Post-classification Comparison Research Articles

Land cover change assessment in Thai Nguyen Province, Vietnam using GIS and remote sensing techniques

The expansion of agricultural production and urbanization has led to the depletion of the global forest ecosystem, resulting in land cover changes (LCCs) that pose threats to the land environment. Therefore, it is imperative to identify these LCCs as an essential step toward resolution and mitigation. This study aims to evaluate temporal LCCs in Thai Nguyen province by utilizing GIS and remote sensing techniques (RST) to analyze high-resolution satellite imagery spanning from 2001 to 2023. LCCs were identified using semi-automatic classification plugin (SCP) techniques with Landsat-8 and Sentinel-2 images in ERVI software. The accuracy of the LCC maps was validated through post-classification comparisons, yielding high precision rates ranging from 87% to 96%. The results indicate a significant reduction in forest cover area, declining by 1018 ha from 226,18 ha in 2001 to 207,22 ha in 2023. This decline primarily occurred in urban areas, highlighting the intensification of urbanization processes. Cultivated and bare land experienced a minor decrease and a substantial decrease from 18,631 ha and 105,000 ha to 16,578 ha and 86,82 ha between 2001 and 2010, followed by a slight increase and a significant increase to 5,069 ha and 24,487 ha (1.4% and 6.9%) during the 2010-2023 period. These findings underscore the escalating risk of diminishing forest cover in the study area.

Employing post classification comparison to detect land use cover change patterns and quantify conversions in Abakaliki LGA Nigeria from 2000 to 2022

Rapid urbanization is restructuring landscapes across sub-Saharan Africa. This study employed post-classification comparison of multi-temporal Landsat imagery to characterize land cover changes in Abakaliki Local Government Area, Ebonyi State, Nigeria between 2000 and 2022, addressing the need for empirical baselines to guide sustainable planning. Four classes were considered and images classified with overall accuracy of 95% for the year 2000 and 97% for the year 2022. Notably, 21,000 hectares of vegetation were lost, while built-up and bare land increased by 7500 and 13,700 hectares respectively. Spatial patterns revealed built-up encroachment from vegetation and bare land; this establishes the first standardized quantification of Abakaliki LGA’s shifting landscape, with results supporting compact development models while conserving ecological services under ongoing transformations. The study makes a significant contribution by establishing an empirical baseline characterizing Nigeria's urbanization trajectory essential for evidence-based stewardship of regional resources and livelihoods in a period of accelerating change.

Long-term land cover changes assessment in the Jiului Valley mining basin in Romania

Introduction: Highlighting and assessing land cover changes in a heterogeneous landscape, such as those with surface mining activities, allows for understanding the dynamics and status of the analyzed area. This paper focuses on the long-term land cover changes in the Jiului Valley, the largest mining basin in Romania, using Landsat temporal image series from 1988 to 2017.Methods: The images were classified using the supervised Support Vector Machine (SVM) algorithm incorporating four kernel functions and two common algorithms (Maximum Likelihood Classification - MLC) and (Minimum Distance - MD). Seven major land cover classes have been identified: forest, pasture, agricultural land, built-up areas, mined areas, dump sites, and water bodies. The accuracy of every classification algorithm was evaluated through independent validation, and the differences in accuracy were subsequently analyzed. Using the best-performing SVM-RBF algorithm, classified maps of the study area were developed and used for assessing land cover changes by post-classification comparison (PCC).Results and discussions: All three algorithms displayed an overall accuracy, ranging from 76.56% to 90.68%. The SVM algorithms outperformed MLC by 4.87%–8.80% and MD by 6.82%–10.67%. During the studied period, changes occurred within analyzed classes, both directly and indirectly: forest, built-up areas, mined areas, and water bodies experienced increases, whereas pasture, agricultural land, and dump areas saw declines. The most notable changes between 1988 and 2017 were observed in built-up and dump areas: the built-up areas increased by 110.7%, while the dump sites decreased by 53.0%. The mined class showed an average growth of 6.5%. By highlighting and mapping long-term land cover changes in this area, along with their underlying causes, it became possible to analyze the impact of land management and usage on sustainable development and conservation effort over time.

A multi-scale weakly supervised learning method with adaptive online noise correction for high-resolution change detection of built-up areas

Accurate change detection of built-up areas (BAs) fosters a comprehensive understanding of urban development. The post-classification comparison (PCC) is a widely-used change detection method by classification and temporal comparison. For classification, image-level labeling is an efficient alternative to pixel-level one for pixel-wise weakly supervised segmentation, which frequently applies pixel-level pseudo labels generated from class activation map (CAM) to train semantic segmentation networks. CAM can be obtained from classification networks trained with image-level labels and can indicate the spatial location of objects. The existing studies are subject to the following issues: 1) They only rely on the single-scale and low-resolution CAM, but ignore the multi-scale property of BAs; 2) Pixel-level pseudo labels usually contain noises (e.g., omissions and false alarms); 3) The temporal correlation between multi-temporal images is less considered in PCC. To address these limitations, this paper proposed a multi-scale weakly supervised learning method, which utilized a large number of single-temporal high-resolution images and image-level labels to detect BA changes. This method consisted of three modules: 1) multi-scale CAM for BA pseudo label generation; 2) adaptive online noise correction for BA detection; and 3) generation of reliable pseudo labels for BA change detection. Based on ZY-3 images (2.5 m), we constructed the first multi-view datasets for both BA detection and change detection. Each ZY-3 image includes a multi-spectral image with red, green, blue, and near-infrared bands and a multi-view image with nadir-, forward-, and backward-views. The BA detection dataset contained 86,166 image-level samples (256 × 256 pixels for each sample), covering 48 major cities in China, while the BA change detection dataset consisted of ZY-3 bi-temporal images at rapidly urbanizing areas (i.e., Beijing and Shanghai). Experiments showed that the proposed method can detect BA changes and suppress pseudo changes effectively, yielding 88.2% F1-score in BA detection and 79.3% for Shanghai and 78.5% for Beijing in change detection. Further analysis demonstrated the proposed method to be advantageous in the following two fronts: 1) the image-level weak labels can achieve pixel-wise BA change detection at low cost; and 2) the multi-scale CAM and temporal correlation are effective in the scenarios with limited labels. Datasets and codes will be accessed at https://github.com/lauraset/MSWS.

An Assessment of Landscape and Land Use/Cover Change and Its Implications for Sustainable Landscape Management in the Chittagong Hill Tracts, Bangladesh

Human-caused environmental change has profoundly impacted resource management and land use patterns in Bangladesh’s Chittagong Hill Tracts. This study used multi-temporal Landsat images from 1998, 2008, and 2018 to analyze land use and land cover changes, particularly those associated with forest cover changes, in Bangladesh’s Chittagong Hill Tracts. Using object-based image classification, Landsat images from 1998, 2008, and 2018 were separated into four categories based on their dominant land use and land cover features: forest, grassland, water bodies, and bare land. Post-classification comparison was used to assess the degree and frequency of change, and this method was further developed to evaluate the balance, fluctuation, and adaptation of forests. In addition, the spatial structure of land cover and temporal trajectories related to changes in forest cover were studied. The CA–Markov chain model was also used to anticipate the 2048 LULC map. The image classification of the years 1998, 2008, and 2018 showed that the overall accuracy was 89.65%, 84.44%, and 86.26%; producer accuracy was 90.00%, 68.75%, and 72.22%; and the Kappa coefficient was 85.68, 82.84, and 76.36, respectively. The results showed that between 1998 and 2018, forest cover increased by 58.03%, transforming grassland to forest; grassland increased by 29.50%, converting bare land to grassland; and forest conversion to grassland was 13.34%. In addition, the result of the landscape metric revealed that during the whole study period, class level indicated a fragmentation of forest, bare land, grassland, and water in the CHT, and landscape level indicated by Shannon’s Diversity Index and Shannon’s Evenness Index showed a slight decrease in the land. Based on the CA–Markov model, forest area is predicted to expand to 9129 Km2 in 2048; however, other land uses (bare land and grassland) continue to decrease. This substantial increase in forest cover results from effective forest management based on community forestry practices and the successful execution of Bangladesh’s national forest strategy. However, as Bangladesh’s population rises, so does the country’s need for lumber/timber. Bangladesh’s government should revise its forest policy to meet the local community’s needs without endangering the forest, and policymakers must take climate change seriously. Our strategy for evaluating the critical indicators of changes in forest cover and pathways of change will aid in connecting these patterns to the dynamics of change, such as deforestation and reforestation. It would therefore serve as a framework for developing effective conservation and management plans for the Chittagong Hill regions in Bangladesh.

Remote sensing change detection: A comparative study of spectral distances

ABSTRACT Several methods have been developed to detect differences between temporal satellite images for change detection. Image differencing, which is easy to compute and implement, does not require ground-based data. In this study, the performance of 11 other spectral distances was explored in addition to simple differencing for change detection. Moreover, the fusion of these distances was evaluated using various methods, including linear combination, classification, and majority voting. Comparing the results in different study areas showed that Pearson-Correlation and Spearman-Correlation were the most accurate distances. Additionally, the evaluation of the results indicated that the unsupervised fusion of different distances could increase the final accuracy by an average of 10 percent. Furthermore, the classification of distance images, which had slightly lower accuracy than the post-classification comparison of original images, was more accurate than the fusion of distances using these methods or thresholding the individual distances.

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

Remote sensing is an effective way to analyze land surface changes on regular basis globally. In the previous literature, numerous change detection models were developed to detect the multitemporal or seasonal variations using different optical datasets, such as multispectral and hyperspectral. But there are many challenges involved in the processing of numerous bands, especially in the case of hyperspectral datasets, such as computational constraints and radiometric/atmospheric distortion. A simple framework-based artificial neural network (ANN) and post-classification comparison (PCC), named ANN-based PCC (ANPC), has been proposed to detect the multitemporal changes over agricultural land using hyperspectral, i.e., Earth Observation (EO-1) Hyperion and multispectral, i.e., Landsat-8. For cross-referencing, a comparative analysis is performed with respect to well-known PCC-based change detection methods, i.e., random forest-based post-comparison (RFPC) and support vector machine-based post-comparison (SVMPC). Experimental outcomes confirmed the effectiveness of ANPC (with an accuracy of more than 90%) in the extraction of multitemporal changes as compared to RFPC and SVMPC (with an accuracy of <90 % ). Our study enhances the utilization of the hyperspectral dataset (due to narrow spectral bands) in the extraction of critical information about the earth’s surface parameters.

Analysis of land cover land use change in the greater Gaborone area of South Eastern Botswana

Changes in land cover land use (LCLU) have long been considered to be among the many factors responsible for global environmental challenges. This study focused on assessing LCLU changes in the Greater Gaborone area of South Eastern Botswana between 1988 and 2022. The study employed remote sensing (RS) and geographic information systems (GIS) tools for analyzing LCLU changes in the study area during the study period. Landsat images of 1988 and 2002 and Sentinel-2A images of 2022 were used to detect LCLU changes. Image classification was done using a Supervised classification approach based on a Maximum Likelihood Classifier. Six LCLU types such as water body, trees dominated, cropland, shrubland, bare land, and built-up, were identified in the area. Post Classification Comparison (PCC) approach was used to detect LCLU change during the study period. Shrubland class was found to be the dominant LCLU type in the study area. A significant gain was observed in the built-up class (75.12 km2), while significant losses were observed in shrubland (24.16 km2) and trees dominated (33.32 km2) classes in the entire study period. Given the rapid increase in built-up areas, this recommends that land managers and policymakers should invest in implementing sustainable land management interventions to prevent undesirable LCLU changes.

Land use-land cover change in the Tano basin, Ghana and the implications on sustainable development goals

The changes in land use and land cover (LULC) contribute to carbon emissions that lead to climate change and global warming. Obtaining information on LULC change is of necessity to ensure land transformation planning, and anthropogenic and natural impact evaluation. The objective of this study is to analyze the historical changes in LULC in the Tano River Basin of Ghana, to provide scientific information for decision-making on achieving sustainable development. A supervised classification of Landsat images (1986, 2010, and 2020) using the Random Forest algorithm was performed, as well as a post-classification comparison of LULC maps in terms of area and size. A “from–to” matrix was used to identify the changes in LULC that occurred between the periods 1986–2010, 2010–2020, and 1986–2020. The results show an overall classification accuracy of 88.9, 88.5, and 88% for LULC maps for the years 1986, 2010, and 2020, respectively. Between the period 1986 and 2020, there is a conversion of the greater part of dense forest to open forest, and subsequently to settlement and cropland as a major historical LULC change pattern in the Tano basin. Cropland and Settlement increased at the rate of 24.8 km/yr and 1.5 km/yr respectively, while Dense forest and Open forest decreased at the rate of 298.4 km/yr and 173.9 km/yr over the period 1986 to 2020. The study outputs are not only useful for formulating and implementing national policies and programs but also, can contribute to assessing and monitoring progress towards attaining Sustainable Development Goal 13 (climate action).

Modelling and Analysis of Urban Growth of Idah Metropolis Using GIS Integrated Gradient Technique

This thesis aims to better understand changes in the spatial pattern of urban growth and its impact on landscape configuration by conducting a case study in Idah metropolis. The objectives are as follows: &#x0D; &#x0D; To model and analyze multi-temporal images (2001, 2010, and 2021) for determining the urban growth in Idah,&#x0D; To determine urban development trends using post-classification comparisons,&#x0D; Evaluating the degree of urban growth according to urban land cover change over that period &#x0D; &#x0D; Satellite imagery was employed to distinguish and identify different land surface categories. Integrated remote sensing and GIS (Geographic Information System) technique was used to analyse both qualitative and quantitative perspectives regarding the objectives. The results indicate that the urban area of Idah experienced changes in the various classes. The vegetated area experienced a great change of 49.98%. That is a decrease from the previous year of 2001 to 2010. Also, another 48.67% decrease from 2010 to 2021 is due to the developments in the study area. The built-up area equally decreased by 1.33 % between 2010 and 2021. There was an increase of 12.04% from the year 2001 to 2010.Beach has a drastic increase from 28.10% observed between 2001 and 2010 to 44.50% between 2010. The geographic footprint demonstrates that the distribution of the built-up area was dispersed and continues to grow more dispersed. The significant contribution of this study could benefit many aspects, such as comparative studies between cities or continuous studies relevant to urban growth.

Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network

A semi supervised image classification method for satellite images is proposed in this paper. The satellite images contain enormous data that can be used in various applications. The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data. Thus, in this paper, a Radial Basis Function Neural Network (RBFNN) trained using Manta Ray Foraging Optimization algorithm (MRFO) is proposed. RBFNN is a three-layer network comprising of input, output, and hidden layers that can process large amounts. The trained network can discover hidden data patterns in unseen data. The learning algorithm and seed selection play a vital role in the performance of the network. The seed selection is done using the spectral indices to further improve the performance of the network. The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays. It emulates three unique foraging behaviours namelys chain, cyclone, and somersault foraging. The satellite images contain enormous amount of data and thus require exploration in large search space. The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively. The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager (OLI) images of New Brunswick area. The method was applied to identify and classify the land cover changes in the area induced by flooding. The images are classified using the proposed method and a change map is developed using post classification comparison. The change map shows that a large amount of agricultural area was washed away due to flooding. The measurement of the affected area in square kilometres is also performed for mitigation activities. The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased. The performance of the proposed method is done with existing state-of-the-art methods.

Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

Monitoring the Earth’s surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth’s surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth’s surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring.

A Comparison between Supervised Classification Methods: Study Case on Land Cover Change Detection Caused by a Hydroelectric Complex Installation in the Brazilian Amazon

The Volta Grande do Xingu (VGX) in the Amazon Forest of Brazil was chosen to analyze the land use and land cover changes (LULCC) from 2000 to 2017, with the aim of assessing the most suitable classification method for the area. Three parametric (Mahalanobis distance, maximum likelihood and minimum distance) and three non-parametric (neural net, random forest and support vector machine) classification algorithms were tested in two Landsat scenes. The accuracy assessment was evaluated through a confusion matrix. Change detection of the landscape was analyzed through the post-classification comparison method. While maximum likelihood was more capable of highlighting errors in individual classes, support vector machine was slightly superior when compared with the other non-parametric options, these being the most suitable classifiers within the scope of this study. The main changes detected in the landscape were from forest to agro-pasture, from forest/agro-pasture to river, and from river to non-river, resulting in rock exposure. The methodology outlined in this research highlights the usefulness of remote sensing tools in follow-up observations of LULCC in the study area (with the possibility of application to the entire Amazon rainforest). Thus, it is possible to carry out adaptive management that aims to minimize unforeseen or underestimated impacts in previous stages of environmental licensing.

Evaluation of causes and effects of environmental shifts in the Lower Digaru River Basin, Assam, India

Environmental issues like deforestation, biodiversity loss, soil erosion, siltation, wetland shrinkage, river bank erosion, etc. in drainage basin perspectives today are in a rapid flux triggered by population growth and developmental activities. Focusing on the Lower Digaru River basin of North-East India,the present study investigates the changes in theLand Use / Land Cover (LULC)trend using geospatial techniques and the cause-effect relationship by identifying the various environmental issues operating within the study area. The study area has been classified into nine LULC classes: dense forest, openforest, scrub forest, fallow land, barren land, cropland, dense scrub, mixed built-up land, and water body. The results revealed that the area under dense forest has drastically declined from 60.25 % to 16.63 % during 1999-2020. In contrast, the other important categories like Mix built-up land (8.11 % to 14.05 %), scrub forest (2.1 % to 25.11 %), barren land (0.2 % to 6.63 %) and open forest (11.9 % to 18.34 %) show an increasing trend of change during the said period. Post-classification comparison of the classified images based on the transition matrix indicated that approximately 40 % of the total area under forest had been converted to scrub forest, followed by 25.09 % to open forest and, most significantly, 5.22 % to mix built-up land during 1999 -2020. Increasing population pressure and growth of economic activities like the establishment of brick industries, coke industries, and sand mining were the major driving forces for such LULC changes. Environmental implications like wetland shrinkage, river bank erosion, alteration of human occupation and economy, etc., were the study area’s prime concern. The findings suggest that integrated watershed management and land use planning should be implemented in the Lower Digaru River Basin.

Landscape changes over 30 years of intense economic activity in the upper Paraná River basin

In this study, we show the complexity associated with the recent land cover changes by elucidating the paths of 30 years of changes in the Upper Paraná River Basin (UPRB), a region severely impacted by agricultural activity, one of the areas with the highest density in the production of hydroelectricity, biofuels and food in the world. In this sense, a post-classification comparison approach based on Landsat images was used to identify detailed ‘from-to’ paths behind those land cover changes. The most expressive changes were the expansion of Cropland and Forest areas and the reduction in savannas, with a net change of 17.9%, 4.1%, and −16.9% of the UPRB area, respectively. Cropland areas showed an expressive increase between 1985 and 2015, rising from 249,439 km2 (27.7%) to 412,909 km2 (45.9%). Forest areas increased from 149,389 km2 to 185,839 km2 in the period. Notably, for this class, an intense spatial dynamic of losses (7.5%) and gains (11.6%) took place between 1985 and 2015. This behavior is related to the disappearance of native vegetation fragments in some sub-basins, as well as to afforestation, reforestation, and/or forest restoration in others. The Cerrado (a typical tropical savanna in South America), the most impacted natural biome of the Basin, decreased from 21.9% of the UPRB in 1985 (196,746 km2) to only about 5% of the whole UPRB area in 2015. Grassland areas, mostly used for livestock, decreased from 271,827 km2 (30.2%) to 229,007 km2 (25.5%). This net decrease was associated with a reduction of 160,830 km2 (17.8%) and the appearance of 118,010 km2 (13.2%) in new areas, previously occupied by tropical savannas in 1985. In conclusion, economic factors were the main drivers for land cover changes, especially agriculture and livestock activities, besides forestry and hydroelectric energy production. In addition, Grassland areas that predominated on the left banks of the UPRB in 1985 retreated with the advance of Cropland areas, mainly due to the expansion of sugarcane for ethanol production, a biofuel widely used in Brazil. In turn, pasture areas migrated to the right bank and occupied a significant part of the Cerrado. Finally, our results demonstrate that the transition dynamics among land cover classes can involve complex political-economical mechanisms that are not always captured by remote sensing.

Change Detection over mining areas using Deep Neural Networks from Planetscope image time series

The processes of change detection (CD) in land use and land cover through satellite images acquired in different temporal phases represent a key process to monitor the land, the environment and evaluate disasters. Currently the methodologies used for CD based on pixels do not allow processing a large amount of images massively, so it is not possible to analyze all the available information. Main aim of this research is to evaluate the performance of Deep learning methods for CD, grouped in two approaches, across open mining areas from PlanetScope (PS) image time series of the Cerrejon mine in Colombia. Two approaches for change generation are proposed, one based on post-classification comparison in which two convolutional neural network (CNN) architectures are evaluated: U-Net and Feature Pyramidal Network (FPN) for the classification of mining areas along the time series, for this purpose different models with different hyperparameters were generated and trained to select the most suitable for such process and subsequently perform the difference between the periods of the series; and a second approach based on direct change detection in which a modified U-Net network was evaluated from pairs of images. For this purpose, different models were also trained selecting the most appropriate for the detection of changes for each period of the time series, obtaining a map of changes for each approach, each one the results were validated; the most appropriate approach was number 2 (Direct change detection), with kappa accuracies greater than 0.9 in each period of the time series.

Climate Change and Land Use Dynamics in Djirnda Commune (Fatick Region - Senegal): Remote Sensing Approach

Climate change is one of the greatest challenges of our time. It is a real and rapid phenomenon which, perhaps, does not dramatically affect people today but will certainly have harmful effects on future generations. It has led to the weakening of ecosystems and accelerated the degradation of natural resources. This situation has prompted the scientific world to sound the alarm to warn of its environmental and socio-economic impacts. This study seeks to analyze the dynamics of land use in Djirnda municipality in climate change. A pixel-based classification approach and the maximum likelihood algorithm were retained. The data used concerns Landsat images from 1986, 2000 and 2015. The post-classification comparison method is used to detect changes in the classified images. The overall accuracy of the classifications gives satisfactory results with 90 % for 1986, 89 % for 2000 and 91 % for 2015 and a respective Kappa coefficient of 89 %, 85 % and 90 %. The results of land use dynamics revealed an increase in mangroves (1553.13 ha) and salt lands (957.15 ha), and a regression in water surface (- 458.64 ha) and mudflats (- 2051.64 ha).

GIS-based analysis of green space in urban Himalayas and modelling the effects on destination business performance

Purpose This paper aims to analyse the changes in the extent of the green space in the city of Srinagar, a unique urban Himalayan destination, and examine whether the difference in the green space has a significant effect on the destination business performance measured in terms of loss of ecological attractiveness. Design/methodology/approach This study was carried out in two phases in the study area. In phase I, the changes in the extent of the green space area were analysed using Landsat TM and Sentinel Images for classification. The study has used the period from 2001 to 2018 to understand changes in the green space. The Post-Classification Comparison technique was used to investigate the variation in the green space zones in the city of Srinagar. In phase II, the paper evaluated the impact of change in the green space on the destination business performance. The data was collected from the tour operation companies through a questionnaire survey, and the impact path was examined using structural equation modelling. Findings Results reveal that the green space in the city of Srinagar has decreased over the past 18 years, and the decreasing green space has a significant effect on the destination business performance. Research limitations/implications Identifying the impact of decreasing green space on the destination business performance of the study area under investigation is essential for tourism development both in terms of new product development and resource preservation. Developing a measurement scale showing the impact of decreasing green space on destination business performance could offer destination managers a means of identifying the essence of the green space in the destination regions. These findings add to the growing literature on the attributes of tourism destinations, providing scholars with new insights into the role of green space in destination performance. The current study offers evidence of the impact of decreasing green space on the destination's performance. This provides a new perspective for future studies on visitor satisfaction as a potential mediator of the relationship between reducing greenspace and destination business performance. The main limitation of this study is that the researchers have only analysed the impact of decreasing green space on the destination business performance in terms of its ecological competitiveness. Other destinations business performance verticals, such as hotels, restaurants and grocery stores were not considered by this study and can be taken up for future investigation. Practical implications This study provides empirical insights that can have significant implications for researchers, policymakers, destination management organizations, academia and practitioners and further enrich the existing literature by establishing an empirical argument in the context of urban destinations positioned with a fragile Himalayan ecosystem. Originality/value This study aims to assist the urban administrators in improving the green space ecosystem in the region, which can help attain the sustainability of the city environment and assist in economic regeneration in urban settings.

Land Cover Change Detection and Subsistence Farming Dynamics in the Fringes of Mount Elgon National Park, Uganda from 1978–2020

Analyzing the dominant forms and extent of land cover changes in the Mount Elgon region is important for tracking conservation efforts and sustainable land management. Mount Elgon’s rugged terrain limits the monitoring of these changes over large areas. This study used multitemporal satellite imagery to analyze and quantify the land cover changes in the upper Manafwa watershed of Mount Elgon, for 42 years covering an area of 320 km2. The study employed remote sensing techniques, geographic information systems, and software to map land cover changes over four decades (1978, 1988, 2001, 2010, and 2020). The maximum likelihood classifier and post-classification comparison technique were used in land cover classification and change detection analysis. The results showed a positive percentage change (gain) in planted forest (3966%), built-up (890%), agriculture (186%), and tropical high forest low-stocked (119%) and a negative percentage change (loss) in shrubs (−81%), bushland (−68%), tropical high forest well-stocked (−50%), grassland (−44%), and bare and sparsely vegetated surfaces (−14%) in the period of 1978–2020. The observed changes were concentrated mainly at the peripheries of the Mount Elgon National Park. The increase in population and rising demand for agricultural land were major driving factors. However, regreening as a restoration effort has led to an increase in land area for planted forests, attributed to an improvement in conservation-related activities jointly implemented by the concerned stakeholders and native communities. These findings revealed the spatial and temporal land cover changes in the upper Manafwa watershed. The results could enhance restoration and conservation efforts when coupled with studies on associated drivers of these changes and the use of very-high-resolution remote sensing on areas where encroachment is visible in the park.

Declining land for subsistence and small-scale farming in South Africa: A case study of Thulamela local municipality

In Thulamela Local Municipality, South Africa, competing land use activities such as urbanisation and subsistence farming have accelerated rapid, widespread changes in land use and land cover (LULC) with potential negative impacts on subsistence and small-scale farming, food security, and sustainable rural livelihoods. Therefore, the aim of the current study was to investigate the decline in land available for subsistence and small-scale farming in the Thulamela Local Municipality. A post-classification comparison approach was performed using three Landsat images from 2005, 2013 and 2020 in order to quantify the spatio-temporal dynamics of LULC in the study area. The land use management system and applicable land use policies were examined to gain an understanding of the factors driving LULC. The results showed a general decline in agricultural land with a general increase in built-up areas. Land under Agricultural Fallow shrank from 26% to 8% with a 69% decline in extent while the extent of Grass reduced from 22% to 16%. The study has shown that the decline in agricultural land and its conversion to built-up areas can be attributed to ineffective land use management system, which is less protective of land for subsistence and small-scale farming in rural areas characterised customary land tenure rights. The land use scheme’s generalisation of vacant and unsurveyed land as agricultural use zone makes it difficult to oppose development on productive agricultural land currently used for subsistence and small-scale farming in rural areas. Therefore, the study recommends that suitable agricultural land or land currently used for subsistence and small-scale farming should be clearly categorised as arable use zone from municipal land use schemes in order to ensure sustainable utilisation and protection of arable land for agricultural purposes. Measures should also be taken to secure tenure rights for subsistence farmers in order to curb the widespread conversion of agricultural land into built-up areas.