Trees play a vital role in the urban ecosystem’s carbon sequestration; consequently, an accurate evaluation of tree carbon storage is critical for climate mitigation, sustainability planning, and urban ecosystem management. Traditional field-based methods are labor-intensive and struggle with scalability. Single-sensor remote sensing approaches lack the structural and spectral depth required, which is critical in the context of complex urban environments to combat climate change. There is no comprehensive review on how the fusion of Synthetic Aperture Radar (SAR) with optical, LIDAR, and UAV data, combined with machine learning, can improve SAR-based tree carbon estimation. This systematic review evaluates studies on estimating tree carbon in natural and urban environments. A keyword search was conducted using three databases: Google Scholar, Scopus, and Web of Science, for articles published between 2004 and 2024. A total of 842 articles were screened using defined eligibility criteria, and 61 articles were included for full-text screening. The review analyzes publication trends, geographical distribution of studies, remote sensor types, and data fusion strategies across different environments, as well as the data sources used for tree carbon estimation and the machine learning methods applied in carbon estimation. The review indicates that, despite the availability of the Sentinel-1 dataset, an open-access SAR, only few studies have utilized its potential for estimating carbon stock in urban trees. This review underscores that fusing SAR with optical, LiDAR, and other remote sensing data enables scalable and accurate tree carbon estimation. Such precision is essential for sustainable urban ecosystem management and climate planning.

The worldwide shortage of fresh water is a critical issue, with two-thirds of the world’s freshwater being inaccessible, as it is locked up in frozen glaciers. Additionally, arid areas such as South Africa are experiencing increased water scarcity, with regions such as Cape Town having already announced “day zero”, the day when the city’s dams will run out of water. Fog water collection provides a sustainable and alternative source of fresh water. Nevertheless, existing methods for identifying suitable locations rely on manual and trial-based approaches. This study focuses on finding suitable locations for fog harvesting in Cape Town via geographic information system (GIS)-based multicriteria decision analysis. To accomplish this, relevant factors for fog harvesting were identified in the literature and then transformed into spatial data layers. Next, weights were assigned to criteria layers via the analytical hierarchy process method, ultimately creating a final suitability map through a weighted overlay of these criteria layers. The findings of this study indicated that regions near coastlines, with low temperatures and strong winds, elevations above 1000 m or below 200 m, and steep slopes facing the ocean are the most favourable locations for harvesting fog water. This approach could be replicated in other regions, but caution is necessary when determining criteria and thresholds because of the localised nature of fog.

The correction of digital elevation models (DEMs) can be achieved using a variety of techniques. Machine learning and statistical methods are broadly applicable to a variety of DEM correction case studies in different landscapes. However, a literature survey did not reveal any research that compared the effectiveness or performance of both methods. In this study, we comparatively evaluate three gradient boosted decision trees (XGBoost, LightGBM and CatBoost) and multiple linear regression for the correction of two publicly available global DEMs: Copernicus GLO-30 and ALOS World 3D (AW3D) in Cape Town, South Africa. The training datasets are comprised of eleven predictor variables including elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector ruggedness measure, percentage bare ground, urban footprints and percentage forest cover as an indicator of the overland forest distribution. The target variable (elevation error) was derived with respect to highly accurate airborne LiDAR. The results presented in this study represent urban/industrial and grassland/shrubland/dense bush landscapes. Although the accuracy of the original DEMs had been degraded by several anomalies, the corrections improved the vertical accuracy across vast areas of the landscape. In the urban/industrial and grassland/shrubland landscapes, the reduction in the root mean square error (RMSE) of the original AW3D DEM was greater than 70%, after correction. The corrections improved the accuracy of Copernicus DEM, e.g., > 44% RMSE reduction in the urban area and >32% RMSE reduction in the grassland/shrubland landscape. Generally, the gradient boosted decision trees outperformed multiple linear regression in most of the tests.

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The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artifacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30m DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The correction achieved significant accuracy gains which are competitive with other proposed methods. The root mean square error (RMSE) of Copernicus DEM improved by 46 to 53% while the RMSE of AW3D DEM improved by 72 to 73%. These results showcase the potential of gradient boosted trees for enhancing the quality of DEMs, and for improved hydrological modelling in urban catchments.

Validation studies of global Digital Elevation Models (DEMs) in the existing literature are limited by the diversity and spread of landscapes, terrain types considered and sparseness of groundtruth. Moreover, there are knowledge gaps on the accuracy variations in rugged and complex landscapes, and previous studies have often not relied on robust internal and external validation measures. Thus, there is still only partial understanding and limited perspective of the reliability and adequacy of global DEMs for several applications. In this study, we utilize a dense spread of LiDAR groundtruth to assess the vertical accuracies of four medium-resolution, readily available, free-access and global coverage 1 arc-second (30 m) DEMs: NASADEM, ASTER GDEM, Copernicus GLO-30, and ALOS World 3D (AW3D). The assessment is carried out at landscapes spread across Cape Town, Southern Africa (urban/industrial, agricultural, mountain, peninsula and grassland/shrubland) and forested national parks in Gabon, Central Africa (low-relief tropical rainforest and high-relief tropical rainforest). The statistical analysis is based on robust accuracy metrics that cater for normal and non-normal elevation error distribution, and error ranking. In Cape Town, Copernicus DEM generally had the least vertical error with an overall Mean Error (ME) of 0.82 m and Root Mean Square Error (RMSE) of 2.34 m while ASTER DEM had the poorest performance. However, ASTER GDEM and NASADEM performed better in the low-relief and high-relief tropical forests of Gabon. Generally, the DEM errors have a moderate to high positive correlation in forests, and a low to moderate positive correlation in mountains and urban areas. Copernicus DEM showed superior vertical accuracy in forests with less than 40% tree cover, while ASTER and NASADEM performed better in denser forests with tree cover greater than 70%. This study is a robust regional assessment of these global DEMs.

Gradient-Boosted Decision Trees (GBDTs), particularly when tuned with Bayesian optimisation, are powerful machine learning techniques known for their effectiveness in handling complex, non-linear data. However, the performance of these models can be significantly influenced by the characteristics of the terrain being analysed. In this study, we assess the performance of three Bayesian-optimised GBDTs (XGBoost, LightGBM and CatBoost) using digital elevation model (DEM) error correction as a case study. The performance of the models is investigated across five landscapes in Cape Town South Africa: urban/industrial, agricultural, mountain, peninsula and grassland/shrubland. The models were trained using a selection of datasets (elevation, terrain parameters and land cover). The comparison entailed an analysis of the model execution times, regression error metrics, and level of improvement in the corrected DEMs. Generally, the optimised models performed considerably well and demonstrated excellent predictive capability. CatBoost emerged with the best results in the level of improvement recorded in the corrected DEMs, while LightGBM was the fastest of all models in the execution time for Bayesian optimisation and model training. These findings offer valuable insights for applying machine learning and hyperparameter tuning in remote sensing.

There has been a rapid evolution of tree-based ensemble algorithms which have outperformed deep learning in several studies, thus emerging as a competitive solution for many applications. In this study, ten tree-based ensemble algorithms (random forest, bagging meta-estimator, adaptive boosting (AdaBoost), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting (LightGBM), histogram-based GBM, categorical boosting (CatBoost), natural gradient boosting (NGBoost), and the regularised greedy forest (RGF)) were comparatively evaluated for the enhancement of Copernicus digital elevation model (DEM) in an agricultural landscape. The enhancement methodology combines elevation and terrain parameters alignment, with feature-level fusion into a DEM enhancement workflow. The training dataset is comprised of eight DEM-derived predictor variables, and the target variable (elevation error). In terms of root mean square error (RMSE) reduction, the best enhancements were achieved by GBM, random forest and the regularised greedy forest at the first, second and third implementation sites respectively. The computational time for training LightGBM was nearly five-hundred times faster than NGBoost, and the speed of LightGBM was closely matched by the histogram-based GBM. Our results provide a knowledge base for other researchers to focus their optimisation strategies on the most promising algorithms.

Several methods have been proposed for correcting the elevation bias in digital elevation models (DEMs) for example, linear regression. Nowadays, supervised machine learning enables the modelling of complex relationships between variables, and has been deployed by researchers in a variety of fields. In the existing literature, several studies have adopted either machine learning or statistical approaches in the task of DEM correction. However, to our knowledge, none of these studies have compared the performance of both approaches, especially with regard to open-access global DEMs. Our previous work has already shown the potential of machine learning approaches, specifically gradient boosted decision trees (GBDTs) for DEM correction. In this study, we share some results from the comparison of three recent implementations of gradient boosted decision trees (XGBoost, LightGBM and CatBoost), versus multiple linear regression (MLR) for enhancing the vertical accuracy of 30 m Copernicus and AW3D global DEMs in Cape Town, South Africa.

African cities are rapidly experiencing an increase in population, thereby making it difficult to attain self-sustainability. Traffic congestion is a major contributing factor to this issue. Johannesburg's inner-city fits this profile, with an increasing decline in economic and social activities, and quality of life due to traffic congestion. Furthermore, the lack of a road transport infrastructure geodatabase and traffic data in these cities makes it more difficult for stakeholders to make an informed decision on how to effectively manage roads prone to traffic congestion or due for infrastructure upgrade. This paper focuses on developing a geodatabase using factors that cause traffic congestion such as bus stops, traffic lights, speed humps, t-joints, cross joints, street parking, and others. These factors were investigated on some selected roads within the Johannesburg inner-city by enumerating the number of such factors existing on each road with the aid of high-resolution aerial imagery. The developed geodatabase becomes a tool that can support the decision-making process in solving traffic congestion by querying the geodatabase to select roads that are prone to traffic congestions depending on the number of factors occurring along a road.

The geographic potentials of building roofs in urban areas are often under-utilized or sometimes neglected entirely. This is due to the lack of large-scale building roof inventory in the municipality planning offices in African cities. Building rooftops offer great potentials in addressing key urbanization problems such as suitability analysis, disaster management, and others. This paper seeks to demonstrate how remote sensing techniques can be applied to detect and extract building roofs for various suitability analyses (e.g., solar energy and rainwater harvesting) by developing a unique two-in-one technique, which detects and extracts building roof planes and building roof outlines. Using the City of Cape Town, South Africa, as a case study, building rooftops were extracted using the integration of light detection and ranging (LiDAR) data and aerial imagery in an object-based rule-set classification process. This resulted in a building roof inventory, which has crucial attributes of evaluating a building roof for various suitability analyses. The technique developed is well suited for residential and industrial areas where buildings are easily separated from each other. The method and the extracted results were accessed for accuracy and were found to compare well with similar methods already benchmarked.

A web-based multi-criteria decision support tool is designed to support the planning, control and deployment of building-integrated photovoltaic (BIPV) in the City of Cape Town, South Africa. Solar energy is one of the renewable sources for generating electricity by means of photovoltaic systems, and it offers a viable and expedient means of generating electricity within a short period of time. Nevertheless, there are some impediments to the extensive deployment of solar photovoltaic systems. The most prominent among these are generation potential and the high capital cost of initial set-up. Hence, the location, design and yield of building-integrated photovoltaic systems have to be well thought-out before their deployment. The interactive web-based tool designed utilises JavaScript and Hypertext Mark-up Language (HTML) to implement a map mashup, which can be queried to retrieve vital information about the solar photovoltaic potential of a building roof. From results generated and the system developed, it becomes possible to remotely and sufficiently evaluate buildings in the city in order to make decisions about solar photovoltaic potentials, designs and installations.

The risk of fire spread in informal settlements is significant and can be analysed as a function of the spatial arrangement of dwellings. Spatial metrics representing density and shape of dwellings are proposed as a method to identify settlements at high risk of fire spread. LiDAR data is used to map dwelling roofs for informal settlements in the City of Cape Town, South Africa. The LiDAR roof dataset is validated against a visually interpreted dataset digitised from 6 cm resolution aerial photography and is found to have an overall completeness and correctness accuracy of greater than 75% with systematic underrepresentation of roofs in the LiDAR dataset. Correlation analysis of metrics derived from the LiDAR dataset and the reference dataset indicates that only the edge density and landscape density metrics could be applied with confidence to all the settlements. These two metrics are then applied to the informal settlements of Kosovo and Imizamo Yethu. A high landscape density in combination with a low edge density is found to be indicative of fire spread risk. This study represents a first step in the development of spatial metrics for understanding informal settlement fire spread risk.

Apart from the drive to reduce carbon dioxide emissions by carbon-intensive economies like South Africa, the recent spate of electricity load shedding across most part of the country, including Cape Town has left electricity consumers scampering for alternatives, so as to rely less on the national grid. Solar energy, which is adequately available in most part of Africa and regarded as a clean and renewable source of energy, makes it possible to generate electricity by using photovoltaics technology. However, before time and financial resources are invested into rooftop solar photovoltaic systems in urban areas, it is important to evaluate the potential of the building rooftop, intended to be used in harvesting the solar energy. This paper presents methodologies making use of LiDAR data and other ancillary data, such as high-resolution aerial imagery, to automatically extract building rooftops in City of Cape Town and evaluate their potentials for solar photovoltaics systems. Two main processes were involved: (1) automatic extraction of building roofs using the integration of LiDAR data and aerial imagery in order to derive its’ outline and areal coverage; and (2) estimating the global solar radiation incidence on each roof surface using an elevation model derived from the LiDAR data, in order to evaluate its solar photovoltaic potential. This resulted in a geodatabase, which can be queried to retrieve salient information about the viability of a particular building roof for solar photovoltaic installation.