Mapping Seasonal Tree Canopy Cover and Leaf Area Using Worldview-2/3 Satellite Imagery: A Megacity-Scale Case Study in Tokyo Urban Area

Abstract

This study presents a methodology for developing a high-resolution (2 m) urban tree canopy leaf area inventory in different tree phenological seasons and a subsequent application of the methodology to a 625 km2 urban area in Tokyo. Satellite remote sensing has the advantage of imaging large areas simultaneously. However, mapping the tree canopy cover and leaf area accurately is still difficult in a highly heterogeneous urban landscape. The WorldView-2/3 satellite imagery at the individual tree level (2 m resolution) was used to map urban trees based on a simple pixel-based classification method. The comparison of our mapping results with the tree canopy cover derived from aerial photography shows that the error margin is within an acceptable range of 5.5% at the 3.0 km2 small district level, 5.0% at the 60.9 km2 municipality level, and 1.2% at the 625 km2 city level. Furthermore, we investigated the relationship between the satellite data (vegetation index) and in situ tree-measurement data (leaf area index) to develop a simple model to directly map the tree leaf area from the WorldView-2/3 imagery. The estimated total leaf area in Tokyo urban area in the leaf-on season (633 km2) was twice that of the leaf-off season (319 km2). Our results also showed that the estimated total leaf area in Tokyo urban area was 1.9–6.2 times higher than the results of the moderate-resolution (30 m) satellite imagery.

Mapping the distribution and tree canopy cover of Jacaranda mimosifolia and Platanus × acerifolia in Johannesburg’s urban forest

“…Nevertheless, in larger sampling areas the accuracy assessment of the classification still yields and checks for issues of spectral confusion and misclassification of results. For instance, a pixel-based classification of TCC in a heterogeneous urban environment using a high-resolution worldview 2/3 imageries showed only < 5.5% of difference compared to those estimated using aerial photograph interpretation in Tokyo, Japan 51 . Thus, a spectral confusion of 17% produced between the J. mimosifolia and the class of ‘other vegetation’ is still within the acceptable limit of accuracy, considering the high level of complex urban heterogeneity for a city believed to have over 10 million trees.…”

CNN-Based Individual Tree Species Classification Using High-Resolution Satellite Imagery and Airborne LiDAR Data

“…Additionally, five vegetation indexes were considered for both the RF and SVM. The indexes include the normalized difference vegetation index (NDVI) [42][43][44] using NIR1, the NDVI using NIR2 [45], the green normalized difference vegetation index (GNDVI) [45,46], the enhanced vegetation index (EVI) [46,47], and the visible atmospherically resistant indices (VARI) [48,49] using the red-edge band. The details of the five indexes are shown in Table 4.…”

Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods

“…This is because their inter-row spaces occur in the same pixels as the vines and have an impact on the overall field observations [35]. In this situation, remote-sensing data with a higher resolution, such as those obtained from Gaofen-2 [36] and WorldView-2 [37][38][39], as well as very high spatial resolution images obtained by manned or unmanned aerial vehicles (UAVs) [40][41][42][43], are becoming increas-ingly popular in precision agriculture. In particular, UAV platforms capable of carrying a variety of sensors, such as visible [44,45], multispectral [40,46], hyperspectral [47,48], and LiDAR [49], have more advantages such as high flexibility, time-saving, and less of a labor requirement.…”