Land Use And Land Cover Classes Research Articles

The Land-Use and Land-Cover Changes in the Este District, South Gondar Zone, Northwestern Ethiopia, in the Last Four Decades (the 1980s to 2020s)

Environmental transformations are the results of land-use and land-cover (LULC) changes. This study aims to investigate the LULC changes and associated factors in the Este District in northwestern Ethiopia, for the last four decades (the 1980s to 2020s). The land-use and land-cover classes were analyzed using supervised classification techniques in ERDASS IMAGINE 2015 and ArcGIS 10.3.1, categorizing the Landsat satellite images for 1984, 2000, and 2018 into six cover classes: settlement, forests-shrublands, cropland, grassland, bare land, and water body. We used a historical Google map, topo-sheets, and ancillary data to verify the classification accuracies for 1984, 2000, and 2018, respectively. The climate and demographic data were obtained from the Ethiopian Meteorological Station and Ethiopian Statistical Agency, respectively. In addition, data from key informant interviews and focus group discussions were also used to understand the local community experiences and perceptions toward LULC changes. The values of each LULC class were correlated with the demographic and climatic variables, using simple correlation analysis to evaluate the impact of demographic and climatic conditions on LULC changes. The analysis indicated that the least and largest classes of LULC in Este District were water bodies (mean cover = 1.9 km2) and croplands (mean cover = 791.7 km2), respectively. Cropland coverage increased by 2% in 2000, then decreased by 11% in 2018. Between 1984 and 2018, the grasslands and settlements increased by 22% and 0.5%, respectively. Half of the bare land and one-tenth of the forests-shrublands also decreased over the last four decades (the 1980s to 2020s). As a result, the original croplands and forests–shrublands classes (11% each) before 2000 were converted into new grasslands and croplands after 2018, respectively. The study indicated that precipitation, solar radiation, and population growth are the potential drivers, and the perceptions of local communities are nearly in line with the statistical analysis results. Alternative income sources, such as tourism and carbon trading and the participation in afforestation programs, could reverse the situation in the study area.

Mapping Land Use and Land Cover Classes in São Paulo State, Southeast of Brazil, Using Landsat-8 OLI Multispectral Data and the Derived Spectral Indices and Fraction Images

This work aims to develop a new method to map Land Use and Land Cover (LULC) classes in the São Paulo State, Brazil, using Landsat-8 Operational Land Imager (OLI) data. The novelty of the proposed method consists of selecting the images based on the spectral and temporal characteristics of the LULC classes. First, we defined the six classes to be mapped in the year 2020 as forest, forest plantation, water bodies, urban areas, agriculture, and pasture. Second, we visually analyzed their variability spectral characteristics over the year. Then, we pre-processed these images to highlight each LULC class. For the classification, the Random Forest algorithm available on the Google Earth Engine (GEE) platform was utilized individually for each LULC class. Afterward, we integrated the classified maps to create the final LULC map. The results revealed that forest areas are primarily concentrated in the eastern region of São Paulo, predominantly on steeper slopes, accounting for 19% of the study area. On the other hand, pasture and agriculture dominated 73% of all São Paulo’s landscape, reaching 39% and 34%, respectively. The overall accuracy of the classification achieved 89.10%, while producer and user accuracies were greater than 84.20% and 76.62%, respectively. To validate the results, we compared our findings with the MapBiomas Project classification, obtaining an overall accuracy of 85.47%. Therefore, our method demonstrates its potential to minimize classification errors and offers the advantage of facilitating post-classification editing for individual mapped classes.

Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning

Accurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique between 2011 and 2020 based on Landsat time series processed by the Random Forest classifier in the Google Earth Engine platform. The feature selection method was used to reduce redundant data. The final maps comprised five LULC classes (non-vegetated areas, built-up areas, croplands, open evergreen and deciduous forests, and dense vegetation) with an overall accuracy ranging from 80.5% to 88.7%. LULC change detection between 2011 and 2020 revealed that non-vegetated areas had increased by 0.7%, built-up by 2.0%, and dense vegetation by 1.3%. On the other hand, open evergreen and deciduous forests had decreased by 4.1% and croplands by 0.01%. The approach used in this paper improves the current systematic mapping approach in Mozambique by minimizing the methodological gaps and reducing the temporal amplitude, thus supporting regional territorial development policies.

Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data in an Agricultural Watershed

Classification of remotely sensed imagery for reliable land use and land cover (LULC) remains a challenge in areas where spectrally similar LULC features occur. For example, bare soils of harvested crop fields in agricultural watersheds exhibit spectral characteristics similar to high-intensity developed regions and impede an accurate classification. The goal of this study is to improve the accuracy of LULC classification of satellite imagery for the Big Sunflower River Watershed, Mississippi using ancillary data, multiple classification methods, and a post-classification correction (PCC). To determine the best approach, the methodology was applied to Landsat 8 Operational Land Imager (OLI) imagery during the growing season and post-harvest. Imagery for the growing season was acquired on 25 August 2015, and post-harvest was acquired on 7 January 2018. Three classification methods were applied: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). LULC imagery was classified as open water, woody wetlands, harvested crop, rangeland, cultivated crop, high-intensity developed, and mid-low intensity developed areas. Ancillary data such as normalized difference vegetation index (NDVI), thematic maps of urban areas, river networks, transportation networks, high-resolution National Agriculture Imagery Program (NAIP) imagery, Google Earth time-series data, and phenology were used to determine the training dataset. Initially none of the three classification methods performed adequately. Hence, a post-classification correction (PCC) was implemented by masking and applying a majority filter using thematic maps of urban areas. Once PCC was implemented, the accuracies from each of the classification methods increased significantly with the SVM classification method performing best in both the growing season and post-harvest with an overall classification accuracy of 93.5% with a Kappa statistic of 0.88 in the post-harvest imagery and an overall classification accuracy of 84% with a Kappa statistic of 0.789 in the imagery from the growing season. It was found that SVM was the best classification method while PCC is an effective strategy to implement when dealing with spectrally similar LULC features. The use of SVM together with PCC increased the reliability of the information extracted. Strategies from this study can help to evaluate the LULC in agricultural and other watersheds.

Assessment of Object-Based Classification for Mapping Land Use and Land Cover using Google Earth

<p>Land Use and Land Cover (LULC) maps perform a significant part in Remote Sensing (RS) in monitoring and analyzing earth information for human development. Because of the high spectrum variability, LULC classification from RS data is extremely difficult. The study's objective is to assess the object-based LULC classification (OBC) accuracy of composite images from the Landsat 8 OLI (Operational Land Imager) and auxiliary features using SNIC segmentation and five classifiers on Google Earth Engine (GEE). The outcome of the study indicates that the OBC with only spectral features achieves lesser accuracy because small objects on the land surface cannot be observed. But when OBC is paired with a variety of auxiliary features, the OBC may recognize small objects with greater accuracy. After many subsequent assessments the combination of the following aspect: composite image of all the seven bands, new feature set, Simple Non-Iterative Clustering (SNIC) segmentation algorithm, and Support Vector Machine (SVM) classification algorithm gives better accuracy of ~ 94.42% and kappa coefficient of 92.07. The inclusion of auxiliary features and the OBC method reduces the misclassification rate related to the confusion of bare land, uncultivated land, and urban, which provides accurate information on forest, waterbody, and bare land classes.</p>

Land Use and Land Cover (LULC) Assessment within the Batanes Protected Landscapes and Seascapes

Declared protected areas have ecologically important landscapes that must be conserved and protected. Status of protected areas could be monitored through land use and land cover (LULC) assessments. LULC offers baseline data for integrated land use planning and improvement of existing policies are therefore necessary to be conducted. This study was conducted to monitor the existing LULC of six islands within the Batanes Protected Landscapes and Seascapes (BPLS) through a machine learning (ML)-based random forest (RF) classifier using multi-sourced data such as Landsat imageries’ surface reflectance (SR), Landsat-derived land surface temperature (LST), and global ecosystem dynamic investigation (GEDI)-derived height (Ht) metrics and to determine the effects of the LST and Ht metrics to LULC classification. Four layer stacked images with different features were analyzed – including SR, SR-LST, SR-Ht, and SR-LST-Ht. The result of the LULC classification showed an accuracy based on Macro F1-score and Kappa (K) of 0.81 and 0.83, 0.83and 0.86, 0.86 and 0.89, and 0.93 and 0.94, for SR, SR-LST, SR-Ht, and SR-LST-Ht, respectively. When compared to the existing global-scale LULC, this study has higher accuracy than the GLAD and ESRI products, which have Macro F1-scores and K-values of 0.73 and 0.71, and 0.59 and 0.64, respectively. To conclude, the inclusion of LST and Ht information in addition to SR data in LULC classification can improve the accuracy by up to 12% and 11% based on Macro F1-score and K,respectively. The result of this study can serve as a reference for achieving improved and reliable LULC information that is necessary for monitoring fluctuations of the global earth’s resources and comprehensive LULC planning. In addition, the technique used in this study can serve as a reference in generating reliable LULC information that can aid in the sustainable implementation of policies, rules, and regulations intended for declared protected areas like BPLS.

Vejetasyon İndeksleri, Ana Bileşenler Analizi ve Google Earth Engine Kullanılarak Tarımsal Alan Sınıflandırması: Söke/Aydın Örneği

Land use and land cover (LULC) classification is known to be one of the most widely used indicators of environmental change and degradation all over the world. There are various algorithms and methods for LULC classification, whereby reliability of the classification maps presents the principal concern. The study focused on evaluation of accuracies of LULC maps produced from original bands of Sentinel-2 imageries together with Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), and Principal Component Analysis (PCA) using Google Earth Engine (GEE) platform to identify best enhancing method for agricultural land classification. Moreover, short-term LULC changes aimed to be identified in the specified area. To achieve the aims, all available imageries acquired in the same month of different years with less than 10% cloud contamination were used to compose averaged images for May 2018 and May 2022 for generating LULC2018 and LULC2022 maps. The area has separated into seven main classes, namely, olive (O), perennial cultivation (P), non-perennial cultivation (NP), forest (F), natural vegetation (N), settled area-bare land (S), and water surface (W) via random forest algorithym. Reliabilities of LULC maps were evaluated through accuracy assessment procedures considering stratified randomized control points. Transitions between each LULC classes were identified.

Assessment and Spatial Analysis of Land Use and Land Cover (LULC) Dynamics in the Black Volta Basin

The Black Volta Basin, a critical hydrological region, is experiencing rapid changes in Land Use and Land Cover (LULC) patterns due to diverse anthropogenic and environmental factors. This study employs a comprehensive Multi-Sensor Remote Sensing and Geographic Information System (GIS) methodology to assess and analyze the spatiotemporal dynamics of LULC in the basin. Utilizing data from multiple remote sensing platforms, including optical and radar sensors, as well as historical GIS datasets, a detailed LULC classification is performed. The integration of multi-temporal satellite imagery enables the detection and quantification of changes across distinct land cover categories. Through rigorous spatial analysis and statistical techniques, the study reveals the extent and magnitude of LULC transformations over a defined period. Land cover transitions are identified, characterized, and linked to potential drivers such as urbanization, agricultural expansion, and natural processes. The outcomes of this research contribute to an enhanced understanding of the evolving LULC dynamics in the Black Volta Basin, shedding light on both natural and human-induced changes. The results underscore the significance of a multi-sensor approach in accurately capturing complex LULC changes and their implications for regional sustainability and resource management. This study's insights serve as valuable inputs for policymakers, researchers, and stakeholders engaged in land-use planning, environmental conservation, and sustainable development within the Black Volta Basin and similar ecologically sensitive areas.

Pre-Processing Training Data Improves Accuracy and Generalisability of Convolutional Neural Network Based Landscape Semantic Segmentation

Data pre-processing for developing a generalised land use and land cover (LULC) deep learning model using earth observation data is important for the classification of a different date and/or sensor. However, it is unclear how to approach deep learning segmentation problems in earth observation data. In this paper, we trialled different methods of data preparation for Convolutional Neural Network (CNN) training and semantic segmentation of LULC features within aerial photography over the Wet Tropics and Atherton Tablelands, Queensland, Australia. This was conducted by trialling and ranking various training patch selection sampling strategies, patch and batch sizes, data augmentations and scaling and inference strategies. Our results showed: a stratified random sampling approach for producing training patches counteracted class imbalances; a smaller number of larger patches (small batch size) improves model accuracy; data augmentations and scaling are imperative in creating a generalised model able to accurately classify LULC features in imagery from a different date and sensor; and producing the output classification by averaging multiple grids of patches and three rotated versions of each patch produced a more accurate and aesthetic result. Combining the findings from the trials, we fully trained five models on the 2018 training image and applied the model to the 2015 test image. The output LULC classifications achieved an average kappa of 0.84, user accuracy of 0.81, and producer accuracy of 0.87. Future research using CNNs and earth observation data should implement the findings of this project to increase LULC model accuracy and transferability.

Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China

Various land use and land cover (LULC) products have been produced over the past decade with the development of remote sensing technology. Despite the differences in LULC classification schemes, there is a lack of research on assessing the accuracy of their application to croplands in a unified framework. Thus, this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products (i.e., MCD12Q1 V6, GlobCover2009, FROM-GLC and GlobeLand30) based on the harmonised FAO criterion, and quantified the relationships between four factors (i.e., slope, elevation, field size and crop system) and cropland classification agreement. The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency, with overall accuracies of 94.90 and 93.52%, respectively. The FROM-GLC showed the worst performance, with an overall accuracy of 83.17%. Overlaying the cropland generated by the four global LULC products, we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification, respectively. High consistency was mainly observed in the Northeast China Plain, the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain, China. In contrast, low consistency was detected primarily on the eastern edge of the northern and semiarid region, the Yunnan-Guizhou Plateau and southern China. Field size was the most important factor for mapping cropland. For area accuracy, compared with China Statistical Yearbook data at the provincial scale, the accuracies of different products in descending order were: GlobeLand30, FROM-GLC, MCD12Q1, and GlobCover2009. The cropland classification schemes mainly caused large area deviations among the four products, and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products. Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction, which is essential for food security and crop management, so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.

Performance comparison of CNNs on high-resolution multispectral dataset applied to land cover classification problem

Multispectral images acquired by satellites have been used in many fields such as agriculture, urban change detection, finding fire-hazardous forest areas, and real-time surface monitoring. The central issue in remote sensing analysis is land use and land cover classification. Land use and land cover classification (LULC) is the process of classification into meaningful classes based on the spectral characteristics of remote sensing data. Land use and land cover classification is a challenging task due to the complex nature of the Earth's surface. The accuracy of solving the issue using deep learning approaches depends on the quality of the remote sensing data, the choice of the classification algorithm. The ability to obtain high-resolution multispectral images periodically could dramatically improve remote sensing solutions. In this study, we propose a solution for the land cover and land classification problem of high-resolution remote sensing data by applying deep learning methods using EuroPlanet geo-referenced high-quality images with four bands and pixel resolution of 204x204 per image, and acquired by Planet platform in 2020-2022 years. The dataset consists of 25911 images with spatial resolution up to 3.125 meters per pixel and 10 different classes. In the past decade, artificial neural networks have shown great performance in solving complex image classification tasks. For the dataset evaluation, we have taken advantage of state-of-art pretrained convolutional neural network models ResNet50v2, EfficientNetV2, Xception, VGG-16, and DenseNet201 with fine tuning. It has been established that DenseNet201 pretrained neural network outperformed other models. The accuracy of the test data was 92.01 % and the F1 metric was 91.63 %. In addition, bands evaluation for the dataset was carried out. Overall classification accuracy of 93.83 % and F1 score of 93.56 % were achieved by DenseNet201 model. The results could be used for area verification, real-time monitoring, and surface change detection. Nowadays, this is very helpful for Ukrainian territory because of the Russian invasion and the country's recovery in the future.

Land use-land cover dynamics, and local perceptions of change drivers among Nyangatom agro-pastoralists, Southwest Ethiopia

Understanding the spatio-temporal dynamics of land use and land cover (LULC) and their drivers is instrumental in synthesizing knowledge for informed natural resource management, planning, and associated decisions. The objective of this study was to analyze LULC dynamics and their drivers in the Nyangatom district of southwest Ethiopia. This study adopted a mixed-method approach, comprising remote sensing and geographic information system-based analysis, focus-group discussions, key informant interviews, and structured interviews covering 384 agro-pastoral households. Land use and land cover maps were compiled with Landsat image data from 1992, 1999, 2010, and 2020 using supervised pixel-based classification with a maximum likelihood algorithm. The study revealed that grassland (33%) and shrub land (27%) are the dominant LULC classes in the study periods. The results show a significant increase in settlement, cropland, and bare land, between 1992 and 2020 following demand for fertile lands, overgrazing, and climate changes. Reductions in the water body, open woodland, riverine forest, and dense woodland of − 23%, − 18%, − 18%, and − 7%, respectively, were observed for the same period with an overall accuracy of 80%. Agro-pastoralists identified agricultural expansion, overgrazing, and drought as the main drivers of LULC changes, followed by conflicts, invasion of alien plant species, and change in policy institutions. The impacts of unplanned LULC change can be reduced through appropriate land-use planning with genuine stakeholder engagement. Updated information on present and future LULC dynamics can support these social dynamics and align a shared understanding among stakeholders that replaces polarization and erosion of mutual trust.

Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning

Land Use and Land Cover (LULC) classification using remote sensing data is a challenging problem that has evolved with the update and launch of new satellites in orbit. As new satellites are launched with higher spatial and spectral resolution and shorter revisit times, LULC classification has evolved to take advantage of these improvements. However, these advancements also bring new challenges, such as the need for more sophisticated algorithms to process the increased volume and complexity of data. In recent years, deep learning techniques, such as convolutional neural networks (CNNs), have shown promising results in this area. Training deep learning models with complex architectures require cutting-edge hardware, which can be expensive and not accessible to everyone. In this study, a simple CNN based on the LeNet architecture is proposed to perform LULC classification over Sentinel-2 images. Simple CNNs such as LeNet require less computational resources compared to more-complex architectures. A total of 11 LULC classes were used for training and validating the model, which were then used for classifying the sub-basins. The analysis showed that the proposed CNN achieved an Overall Accuracy of 96.51% with a kappa coefficient of 0.962 in the validation data, outperforming traditional machine learning methods such as Random Forest, Support Vector Machine and Artificial Neural Networks, as well as state-of-the-art complex deep learning methods such as ResNet, DenseNet and EfficientNet. Moreover, despite being trained in over seven million images, it took five h to train, demonstrating that our simple CNN architecture is only effective but is also efficient.

Estimating Advance of Built-Up Area in Desert-Oasis Ecotone of Cholistan Desert Using Landsat

There have been few attempts to estimate the effects of land use and land cover (LULC) on ecosystem services in desert-oasis ecotones, which are recognized as critical ecological barriers and buffers that prevent deserts from expanding into oases. This research investigated how remote sensing and geographic information technology may be used to monitor changes in LULC in the Cholistan desert and the Bahawalpur region of Pakistan between the years 2015 and 2022. The objective of this research was to identify thematic and statistical shifts in LULC in the study area due to various human interventions in the area. Landsat-8 images were processed using the maximum likelihood supervised classification technique using 500 training samples to categorize the study area into four LULC classes, i.e., desert/barren land, waterbodies, vegetation, and built-up areas, with an overall accuracy of 93% and 98% for 2015 and 2022, respectively. Results indicate a significant expansion in built-up area in 2022, which is up to 43%, agriculture and vegetation area declined by 8%, waterbodies decreased by 41%, and desert area decreased by 2% when compared with 2015. The change detection approach revealed that agricultural land was directly encroached on by rapidly increasing built-up area and urbanization as the area had an overall 19% rise in population growth within eight years with an annual growth rate of more than 3%. This study will be helpful to assess the quantity of spatial and temporal changes in the desert ecosystem, which is usually ignored by policymakers and governments due to less economic activity, although it plays a huge role in biodiversity conservation and balancing the regional ecosystem.

Impacts of Land Use and Land Cover Changes on Migration and Food Security of North Central Region, Nigeria

Food security is adversely affected by challenges posed by changes in land use and land cover (LULC). LULC change impacts ecosystem functions and services, leading to migration of people, particularly rural dwellers. This paper uses multispectral satellite remote sensing, net migration data, household survey, stakeholders’ meetings, Focus Group Discussions (FGD), expert interviews and yields and estimated land mass of maize, rice, groundnut, cassava, and yam to assess the extent of LULC in Niger, Kwara, and Benue states of North Central Region of Nigeria and their relevance for migration and food security. Remote sensing data for 1990, 2000, 2013, and 2020 were extracted from Landsat imageries to obtain LULC change. Household survey was conducted to validate the data obtained from Landsat imageries. The results of LULC between 1990 and 2020 show that most of the vegetation, agricultural land, and water body areas in Kwara and Benue States have been converted to built-up areas and barren land, while an increase in agricultural land and built-up areas was observed in Niger State. Our household survey, stakeholders’ meetings, and interviews showed that there was a continuous massive migration of people, particularly young farmers, to cities leaving most of the existing agricultural lands uncultivated. This was due to the losses in agricultural land and conversion of some of the other LULC classes to barren land. We conclude that if this permanent migration remains uncontrolled, it will have significantly negative future impacts on food security of Nigeria. It is recommended that the government and its sub-ordinary administrative entities invest in more reliable infrastructure and attractive living environment for the rural dwellers to reduce the rate of rural-urban migration in the study areas.

Modeling of land use change under the recent climate projections of CMIP6: a case study of Indian river basin.

The aim of the study was to investigate the land use change dynamics under CMIP6 projections using Land Change Modeler(LCM). The Global Sensitivity Analysis (GSA) techniques was applied to quantify the sensitivity of single parameter and combinationof parameters. Land use and land cover (LULC) transitions of the baseline period (2006-2016) was assessed with a model performance accuracy of 80%. Receiver operating characteristic (ROC) analysis shows that the model has performed well for all the LULC classes except builtup land. Prediction under the SSP245 scenario depicts that areal extent of agricultural, forest, and snow, and glacier will decrease by the mid-century (2045). However, the grassland and barren land areawill increase from the baseline period. A similar change pattern with a higher magnitude has also been predicted under SSP585 scenario. The CMIP6 forcing index considers socio-economic effects and LCMpredicted an expansion in barren land which may be attributed to changes in cryosphere in the studied area.

LAND USE/LAND COVER CLASSIFICATION IN A HETEROGENEOUS AGRICULTURAL LANDSCAPE USING PLANETSCOPE DATA

Abstract. Land use and land cover (LULC) classification has long been an essential topic in Earth Observation research and plays a key role in the sustainable development of agriculture. This study evaluated the accuracy of LULC classification based on an initial clustering step in a heterogeneous agricultural landscape using PlanetScope imagery while checking for variability among their Normalized Difference Vegetation Index (NDVI) temporal signatures. We adopt an object-based image analysis to generate image-objects and then extract statistical information of PlanetScope spectral bands and vegetation indices as input information for classification. The exploratory analysis focused on the double crop class and calculated the distance between NDVI temporal signatures of paired land parcels. We applied an unsupervised clustering technique along with Random Forest algorithm based on multiple tests to classify and analyse gains and losses in accuracies produced by these approaches. Our results showed that the initial clustering method outperformed the non-clustered classification of LULC in overall accuracy measures. The exploratory analysis demonstrated that double crops might present high intra-class variability and diverse crop calendars for neighbour land parcels. The accuracies achieved represent promising opportunities for the sufficiently accurate classification of such areas, and the knowledge of the intra-class variability allows the analyst to infer the temporal dynamics of crop fields. We reinforce that further work could assess other types of classifiers, especially in areas with a large number of crop types and distinct management practices.

Challenges of Retrieving LULC Information in Rural-Forest Mosaic Landscapes Using Random Forest Technique

Land use and land cover (LULC) information plays a crucial role in determining the trend of the global carbon cycle in various fields, such as urban land planning, agriculture, rural management, and sustainable development, and serves as an up-to-date indicator of forest changes. Accurate and reliable LULC information is needed to address the detailed changes in conservation-based and development-based classes. This study integrates Sentinel-2 multispectral surface reflectance and vegetation indices, and lidar-based canopy height and slope to generate a random forest model for 3-level LULC classification. The challenges for LULC classification by RF approach are discussed by comparing it with the SVM model. To summarize, the RF model achieved an overall accuracy (OA) of 0.79 and a macro F1-score of 0.72 for the Level-III classification. In contrast, the SVM model outperformed the RF model by 0.04 and 0.09 in OA and macro F1-score, respectively. The accuracy difference increased to 0.89 vs. 0.96 for OA and 0.79 vs. 0.91 for macro F1-score for the Level-I classification. The mapping reliability of the RF model for different classes with nearly identical features was challenging with regard to precision and recall measures which are both inconsistent in the RF model. Therefore, further research is needed to close the knowledge gap associated with reliable and high thematic LULC mapping using the RF classifier.

Land use and land cover dynamics: Implications for thermal stress and energy demands

This study examined the interaction between land use and land cover (LULC) dynamics, trend and thermal stress distribution using the universal thermal comfort index (UTCI) and different LULC classifications under two Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (i.e., SSP 370 and 585) climate and land use scenarios for the historical (1959–2014) and future period (2045–2100). The moderate to strong cold stress in the annual and winter climatology in the midlatitudes was replaced by no thermal stress in the summer, while the summertime ranged from moderate to strong heat stress. A negative correlation was observed between thermal stress and southern hemispheric primary forests. Perennial croplands had the most dynamic changes in intensity during the historical period. Primary and secondary forests had an active influence on global thermal stress. Areas in the tropics recording moderate heat stress coincided with secondary nonforest, pastureland, and annual cropland expansions. The conversion of forest to range land and croplands and the subsequent negative forest trends increased the severity of thermal stress. The future projection showed intense thermal stress; however, the SSP-585 signals were more potent. As a result, cooling demands will rise, and heating demands will decline, yet, improved thermal comfort necessitates a higher cooling capacity, especially in the summer. Thermal stress may make it difficult for many cooling systems to meet people's energy demands. These could be a driving factor in shaping better land use policies, improving energy demand preparedness, and elucidating the potentially severe impacts of thermal stress.

Simulating future land-use within the uThukela and uMngeni catchments in KwaZulu-Natal

Due to anthropogenic activities, the earth's surface is consistently being altered. These alterations take the form of Land-use/cover change (LULCC), which is a fundamental driver of global, regional and local environmental change. LULCC studies have become pivotal in supplementing our understanding and observations of environmental change. However, understanding the past and present spatial-temporal variability of LULCC characteristics and their link to future land-use/cover trajectories at a catchment scale is limited, particularly in Southern Africa. To address this limitation, this study simulated future land-use change utilising a spatially distributed, empirical land-use modelling approach for the uThukela and uMngeni catchments in the KwaZulu-Natal province of South Africa. The CA-Markov model, a popular and frequently utilised model employed in land-use and land-cover (LULC) predictive modelling, was selected to simulate LULCC conjointly with Geographic Information Systems (GIS) techniques. The obtained kappa values (Kstandard, Klocation and Kno) achieved during the validation were all above 80%, thus indicating the model's reliability and capability to successfully predict future LULC in the study sites. Future projections indicated that both study areas are anticipated to experience anthropogenic induced LULCC, which further fragments the landscape configuration, functionality and ecological stability. Historical analysis of LULCC between 1990 and 2018, in the study catchments revealed considerable declines in the areas under grassland and indigenous forest, while the areas under cultivated land, commercial forestry and urban LULC classes increased. Future LULC projections showed that urban and agriculture land-uses increased significantly, with natural land-use classes such as grassland, other vegetation and indigenous forest declining in spatial extent across both. With an understanding of the extent of projected LULCC by 2030 within both catchments, proactive planning and management within the framework of sustainable land-use planning and water resource management in the respective catchments can be undertaken. Moreover, the results of the land-use modelling study can be used to infill historical data gaps, support effective land-use planning and provide a means to evaluate the impacts of different future development pathways.