28 ha (63%) in 1995, decreasing to 50322.96 ha (50%) in 2005, and 34969.95 (39%) in the year 2015. While agricultural/farm land was gradually increasing throughout the study period with 21271.05 ha (23%) in 1995, 27017.37 ha (27%) in 2005 and 25406.19 ha (28%) in 2015. Findings also showed that build-up-areas/settlement development increased consistently from 1451.97ha (2%) in 1995, 3290.49 ha (3%) in 2005 to 5817.96 (6%) in 2015. It was concluded that agriculture in the study area is increasing while large areas of vegetation is drastically reducing and being converted to farmlands and settlements. It is recommended that government should put up a reliable land management system in form of restrictions on premature conversion of agricultural land, there should be policies that control threat to the vegetation cover. Government should take cognizance of the land use and land cover at a regular interval to ascertain the changes that are taking place in the study area.

Geospatial assessment of land use and land cover dynamics in the mid-zone of Ghana

Abstract Land use and land cover (LULC) terrain in Ghana has undergone profound changes over the past years emanating mainly from anthropogenic activities, which have impacted countrywide and sub-regional environment. This study is a comprehensive analysis via integrated approach of geospatial procedures such as Remote Sensing (RS) and Geographic Information System (GIS) of past, present and future LULC from satellite imagery covering Ghana’s Ashanti regional capital (Kumasi) and surrounding districts. Multi-temporal satellite imagery data sets of four different years, 1990 (Landsat TM), 2000 (Landsat ETM+), 2010 (Alos and Disaster Monitoring Constellation-DMC) and 2020 (SENTINEL), spanning over a 30-year period were mapped. Five major LULC categories – Closed Forest, Open Forest, Agriculture, Built-up and Water – were delineated premised on the prevailing geographical settings, field study and remote sensing data. Markov Cellular Automata modelling was applied to predict the probable LULC change consequence for the next 20 years (2040). The study revealed that both Open Forest and Agriculture class categories decreased 51.98 to 38.82 and 27.48 to 20.11, respectively. Meanwhile, Built-up class increased from 4.8% to 24.8% (over 500% increment from 1990 to 2020). Rapid urbanization caused the depletion of forest cover and conversion of farmlands into human settlements. The 2040 forecast map showed an upward increment in the Built-up area up to 35.2% at the expense of other LULC class categories. This trend from the past to the forecasted future would demand that judicious LULC resolutions have to be made to keep Ghana’s forest cover, provide arable land for farming activities and alleviate the effects of climate change.

Effect of land use and land cover changes on water quality in the Nawuni Catchment of the White Volta Basin, Northern Region, Ghana

Pollution of freshwater resources is gaining global attention due to increasing freshwater stress and scarcity. This study assessed land use and land cover changes (LULCC) in the Nawuni Catchment of the White Volta Basin and how the change affects water quality in the area. Satellite images from Landsat Thematic Mapper and Landsat 8 Operational land imager of the Nawuni Catchment of the White Volta Basin were acquired and used for this study. These images were analysed to ascertain the influence of land use and land cover changes on water quality parameters such as turbidity, ammonia and total coliform counts for a 10-year period (2007 to 2017). The results of LULCC revealed an increased expanse of grassland/farmland (4.1%), settlement (0.1%), bare land (9.4%) and closed savannah (1.2%), whilst opened savannah (14.7%) and water body (0.1%) recorded a decreasing trend. The study showed an increase in the levels of turbidity and ammonia and a decrease in total coliforms over the study period (2007 to 2017). The study also revealed a positive relationship between LULC categories and water quality parameters, implying that LULCC contribute to the change in water quality in the area. The study therefore recommends frequent water quality monitoring and LULC planning and management to curb pollution of the river basin.

Changing land use/land cover patterns and growing human population in Wular catchment of Kashmir Valley, India

The profound increase in human activities and the degrading scenario in fragile ecosystems of the western Himalayas like that of Wular Lake have highlighted the need to analyze the impact of increasing human population on such environments. Wular Lake, the largest freshwater lake, has under gone/witnessed degradation from the last few decades because of tremendous increase in human population in its catchment area. The present work deals with the land transformation in the Wular lake catchment over a period of 28 years (1991–2018) which represents the specific changes in the classes, majorly like waterbody (− 51.60%), swamps/marshy (− 30.92%), plantation (− 6.34%), dense forest (− 10.56%) and snow cover (− 3.66%). To carry out this study, Landsat-5 (TM) images of 1991, 2001, 2011 and Landsat-8 image of 2018 have been used. The population data for the same time period has also been used to coincide with the study dates. Thereafter, the correlation between the changing LULC and human population has been calculated using Pearson’s correlation (rho ρ). LULC categories like barren/rocky, agriculture, horticulture and settlement showed a positive correlation while the categories like snow cover, waterlogged/swampy, water body, dense forest and plantation showed negative correlation with the growing population.

Land use/cover spatiotemporal dynamics, driving forces and implications at the Beshillo catchment of the Blue Nile Basin, North Eastern Highlands of Ethiopia

BackgroundLand use/cover (LULC) change is a dynamic and complex process that can be caused by many interacting processes ranging from various natural factors to socioeconomic dynamics . It exerts a strong influence on the structure, functions and dynamics of most landscapes. Monitoring and mapping of LULC dynamics are crucial as changes observed reflect the status of the environment and provide input parameters for optimum natural resources management and utilization. The objective of this study was to quantify the spatio-temporal LULC dynamics using satellite image coupled with local perceptions in the Gedalas watershed of the Blue Nile Basin, North Eastern Ethiopia. Maximum likelihood supervised image classification technique were employed to classify LULC categories. After ensuring acceptable accuracy value for each classified image, image differencing approach was used to detect and quantify LULC transitions of the area. Classification results were validated with the aid of field work, topographic maps, and high resolution Google earth images supplemented with other available thematic data sets.The resultsThe result demonstrated seven major LULC classes and the overall scenario presented by the study reveals that the watershed has experienced quite visible LULC transitions that seem to be continued in the future due to eternal anthropogenic activities and natural factors. The study ascertain that though there was change in all land use types, the major change detected was a consistent expansion of farmland/settlements area mainly at the expense of Afro/sub Afro alpine vegetation areas. On the contrary, Afro/sub Afro alpine vegetation showed a consistent net loss of over the study of periods. The findings also highlighted that transitions were ultimately driven by the interplay of biophysical, socioeconomic and institutional factors. Perceptions of the local communities on the LULC change substantially agree with data from satellite images. This implies that the ongoing rural land administration and natural resource conservation and management strategies could not effectively address the expansion of agricultural land towards fragile and marginal lands in the study area.ConclusionThe study concludes that if these trends of crop lands expansion allowed continuing, sooner or later there will be no Afro/sub Afro alpine vegetation will remain. Therefore, local governments should strive to expand SLM activities on such mountain ecosystems and other marginal lands focusing on community livelihood diversification and sustainable intensification strategies.

Evaluation of long term forest fires in India with respect to state administrative boundary, forest category of LULC and future climate change scenario: A Geospatial Perspective

AbstractAnalysing the forest fires events in climate change scenario is essential for protecting the forest from further degradation. Geospatial technology is one of the advanced tools that has enormous capacity to evaluate the number of data sets simultaneously and to analyse the hidden relationships and trends. This study has evaluated the long term forest fire events with respect to India’s state boundary, its seasonal monthly trend, all forest categories of LULC and future climate anomalies datasets over the Indian region. Furthermore, the spatial analysis revealed the trend and their relationship.The state wise evaluation of forest fire events reflects that the state of Mizoram has the highest forest fire frequency percentage (11.33%) followed by Chhattisgarh (9.39%), Orissa (9.18%), Madhya Pradesh (8.56%), Assam (8.45%), Maharashtra (7.35%), Manipur (6.94%), Andhra Pradesh (5.49%), Meghalaya (4.86%) and Telangana (4.23%) when compared to the total country’s forest fire counts. The various LULC categories which represent the forest show some notable forest fire trends. The category ‘Deciduous Broadleaf Forest’ retain the highest fire frequency equivalent to 38.1% followed by ‘Mixed Forest’ (25.6%), ‘Evergreen Broadleaf Forest’ (16.5%), ‘Deciduous Needle leaf Forest’ (11.5%), ‘Shrub land’ (5.5%), ‘Evergreen Needle leaf Forest’ (1.5%) and ‘Plantations’ (1.2%). Monthly seasonal variation of forest fire events reveal the highest forest fire frequency percentage in the month of ‘March’ (55.4%) followed by ‘April’ (28.2%), ‘February’ (8.1%), ‘May’ (6.7%), ‘June’ (0.9%) and ‘January’ (0.7%). The evaluation of future climate data for the year 2030 shows significant increase in forest fire seasonal temperature and abrupt annual rainfall pattern; therefore, future forest fires will be more intensified in large parts of India, whereas it will be more crucial for some of the states such as Orissa, Chhattisgarh, Mizoram, Assam and in the lower Sivalik range of Himalaya. The deciduous forests will further degrade in future.The highlight/results of this study have very high importance because such spatial relationship among the various datasets is analysed at the country level in view of the future climate scenario. Such analysis gives insight to the policymakers to make sustainable future plans for prioritization of the various state forests suffering from forest fire keeping in mind the future climate change scenario.