Urban Tree Species Identification and Carbon Stock Mapping for Urban Green Planning and Management

Abstract

Recently, the severe intensification of atmospheric carbon has highlighted the importance of urban tree contributions in atmospheric carbon mitigations in city areas considering sustainable urban green planning and management systems. Explicit and timely information on urban trees and their roles in the atmospheric Carbon Stock (CS) are essential for policymakers to take immediate actions to ameliorate the effects of deforestation and their worsening outcomes. In this study, a detailed methodology for urban tree CS calibration and mapping was developed for the small urban area of Sassuolo in Italy. For dominant tree species classification, a remote sensing approach was applied, utilizing a high-resolution WV3 image. Five dominant species were identified and classified by applying the Object-Based Image Analysis (OBIA) approach with an overall accuracy of 78%. The CS calibration was done by utilizing an allometric model based on the field data of tree dendrometry—i.e., Height (H) and Diameter at Breast Height (DBH). For geometric measurements, a terrestrial photogrammetric approach known as Structure-from-Motion (SfM) was utilized. Out of 22 randomly selected sample plots of 100 square meters (10 m × 10 m) each, seven plots were utilized to validate the results of the CS calibration and mapping. In this study, CS mapping was done in an efficient and convenient way, highlighting higher CS and lower CS zones while recognizing the dominant tree species contributions. This study will help city planners initiate CS mapping and predict the possible CS for larger urban regions to ensure a sustainable urban green management system.

Estimating Aboveground Carbon Stock at the Scale of Individual Trees in Subtropical Forests Using UAV LiDAR and Hyperspectral Data

“…The improvement might be due to the complementarity between LiDAR-detected structural characteristics and hyperspectral-captured vegetation spectral information, and the combined model took full advantage of the predictive ability of structural features and hyperspectral features, which is consistent with previous studies [72,74,75]. Several studies have combined hyperspectral and LiDAR data to estimate carbon stocks at individual tree level [34,76,77]. They used hyperspectral information to classify tree species, and then developed carbon stock estimation model based on LiDARderived structural features.…”

Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

“…High Spatial Resolution Sensors. Since 2000, VHR commercial satellite sensors' data have shown a potential for creating digital base maps [12] and single TS can be successfully identified and mapped from the VHR images (e.g., [34,[58][59][60][61][62][63][64]). Such VHR satellite sensors, listed in Table 2, may include GeoEye-1, Gaofen-2, IKONOS, Quickbird, Pléiades, RapidEye, and WorldView-2/3 (WV2/3).…”

Remotely Sensed Tree Characterization in Urban Areas: A Review

“…The aforementioned study by Hartling et al [37] also used eight SWIR bands (short-wave infrared) of WV3 as complementary information to WV2 and LiDAR, improving model accuracy by at least 2%. Choudhury et al [88] used spectral and textural attributes derived from WV3 and photogrammetry techniques to map five dominant urban trees in Sassuolo, Italy, and study their carbon sequestration capacity, achieving an accuracy of 78% on tree identification.…”

“…PCCs are products derived from aerial images from which, through image matching techniques of the overlapping stereo images, a 3D image or point cloud can be obtained [101]. As with LiDAR data, some metrics such as tree height can be obtained from a digital elevation model (DEM) or a canopy height model (CHM) [88]. PPCs were used in two studies.…”

“…Roberts et al [102] used mobile PPCs for mapping and measuring the diameter of 88 roadside trees, achieving a R 2 of 0.95, and found that the accuracy increased by 8% compared to static PPCs. Choudhury et al [88] used static PPCs at ground level to measure tree heights in 22 plots (100 m 2 ), and required a total of 150 photos per plot to properly map and reconstruct each plot. The mean height error was 0.3 cm for the largest trees (25 m), indicating high model accuracy.…”