Monitoring Land Cover Change Research Articles

A fast hybrid approach for continuous land cover change monitoring and semantic segmentation using satellite time series

Land cover change detection and classification, including both inter-class changes (land cover conversion, LCC) and intra-class changes (land cover modification, LCM), is critical for understanding the Earth’s dynamic processes and promoting sustainability. However, previous studies have predominantly focused on LCC, with less emphasis on LCM. Land cover classification remains challenging, and its mapping results are often affected by salt and pepper noise. Here, we propose a hybrid approach for continuous change detection and classification of LCC and LCM using Jinchang City, China, as a case study. Firstly, we combined the Continuous Change Detection and Classification (CCDC) and the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) algorithms to identify LCC and LCM using all available Landsat time series (TS) data from 2000 to 2020. Then, the harmonic regression coefficients and RMSE values derived from CCDC (hereafter called CCDC features) were fed into the DCNN model for LCC classification. Our findings indicate: (1) For LCC and LCM accuracy assessment, the CCDC and BEAST ensemble achieved a spatial F1 score of 82.7% and an average temporal F1 score of 79.7%. (2) In LCC classification, the DCNN model with CCDC features, particularly DeepLabV3+, outperformed the pixel-based XGBoost and other multi-year land cover products, with frequency-weighted intersection over union (FWIoU), overall accuracy, and Kappa scores of 88.7%, 94%, and 0.87, respectively. (3) Seasonal LCM showed a more concentrated distribution than trend LCM. (4) In Jinchang City, LCM larger than LCC in area, and grassland and cultivated land are the most distributed. Our approach can be contributed to wall-to-wall land surface monitoring and enhance land management capabilities.

The effects of land cover transition and its patch mosaics on soil erosion using geospatial technology in South Wollo, Ethiopia

Soil erosion has been a significant threat to agricultural productivity, dam sustainability, and ecosystem services in the highlands of Ethiopia. Despite some watershed-specific research on this problem, the spatiotemporal distribution of soil erosion risks is still rarely assessed in South Wollo. This study aims to monitor land cover changes, analyze landscape mosaics, and estimate soil loss in South Wollo from 1990 to 2020. The Revised Universal Soil Loss Equation (RUSLE) was utilized to estimate soil loss. Key informant interviews and field observations were conducted to gain further insights into land cover change and soil erosion. The results showed that agricultural land use was dominant, covering 50.75 % of the study area in 1990 and it continued to expand at an annual rate of 1.73 %, primarily on shrubland and grassland. The landscape structure was more heterogeneous in the north compared to the central and southern areas. In 1990, the mean soil loss was 21.13 t ha-1yr−1, totaling 41.53 million tons. By 2020, These figures had increased to 28.97 t ha-1yr−1 and 49.86 million tons, respectively. Areas experiencing very severe soil erosion (>50 t ha-1yr−1) expanded from 10.81 % (1990) to 13.57 % (2020). The magnitude of soil loss also differed among land cover types, with the highest rates observed in bare land (>85 t ha-1yr−1), followed by agricultural land (>30 t ha-1yr−1). Soil loss factors and landscape metrics exhibited a significant correlation with soil loss, though the strength and direction of these interactions varied. Land cover change and its associated soil erosion were primarily exacerbated by policy factors, including tenure insecurity and disregard for farmers’ indigenous conservation knowledge. This study offers valuable insights into the trends of land cover transitions, landscape structure, and soil loss. It will help guide sustainable land management, aimed at increasing green legacy and reducing soil loss.

Revolutionizing Land Cover Analysis: A Systematic Review of Geospatial Intelligence with Classification and Segmentation

In today’s fast-paced technological landscape, artificial intelligence (AI) is revolutionizing industries, with geospatial analysis standing at the forefront of this transformation. The process of land cover classification, which involves categorizing different types of land surfaces—such as forests, urban areas, water bodies, and agricultural fields—has traditionally been plagued by inconsistencies and inaccuracies. These shortcomings have led to a variety of pressing real-world issues. Misclassified land cover data can result in the inefficient allocation of resources, where critical areas may be overlooked while less urgent regions receive attention. Additionally, the failure to accurately monitor land cover changes can allow illegal activities, such as deforestation, to go unnoticed, resulting in severe environmental degradation. Similarly, unmonitored topographical changes, like unauthorized construction projects, can significantly alter landscapes without regulatory oversight, posing risks to both the environment and public safety. Unchecked forest fires, exacerbated by delayed detection due to poor land cover classification, can spread rapidly, causing widespread damage. Furthermore, inaccurate monitoring of border fences can lead to security vulnerabilities and geopolitical tensions. Collectively, these issues contribute to the escalating challenges of climate change and urbanization, highlighting the critical need for more precise and reliable land cover classification methods. In response to these challenges, our study seeks to explore the potential of advanced machine learning (ML) techniques to revolutionize land cover classification. We leverage publicly available geospatial datasets, specifically EuroSAT and DeepGlobe, which provide comprehensive satellite imagery data across various regions. The focus of our research is on two primary tasks: Image Classification and Semantic Segmentation. Image Classification involves categorizing entire satellite images into specific land cover classes, providing a broad overview of the landscape. In contrast, Semantic Segmentation is a more granular approach that labels each pixel in an image according to its land cover class, offering detailed insights into the spatial distribution of different land types. To conduct a thorough analysis, we evaluate the performance of several cutting-edge models in these tasks. For Semantic Segmentation, we employ Meta AI’s Segment Anything Model (SAM), which is recognized for its ability to segment objects within images with high precision, and the U-Net architecture, a model that has been widely used in medical image analysis and has proven effective in various segmentation tasks. For Image Classification, we use the VGG and ResNet models, both of which are highly regarded in the field of computer vision for their capacity to extract detailed features from images and classify them with high accuracy. The primary objective of our research is to assess how these models perform when applied to land cover classification tasks and to identify their strengths and weaknesses in this specific context. By analyzing their performance, we aim to provide valuable insights that can guide future research efforts and help improve the accuracy and reliability of land cover classification methods. Additionally, our study seeks to highlight new opportunities for enhancing the monitoring and management of land resources, which is crucial for addressing the environmental and urbanization challenges that the world faces today. Our research aspires to contribute to the geospatial field by offering practical recommendations for utilizing machine learning techniques to improve land cover classification. By addressing the limitations of current methods and proposing more effective solutions, we hope to support the development of tools that are capable of accurately monitoring land cover changes and responding to the complex environmental and societal challenges of our time.

Spatiotemporal changes and driving factors of alpine land cover in Tianshan world natural heritage sites

Alpine natural heritage sites hold significant value due to their unique global resources. Studying land cover changes in these areas is crucial for maintaining and preserving multiple their values. This study takes Kalajun-Kuerdening, one of the components of Xinjiang Tianshan World Natural Heritage Site, as an example to analyze land cover changes and their driving factors in alpine heritage sites. Highlights include: (1) Between 1994 and 2023, Forest and Grassland increased by 55.96 km2 and 18.16 km2, with notable forest growth from 2007 to 2017. Trends in Forest changes align with forest protection policies, and a substantial amount of Bareland converted to Grassland indicates an increase in vegetation cover. (2) Elevation, precipitation, temperature, and evapotranspiration are key drivers of land cover changes, as validated by Random Forest algorithm and Geodetector model. (3) Favorable conditions for Grassland to Forest transition include annual precipitation between 275 and 375 mm, annual temperature between −2 and 3 °C, annual evapotranspiration between 580 and 750 mm, elevation between 1800 and 2600 m, and aspect between 0 to 110° and 220 to 259.9°. Continuous monitoring of land cover changes and their driving factors in mountain heritage sites contributes to the protection of the ecological environment and provides data and information support for addressing climate change, resource management, and policy making.

Assessment of Machine Learning Algorithms for Land Cover Classification in a Complex Mountainous Landscape

Mapping land cover (LC) in mountainous regions, such as the Gilgit-Baltistan (GB) area of Pakistan, presents significant challenges due to complex terrain, limited data availability, and accessibility constraints. This study addresses these challenges by developing a robust, data-driven approach to classify LC using high-resolution Sentinel-2 (S-2) satellite imagery from 2019 within Google Earth Engine (GEE). The research evaluated the performance of various machine learning (ML) algorithms, including classification and regression tree (CART), maximum entropy (gmoMaxEnt), minimum distance (minDistance), support vector machine (SVM), and random forest (RF), without extensive hyperparameter tuning. Additionally, ten different scenarios based on various band combinations of S-2 data were used as input for running the ML models. The LC classification was performed using 2759 sample points, with 70% for training and 30% for validation. The results indicate that the RF algorithm outperformed all other classifiers under scenario S1 (using 10 bands), achieving an overall accuracy (OA) of 0.79 and a kappa coefficient of 0.76. The final RF-based LC mapping shows the following percentage distribution: barren land (46.7%), snow cover (22.9%), glacier (7.9%), grasses (7.2%), water (4.7%), wetland (2.9%), built-up (2.7%), agriculture (1.9%), and forest (1.2%). It is suggested that the best identified RF classifier within the GEE environment should be used for advanced multi-source data image classification with hyperparameter tuning to increase OA. Additionally, it is suggested to build the capacity of various stakeholders in GB for better monitoring of LC changes and resource management using geospatial big data.

Oued Lakhdar watershed (Morocco), monitoring land use/cover changes: remote sensing and GIS approach

ABSTRACT Landsat is a series of Earth-observation satellites jointly managed by NASA and the United States Geological Survey (USGS) that capture multispectral images of the Earth’s surface dating back to the 1970s, providing a valuable tool for tracking changes in land cover over time. In this study, Landsat satellite data were combined with remote sensing and Geographic Information System (GIS) technologies to monitor land cover changes in the Oued Lakhdar watershed, which covers an area of 1,638 km2. Landsat satellite data for the years 1987, 1994, 2004, 2014, and 2021 were used, and the maximum likelihood method was applied in ArcGIS software version 10.4.1 to classify land components. The classification was validated using total accuracy, the kappa index, Google Earth, and terrain knowledge. The results show remarkable changes in land cover, with an increase in bare soil and irrigated crops between 1987 and 2021, especially in the last six years, totaling 0.672 km2/yr and 15.028 km2/yr of the study area, respectively, and a decrease in water bodies and dense forests, totaling −0.941 km2/yr and −30.046 km2/yr, respectively. Water retention coefficient analysis indicates that the watershed is anthropized and has a low retention capacity. This study provides valuable information for land use planning and management in the Oued Lakhdar watershed (Morocco).

Using the MSFNet Model to Explore the Temporal and Spatial Evolution of Crop Planting Area and Increase Its Contribution to the Application of UAV Remote Sensing

Remote sensing technology can be used to monitor changes in crop planting areas to guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote sensing technology is efficient, accurate, and flexible, which can quickly collect and transmit high-resolution data in real time to help precision agriculture management. It is widely used in crop monitoring, yield prediction, and irrigation management. However, the application of remote sensing technology faces challenges such as a high imbalance of land cover types, scarcity of labeled samples, and complex and changeable coverage types of long-term remote sensing images, which have brought great limitations to the monitoring of cultivated land cover changes. In order to solve the abovementioned problems, this paper proposed a multi-scale fusion network (MSFNet) model based on multi-scale input and feature fusion based on cultivated land time series images, and further combined MSFNet and Model Diagnostic Meta Learning (MAML) methods, using particle swarm optimization (PSO) to optimize the parameters of the neural network. The proposed method is applied to remote sensing of crops and tomatoes. The experimental results showed that the average accuracy, F1-score, and average IoU of the MSFNet model optimized by PSO + MAML (PSML) were 94.902%, 91.901%, and 90.557%, respectively. Compared with other schemes such as U-Net, PSPNet, and DeepLabv3+, this method has a better effect in solving the problem of complex ground objects and the scarcity of remote sensing image samples and provides technical support for the application of subsequent agricultural UAV remote sensing technology. The study found that the change in different crop planting areas was closely related to different climatic conditions and regional policies, which helps to guide the management of cultivated land use and provides technical support for the realization of regional carbon neutrality.

Twenty Years of Land Accounts in Europe

Land use and its change impact food security, carbon cycling, biodiversity, and, hence, the condition of ecosystems to mitigate and adapt to climate change, support economic prosperity, and human well-being. To support and guide policy actions between the economy and the environment, harmonized time series datasets, transparent methodologies, and easily interpretable statistics are needed. Therefore, monitoring of the function and condition of lands and their change, along with properly agreed methodologies and freely accessible data, are essential. The Copernicus Land Monitoring Service has produced over 20 years of Corine Land Cover datasets for 39 countries in Europe, which allows continental-wide harmonized and comparable monitoring and accounting of land cover and land use change at a high thematic resolution and in a long time series (2000–2018). With the upcoming 2024 update, the time series will reach a unique product worldwide in terms of time series length, spatial resolution, extent, and thematic detail, enabling policymakers and the scientific community to address the main anthropogenic drivers of land and ecosystem degradation. This paper describes a unified approach for producing continental-wide land accounts that aligns with internationally agreed-upon standards for measuring the environment and its relationship with the economy. Furthermore, the study provides a harmonized time series of geospatial data for deriving land accounts and provides statistics of land cover and land use status and changes for a twenty-year period. All geospatial data and statistics presented in this paper are freely accessible and downloadable to serve other studies.

Land cover object change monitoring and environmental suitability confirmation in Cat Ba Biosphere Reserve of Vietnam

ABSTRACT The study examined the core, buffer, and transition zones within the Cat Ba Biosphere Reserve in Vietnam to assess changes in five land cover categories (water surface, cropland, vacant land, residential land, and forestland) between 2018 and 2022. Additionally, we aimed to evaluate water quality using three indicators (normalized difference turbidity index, normalized difference chlorophyll index, total suspended solids) and air quality using four indicators (nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone) for the 2022 data. Land cover change analysis and monitoring of water and air quality indicators were conducted using the Google Earth Engine platform, employing Sentinel-2 imagery for land cover and water quality assessment and Sentinel-5P images for air quality assessment. The findings revealed correlations between changes in land cover and water and air quality indicators within the Cat Ba Biosphere Reserve. While occasional instances of values surpassing permissible limits were noted, the average values over the 12-month period in 2022 indicated that water and air quality indicators remained within acceptable thresholds. These results suggest effective preservation efforts in the Cat Ba Biosphere Reserve, indicating no significant signs of degradation.

Correction to: Monitoring and prediction of land use land cover change of Chittagong Metropolitan City by CA-ANN model

Correction to: Monitoring and prediction of land use land cover change of Chittagong Metropolitan City by CA-ANN model

A Deep Learning Based Platform for Remote Sensing Images Change Detection Integrating Crowdsourcing and Active Learning.

Remote sensing images change detection technology has become a popular tool for monitoring the change type, area, and distribution of land cover, including cultivated land, forest land, photovoltaic, roads, and buildings. However, traditional methods which rely on pre-annotation and on-site verification are time-consuming and challenging to meet timeliness requirements. With the emergence of artificial intelligence, this paper proposes an automatic change detection model and a crowdsourcing collaborative framework. The framework uses human-in-the-loop technology and an active learning approach to transform the manual interpretation method into a human-machine collaborative intelligent interpretation method. This low-cost and high-efficiency framework aims to solve the problem of weak model generalization caused by the lack of annotated data in change detection. The proposed framework can effectively incorporate expert domain knowledge and reduce the cost of data annotation while improving model performance. To ensure data quality, a crowdsourcing quality control model is constructed to evaluate the annotation qualification of the annotators and check their annotation results. Furthermore, a prototype of automatic detection and crowdsourcing collaborative annotation management platform is developed, which integrates annotation, crowdsourcing quality control, and change detection applications. The proposed framework and platform can help natural resource departments monitor land cover changes efficiently and effectively.

Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon.

Monitoring is essential to ensure that environmental goals are being achieved, including those of sustainable agriculture. Growing interest in environmental monitoring provides an opportunity to improve monitoring practices. Approaches that directly monitor land cover change and biodiversity annually by coupling the wall-to-wall coverage from remote sensing and the site-specific community composition from environmental DNA (eDNA) can provide timely, relevant results for parties interested in the success of sustainable agricultural practices. To ensure that the measured impacts are due to the environmental projects and not exogenous factors, sites where projects have been implemented should be benchmarked against counterfactuals (no project) and control (natural habitat) sites. Results can then be used to calculate diverse sets of indicators customized to monitor different projects. Here, we report on our experience developing and applying one such approach to assess the impact of shaded cocoa projects implemented by the Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA) near São Félix do Xingu, in Pará, Brazil. We used the Continuous Degradation Detection (CODED) and LandTrendr algorithms to create a remote sensing-based assessment of forest disturbance and regeneration, estimate carbon sequestration, and changes in essential habitats. We coupled these remote sensing methods with eDNA analyses using arthropod-targeted primers by collecting soil samples from intervention and counterfactual pasture field sites and a control secondary forest. We used a custom set of indicators from the pilot application of a coupled monitoring framework called TerraBio. Our results suggest that, due to IMAFLORA's shaded cocoa projects, over 400 acres were restored in the intervention area and the community composition of arthropods in shaded cocoa is closer to second-growth forests than that of pastures. In reviewing the coupled approach, we found multiple aspects worked well, and we conclude by presenting multiple lessons learned.

Accuracy Assessment of Land Use/Land Cover Indices for Al-Rusafa in Baghdad Governorate by Remote Sensing Technology and GIS

The management and planning of natural and artificial resources depend on accurately monitoring land cover changes. Land cover change mapping and monitoring used to require expensive field surveys. Remote sensing is cheaper and more practical for mapping land use and cover changes. The Tigris River divides the Iraqi capital, Baghdad, into two parts: Karkh and Rusafa. Al-Rusafa was selected as a study area for current research, which has had rapid population and urban growth in recent decades. The current research applies the support vector machine technique to supervised LU/LC maps’ classification into barren regions, water bodies, vegetation cover and built-up regions. Spectral indicators were calculated: Enhanced Vegetation Index, Modified Normalized Difference Water Index, Normalized Built-Up Area Index, Dry Bareness Index in addition to calculating the accuracy assessment and Kappa coefficient. Using the Landsat 9 satellite image, ArcGIS 10.8 and Envi5.3 software were used to analyze and evaluate the results and field points observed by GPS devices. The results showed that the SVM classification algorithm accurately revealed the categories of LU/LC, where the classification accuracy reached 95%, and that the arid lands covered most of the study area 848.864 km2 and water bodies 76.747 km2, the vegetation and the built-up regions 466.459 km2 and 439.077 km2, respectively. The spectral indices showed slightly different areas of barren lands (DBSI 752.589 km2, 93% accuracy), vegetation (EVI 423.651 km2, 96% accuracy), and water bodies (MNDWI 73.187 km2, 98% accuracy) and built-up areas (NBAI 501,731 km2, 90%accuracy). The Support Vector Machine method outperforms other classification methods, and the spectral indicators employed in this work are useful and dependable for extracting each LU/LC category. In conclusion, Landsat 9 satellite data can reliably and swiftly detect ground cover.

Deep Learning based Time Series Classification Model for Monitoring Land Cover Changes in Tropical High Forests

Deep Learning based Time Series Classification Model for Monitoring Land Cover Changes in Tropical High Forests

Application of Satellite Images and Artificial Intelligence to Monitor Land Cover Changes in Hanoi Area During 2013-2023 Period

Artificial intelligence (AI) and remote sensing technology have now increasingly improved their efficiency and reliability in monitoring the changes in land cover. With the amendment of the Vietnamese Law on Land in 2013 and the administrative boundary expansion of Hanoi, Hanoi experiences significant changes in land use and land cover for the last ten years. To monitor the actual land use changes in the area, this study used the Random Forest (RF) machine learning algorithm to classify the basic land covers, monitor, and analyze the spatial variation of land use and land cover in the 2013 to 2023 period. The study findings indicate a relatively high rate of expansion of construction zone area and a decrease in land cover related to water bodies and vegetated area. Water bodies decrease by an average of 0.8% annually, whereas the construction zone area increased by 7% of the total area.

Effect of Urbanization Through Land Coverage Classification

AbstractLand cover changes from rural or natural habitats to impervious surfaces that support various human activities are called urbanization. The evaluation of policy alternatives for future expansion and the promotion of sustainable urban development therefore place great emphasis on the mapping, analysis, and monitoring of land cover changes in metropolitan environments. By assessing changes in relevant environmental indicators at a range of scales, from local to regional, this study seeks to determine the rate of urbanization and analyze its impact on the environment in and around two key cities in Tamilnadu. Chennai and Madurai are the urban areas under investigation. The results are based on classifications of medium to high spatial resolution optical satellite images from 1995 to 2020. Various classification approaches with combinations of spectral, shape, and texture input information were combined to achieve high classification accuracy. Based on the categories, environmental indicators were created and used to calculate the impact of urbanization on the environment. The findings show that each research area experienced varying degrees of urban expansion and environmental impact. Urban areas grew tenfold between 1995 and 2015, according to a study comparing Chennai and Madurai. To evaluate the spatiotemporal dynamics of urbanization and its environmental impact in various metropolitan regions, this research shows the value of combining urban and environmental indicators with remote sensing data.

Monitoring of wetland cover changes in protected areas to trade-offs between ecological conservation and food security: A case study from the Dongting Lake, China

The United Nations' Sustainable Development Goals (SDGs) for 2030 underscore the urgency of “eradicating hunger and ensuring food security” (SDG 2: Zero Hunger) and the critical need to “cease and reverse land degradation and biodiversity decline” (SDG 15: Life on Land). The Dongting Lake area in China, serving as a significant wetland nature reserve and grain production zone, is increasingly confronted with complex, interdependent trade-offs between agricultural cultivation and the ecological and environmental preservation of wetlands (SDG 2 and 15). To analyze the trade-off between conservation and food security, it is crucial to rely on long-term, continuous monitoring of land cover changes in the lake area. However, the absence of reliable optical remote sensing has posed a significant challenge due to the prevalence of cloudy and rainy conditions. Our study begins by integrating long-term Landsat and MODIS data using the GF-SG fusion algorithm to generate 8-day NDVI data with a spatial resolution of 30 m. This analysis focuses on the areas outside the embankment of Dongting Lake (referred to as O-DTL) from 2003 to 2020. We then employ a pixel-connected sample migration method to facilitate automated sampling of land cover types and a random forest classification algorithm to analyze long-term dynamic changes. Finally, by combining meteorological and social data, we examine the temporal patterns of land cover changes in the study area and identify the underlying factors influencing them. The results indicate a trade-off between the fluctuation of cropland and forest area from 2003 to 2020. Water body areas have exhibited a significant decrease, while impervious surface areas have consistently increased annually. Impervious surface areas have experienced a substantial increase of approximately 70%, with almost 90% of this expansion resulting from the conversion of cropland. Under the premise of maintaining a stable area for food cultivation, the land consumed by urban expansion eventually encroached on the wetland water bodies of the reserve. The reduction in wetland water area in the O-DTL can be primarily attributed to agricultural cultivation, while urban expansion and urbanization have indirectly contributed to the decrease. This study provides a case in which prioritizing food security (SDG 2) takes precedence over ecological conservation (SDG 15), highlighting its significant implications for guiding subsequent development in the Dongting Lake area. This finding is crucial for minimizing unintended consequences and maximizing co-benefits between sustainable development goals.

Slums Evolution and Sustainable Urban Growth: A Comparative Study of Makoko and Badia-East Areas in Lagos City

This research addresses the evolution of slums in two specific areas of Lagos City, a consequence of rapid urbanization in developing economies. The study aims to analyze the key characteristics of slums’ evolution while analyzing mathematical spatial changes within the Badia-East and Makoko slum areas in two decades, focusing on promoting sustainable urban growth. The integration of Remote Sensing (RS) technology and Geographic Information System (GIS) tools has dramatically facilitated the monitoring of Land Use Land Cover (LULC) changes over two decades. This research used Landsat imagery acquired in 2000, 2010, and 2020 to meet our research aims. The research applied supervised classification and the Normalized Difference Built-up Index (NDBI) for image mapping and mathematical-based analysis. Research created a spatial framework using grid-cell maps, performed change detection, and generated detailed maps to support our analysis. A comparative analysis was also performed in the selected areas with a different history in slum management systems. The findings reveal that both Makoko and Badia-East exhibit slum-like characteristics. Makoko witnessed a significant increase in informal built-up expansion of 27.6% and 7.7% between 2000 and 2010 and between 2010 and 2020, respectively. These changes converted 34.65 hectares of non-built-up land in Makoko into built-up areas. Conversely, Badia-East reported an increase in built-up areas only between 2000 and 2010, which amounted to 8.5%. However, the government’s intervention caused a decrease of 12.7% in built-up areas of Badia-East between 2010 and 2020. The study underscores the urgent need for slum clearance and upgrading initiatives in Makoko, like those implemented in Badia-East between 2013 and 2017. The conclusion drawn from the study of Makoko’s informal built-up areas is that it is causing adverse effects on human life and the environment. The expansion has resulted in an increase in air and water pollution, which is having adverse effects. Therefore, the study recommends upgrading and clearing out Makoko and suggests taking lessons from the successful experience in Badia-East. The study also highlights the importance of prioritizing community needs and voices to ensure efficient resource utilization while safeguarding the well-being of future generations.

Investigating the efficiency and capabilities of UAVs and Convolutional Neural Networks in the field of remote sensing as a land classification tool

The study aimed to determine the efficacy and capabilities of using high-resolution aerial imagery and a convolutional neural network (CNN) to identify plant species and monitor land cover and land change in the context of remote sensing. The full capabilities of a CNN were examined, including testing whether the platform could be used for land cover and the evaluation of land change over time. An unmanned aerial vehicle (UAV) was used to collect the aerial data of the study area. The CNN was encoded and operated in RStudio, while digitised data from the input imagery were used by the programme as training and validation data. The object in this respect was to learn about the relevant features of the landscape, and thereafter to classify the Opuntia invasive plant species. Accuracy assessments were carried out on the results to test the efficacy of the aerial imagery in terms of its accuracy and reliability. The classification achieved an overall accuracy of 93%, while the kappa coefficient score was 0.86. CNN was also able to predict the land coverage area of Opuntia to be within four percent (4%) of the ground truthing data. A change in land cover over time was detected by the programme after the manual clearing of the plant had been undertaken. This research has determined that the use of a CNN in remote sensing is a very powerful tool for supervised image classifications. It can be used for monitoring land cover in that it is able to accurately estimate the spatial distribution of plant species and to monitor the growth or decline in the species over time. As such, it is an efficient methodology and its use in remote sensing could be extended.

E-Agriculture Planning Tool for Supporting Smallholder Cocoa Intensification Using Remotely Sensed Data

Remote sensing approaches are often used to monitor land cover change. However, the small physical size (about 1–2 hectare area) of smallholder orchards and the cultivation of cocoa (Theobroma cocoa L.) under shade trees make the use of many popular satellite sensors inefficient to distinguish cocoa orchards from forest areas. Nevertheless, high-resolution satellite imagery combined with novel signal extraction methods facilitates the differentiation of coconut palms (Cocos nucifera L.) from forests. Cocoa grows well under established coconut shade, and underplanting provides a viable opportunity to intensify production and meet demand and government targets. In this study, we combined grey-level co-occurrence matrix (GLCM) textural features and vegetation indices from Sentinel datasets to evaluate the sustainability of cocoa expansion given land suitability for agriculture and soil capability classes. Additionally, it sheds light on underexploited areas with agricultural potential. The mapping of areas where cocoa smallholder orchards already exist or can be grown involved three main components. Firstly, the use of the fine-resolution C-band synthetic aperture radar and multispectral instruments from Sentinel-1 and Sentinel-2 satellites, respectively. Secondly, the processing of imagery (Sentinel-1 and Sentinel-2) for feature extraction using 22 variables. Lastly, fitting a random forest (RF) model to detect and distinguish potential cocoa orchards from non-cocoa areas. The RF classification scheme differentiated cocoa (for consistency, the coconut–cocoa areas in this manuscript will be referred to as cocoa regions or orchards) and non-cocoa regions with 97 percent overall accuracy and over 90 percent producer’s and user’s accuracies for the cocoa regions when trained on a combination of spectral indices and GLCM textural feature sets. The top five variables that contributed the most to the model were the red band (B4), red edge curve index (RECI), blue band (B2), near-infrared (NIR) entropy, and enhanced vegetation index (EVI), indicating the importance of vegetation indices and entropy values. By comparing the classified map created in this study with the soil and land capability legacy information of Bougainville, we observed that potential cocoa regions are already rated as highly suitable. This implies that cocoa expansion has reached one of many intersecting limits, including land suitability, political, social, economic, educational, health, labour, and infrastructure. Understanding how these interactions limit cocoa productivity at present will inform further sustainable growth. The tool provides inexpensive and rapid monitoring of land use, suitable for a sustainable planning framework that supports responsible agricultural land use management. The study developed a heuristic tool for monitoring land cover changes for cocoa production, informing sustainable development that balances the needs and aspirations of the government and farming communities with the protection of the environment.