Geomatics, Volume 4, Issue 4 (December 2024) – 5 articles

Urban tree canopy (UTC) coverage, or area, is an important metric for monitoring changes in UTC over large areas within a municipality. Several methods have been used to obtain these data, but remote sensing image classification is one of the fastest and most reliable over large areas. However, most studies have tested only one or two classification methods to accomplish this while using costly satellite imagery or LiDAR data. This study seeks to compare three urban tree canopy cover classifiers by testing a deep learning U-Net convolutional neural network (CNN), support vector machine learning classifier (SVM) and a random forests machine learning classifier (RF) on cost-free 2012 aerial imagery over a small southern USA city and midsize, growing southern USA city. The results of the experiment are then used to decide the best classifier and apply it to more recent aerial imagery to determine canopy changes over a 10-year period. The changes are subsequently compared visually and statistically with recent urban heat maps derived from thermal Landsat 9 satellite data to compare the means of temperatures within areas of UTC loss and no change. The U-Net CNN classifier proved to provide the best overall accuracy for both cities (89.8% and 91.4%), while also requiring the most training and classification time. When compared spatially with city heat maps, city periphery regions were most impacted by substantial changes in UTC area as cities grow and the outer regions get warmer. Furthermore, areas of UTC loss had higher temperatures than those areas with no canopy change. The broader impacts of this study reach the urban forestry managers at the local, state/province, and national levels as they seek to provide data-driven decisions for policy makers. Full article

Dairy production is one of the most important economic sectors in Bangladesh. However, the traditional management of dairy cow manure and other wastes results in air pollution, eutrophication of surface water, and soil contamination, highlighting the urgent need for more sustainable waste management solutions. To address the environmental problems of dairy waste management, this research explored the potential of community-based biogas production from dairy cow manure in Bangladesh. This study proposed introducing community-based biogas plants using a geographic information system (GIS). The study first applied a restriction analysis to identify sensitive areas, followed by a suitability analysis to determine feasible locations for biogas plants, considering geographical, social, economic, and environmental factors. The final suitable areas were identified by combining the restriction and suitability maps. The spatial distribution of dairy farms was analyzed through a cluster analysis, identifying significant clusters for potential biogas production. A baseline and proposed scenario were designed for five clusters based on the input and output capacities of the biogas plants, estimating the location and capacity for each cluster. The study also calculated electricity generation from the proposed scenario and the net greenhouse gas (GHG) emissions reduction potential of the biogas plants. The findings provide a land-use framework for implementing biogas plants that considers environmental and socio-economic criteria. Five biogas plants were found to be technically and spatially feasible for electricity generation. These plants can collectively produce 31 million m³ of biogas annually, generating approximately 200.60 GWh of energy with a total electricity capacity of 9.8 MW/year in Bangladesh. Implementing these biogas plants is expected to increase renewable energy production by at least 1.25%. Furthermore, the total GHG emission reduction potential is estimated at 104.26 Gg/year CO2eq through the annual treatment of 61.38 thousand tons of dairy manure. Full article

The sustainable management of protected areas has increasingly become difficult due to the lack of updated information on land use and land cover transformations caused by anthropogenic pressures. This study investigates the spatiotemporal dynamics of the Dinderesso and Peni classified forests in Burkina Faso from 1986 to 2022. First, a data driven method was adopted to investigate these forests degradation dynamics. Hence, relevant Landsat images data were collected, segmented, and analyzed using QGIS SCP plugin Random Forest algorithm. Ninety percent of the overall adjusted classification accuracies were obtained. The analysis also showed significant degradation and deforestation with high wooded vegetation classes such as clear forest and wooded savannah (i.e., tree savannah) converging to lower vegetation classes like shrub savannah and agroforestry parks. A second investigation carried out through surveys and field trips revealed key anthropogenic drivers including agricultural expansion, demographic pressure, bad management, wood cutting abuse, overexploitation, overgrazing, charcoal production, and bushfires. These findings highlight the critical need for better management to improve these protected areas. Full article