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.
Highlights
Urban vegetation, trees, provides numerous benefits to human well-being [1,2]; trees improve air quality by removing pollutants from the atmosphere and mitigate the heat island effect by providing direct shade and by transpiration [3]
The land surface was separated with the Normal Differential Index (NDI) calculated from Band 5/Band 7, namely NDVI
In the second step, those non-vegetation pixels in the high NDVI areas were excluded using the NDI calculated from Band 5/Band 6
Summary
Trees, provides numerous benefits to human well-being (ecosystem services) [1,2]; trees improve air quality by removing pollutants from the atmosphere and mitigate the heat island effect by providing direct shade and by transpiration [3]. To maximize the urban forest benefits for smart sustainable city development, the negative impacts of the tree canopy on air quality should be assessed [6]. Sound data on the urban forest structure are required to properly assess the effect of the balance of the services/disservices on urban air quality. The magnitude of plant-atmosphere processes is strongly correlated with the total leaf area [7]. Field surveys and aerial photographs are widely used to obtain urban tree canopy data [8,9,10]; these methods are time-consuming and expensive, and usually cannot provide timely and/or complete coverage of large areas
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