Remote Sensing of Depth-Induced Variations in Soil Organic Carbon Stocks Distribution Within Different Vegetated Landscapes.

The preservation and augmentation of soil organic carbon (SOC) stocks is critical to designing climate change mitigation strategies and alleviating global warming. However, due to the susceptibility of SOC stocks to environmental and topo-climatic variability and changes, it is essential to obtain a comprehensive understanding of the state of current SOC stocks both spatially and vertically. Consequently, to effectively assess SOC storage and sequestration capacity, precise evaluations at multiple soil depths are required. Hence, this study implemented an advanced Deep Neural Network (DNN) model incorporating Sentinel-1 Synthetic Aperture Radar (SAR) data, topo-climatic features, and soil physical properties to predict SOC stocks at multiple depths (0-30cm, 30-60cm, 60-100cm, and 100-200cm) across diverse land-use categories in the KwaZulu-Natal province, South Africa. There was a general decline in the accuracy of the DNN model's prediction with increasing soil depth, with the root mean square error (RMSE) ranging from 8.34 t/h to 11.97 t/h for the four depths. These findings imply that the link between environmental covariates and SOC stocks weakens with soil depth. Additionally, distinct factors driving SOC stocks were discovered in both topsoil and deep-soil, with vegetation having the strongest effect in topsoil, and topo-climate factors and soil physical properties becoming more important as depth increases. This underscores the importance of incorporating depth-related soil properties in SOC modelling. Grasslands had the largest SOC stocks, while commercial forests have the highest SOC sequestration rates per unit area. This study offers valuable insights to policymakers and provides a basis for devising regional management strategies that can be used to effectively mitigate climate change.

Mapping Sub-surface Distribution of Soil Organic Carbon Stocks in South Africa's Arid and Semi-Arid Landscapes: Implications for Land Management and Climate Change Mitigation.

Soil organic carbon (SOC) stocks are critical for land management strategies and climate change mitigation. However, understanding SOC distribution in South Africa's arid and semi-arid regions remains a challenge due to data limitations, and the complex spatial and sub-surface variability in SOC stocks driven by desertification and land degradation. Thus, to support soil and land-use management practices as well as advance climate change mitigation efforts, there is an urgent need to provide more precise SOC stock estimates within South Africa's arid and semi-arid regions. Hence, this study adopted remote-sensing approaches to determine the spatial sub-surface distribution of SOC stocks and the influence of environmental co-variates at four soil depths (i.e., 0-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). Using two regression-based algorithms, i.e., Extreme Gradient Boosting (XGBoost) and Random Forest (RF), the study found the former (RMSE values ranging from 7.12 t/ha to 29.55 t/ha) to be a superior predictor of SOC in comparison to the latter (RMSE values ranging from 7.36 t/ha to 31.10 t/ha). Nonetheless, both models achieved satisfactory accuracy (R2 ≥ 0.52) for regional-scale SOC predictions at the studied soil depths. Thereafter, using a variable importance analysis, the study demonstrated the influence of climatic variables like rainfall and temperature on SOC stocks at different depths. Furthermore, the study revealed significant spatial variability in SOC stocks, and an increase in SOC stocks with soil depth. Overall, these findings enhance the understanding of SOC dynamics in South Africa's arid and semi-arid landscapes and emphasizes the importance of considering site specific topo-climatic characteristics for sustainable land management and climate change mitigation. Furthermore, the study offers valuable insights into sub-surface SOC distribution, crucial for informing carbon sequestration strategies, guiding land management practices, and informing environmental policies within arid and semi-arid environments.

Quantitative remote sensing of forest ecosystem services in sub-Saharan Africa's urban landscapes: a review.

A dearth of information on urban ecosystem services in the past decades has led to little consolidation of such information for informed planning, decision-making and policy development in sub-Saharan African cities. However, the increasing recognition of the value of urban ecological processes and services as well as their contribution to climate change adaptation and mitigation has recently become an area of great research interest. Specifically, the emerging geospatial analytical approaches like remote sensing have led to an increase in the number of studies that seek to quantify and map urban ecosystem services at varying scales. Hence, this study sought to review the current remote sensing trends, challenges and prospects in quantifying urban ecosystem services in sub-Saharan Africa cities. Literature shows that consistent modelling and understanding of urban ecosystem services using remotely sensed approaches began in the 1990s, with an average of five publications per year after around 2010. This is mainly attributed to the approach's ability to provide fast, accurate and repeated spatial information necessary for optimal and timely quantification and mapping of urban ecosystem services. Although commercially available high spatial resolution sensors (e.g. the Worldview series, Quickbird and RapidEye) with higher spatial and spectral properties have been valuable in providing highly accurate and reliable data for quantification of urban ecosystem services, their adoption has been limited by high image acquisition cost and small spatial coverage that limits regional assessment. Thus, the newly launched sensors that provide freely and readily available data (i.e. Landsat 8 and 9 OLI, Sentinel-2) are increasingly becoming popular. These sensors provide data with improved spatial and spectral properties, hence valuable for past, current and future urban ecosystem service assessment, especially in developing countries. Therefore, the study provides guidance for future studies to continuously assess urban ecosystem services in order to achieve the objectives of Kyoto Protocol and Reducing Emissions from Deforestation and forest Degradation (REDD +) of promoting climate-resilient and sustainable cities, especially in developing world.

Estimating aboveground net primary productivity of reforested trees in an urban landscape using biophysical variables and remotely sensed data.

Recently, urban reforestation programs have emerged as potential carbon sinks and climate mitigates in urban landscapes. Thus, spatially explicit information on net primary productivity (NPP) of reforested trees in urban environments is central to understanding the value of reforestation initiatives in the global carbon budget and climate regulation potential. To date, numerous studies have mainly focused on natural and commercial forests NPP at a regional scale based on coarse spatial resolution remotely sensed data. Generally, local scale NPP studies based on fine spatial resolution data are limited. Therefore, this study sought to estimate aboveground NPP of an urban reforested landscape using biophysical and Sentinel-2 Multispectral Imager data derived variables. Using the MOD17 model, results showed that mean NPP ranged between 6.24 Mg C ha-1 with high coefficient of determination (R2: 0.92) and low RMSE (0.82 Mg ha-1) across all reforested trees within the study area. Results also showed a considerable variation in NPP among the reforested trees, with deciduous Acacia and Dalbergia obovate species showing the highest NPP (7.62 Mg C ha-1 and 7.58 Mg C ha-1, respectively), while the evergreen Syzygium cordatum and shrub Artemisia afra had the lowest NPP (4.54 Mg C ha-1 and 5.26 Mg C ha-1). Furthermore, the multiple linear regression analysis showed that vegetation specific biophysical variables (i.e. leaf area index, Normalized Difference Vegetation Index and Fraction of Photosynthetically Active Radiation) significantly improved the estimation of reforested aboveground NPP at a fine-scale resolution. These findings demonstrate the effectiveness of biophysical and remotely sensed variables in determining NPP (as carbon sequestration surrogate) at fine-scaled reforested urban landscape. Furthermore, the utility of species biometric measurements and MOD17 model offers unprecedented opportunity for improved local scale reforestation assessment and monitoring schedules.