Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

Abstract

High-quality urban green space supports the healthy functioning of urban ecosystems. This study aimed to rapidly assess the distribution, and accurately estimate the above-ground biomass, of urban green space using remote sensing methods, thus providing a better understanding of the urban ecological environment in Xuzhou for more effective management. We performed urban green space classifications and compared the performance of Sentinel-2 MSI data and Sentinel-1 SAR data and combinations, for estimating above-ground biomass, using field data from Xuzhou, China. The results showed the following: (1) incorporating an object-oriented method and random forest algorithm to extract urban green space information was effective; (2) compared with stepwise regression models with single-source data, biomass estimation models based on multi-source data provide higher estimation accuracy (R2 = 0.77 for coniferous forest, R2 = 0.76 for shrub-grass vegetation, R2 = 0.75 for broadleaf forest); and (3) from 2016 to 2021, urban green space coverage in Xuzhou decreased, while the total above-ground biomass increased, with higher average above-ground biomass in broadleaf forests (133.71 tons/ha) compared to coniferous forests (92.13 tons/ha) and shrub-grass vegetation (21.65 tons/ha). Our study provides an example of automated classification and above-ground biomass mapping for urban green space using multi-source data and facilitates urban eco-management.

Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images

“…As an ancient coal city, Xuzhou has been transformed from a resource-depleted city into a national ecological garden city in recent years. The ecological environment has undergone great changes, especially the area of forest reaches about 288,567.62 hm 2 and the forest coverage rate reaches about 27.8%, which is the highest forest coverage in the province [16]. Since Xuzhou is a well-developed city in central and eastern China, there are a lot of human activities in both urban and suburban areas, and the vegetation distribution is not uniform.…”

“…Some studies have utilized a geographically weighted regression model (GER) [14], Random Forest model (RF), and other methods to establish the relationship between remote sensing image features and the samples of AGB in the field, and then invert the distribution of AGB in the whole image-covered area [15]. For example, Mthembeni Mngadi et al and Sandra Eckert et al used RF, multiple linear regression method with Sentinel-2 and WorldView-2 image data to invert the AGB distribution in the Buffelsdraai area, and in the northeast of Madagascar, respectively [16,17]. Jue Xiao et al and Nichol et al used the texture features to further improve the accuracy of AGB inversion from remote sensing images [18,19].…”

“…Remote Sens. 2024,16, 1229 17 of 22 shorter vegetation regions. Because the urban unvegetated area is mostly covered by impervious surfaces such as roads, buildings, etc., vegetated areas patches are mostly fragmented and have high landscape heterogeneity when compared to the large dense forests.…”

Progress of carbon sequestration in urban green space based on bibliometric analysis

“…There are few studies on carbon sequestration accounting methods specifically for urban green space, with most of the research methods being optimized by the forest, grassland, and wetland carbon sequestration accounting methods. The biomass method was mainly used to indirectly calculate carbon sequestration based on the change in plant biomass, which is currently the most commonly used method to calculate carbon storage (Xiao et al, 2022). The development of the model accounting method of urban green space carbon sequestration capacity was based on the leaf area index equation (Ren et al, 2018).…”

“…The development of the model accounting method of urban green space carbon sequestration capacity was based on the leaf area index equation (Ren et al, 2018). The carbon footprint method can also be used for statistical analysis of the carbon sequestration capacity of urban green space (Xiao et al, 2022). The vorticity correlation method uses the principle of micrometeorology to monitor CO 2 concentrations, wind speed, and direction at a certain height above the forest land to obtain the amount of CO 2 fixed or released by the ecosystem (Valentini et al, 2000).…”