Tree Crown Density Analysis from Hyperspectral Image

Abstract

A study was conducted to investigate whether reflectance data of hyperspectral image of an area could be used to extract related physical features to produce mapping of vegetation density. This paper explains on estimating percentage of vegetation coverage based on Normalized Difference Vegetation Index (NDVI). Image segmentation based on thresholding was used to separate different features of the land entities like soil, water and road. From here, NDVI values can be integrated for further segmenting the vegetation features. The colour segmentation method is then able to classify the vegetation according to their density level, which can be used to determine tree crown density. Test conducted towards a hyperspectral image shows that different density level can be extracted, where it contains about 7.5% high level tree crown density, 1.8% medium crown density and 5% low. More tests need to be conducted in order to proof the workability of the developed algorithm in analysing hyperspectral images from tree crown density mapping.

Tree Health Condition in Urban Green Areas Assessed through Crown Indicators and Vegetation Indices

“…The smaller diameters and heights of the evaluated trees (Figure 4) indicate that the species may be mostly young (Figure 4A) [20]. It was also found that they are short (Figure 4B), which may be due to pruning activities on the tree crowns.…”

“…Multispectral images (B, G, R and NIR) from the UAV were used to calculate the NDVI, EVI, GNDVI, BNDVI, RGBVI and GRVI indices (Table 2) [26]. Given the precision of the UAV images (0.3 m), it was possible to delimit the crowns of the trees defined in the previous sampling manually (digitization) [27]; this information was transformed from raster to vector format for processing and analyzed through the QGIS version 3.28.4 Firenze geographic information system (GIS) [9,20,24]. This provided greater precision in choosing the pixels of each tree's crown and excluding background information, such as that of another type of vegetation (shrub or herbaceous) or of nearby trees not belonging to the study, to later obtain the average values of the pixels that make up the image of each tree [2,9,26] (Figure 2).…”

“…Tree crown assessment is used as an indicator of health condition in forest species, with some variables being crown density, crown transparency and dieback [11,16,17]. Recently, these indicators have been adapted and used to estimate the health condition of urban trees; however, obtaining this information involves in situ data collection by at least two people [4,6,[17][18][19], which entails a considerable expenditure of time and is complicated when access to the terrain is restricted or dangerous; currently, this method is frequently used to obtain information on tree health condition in both forest and urban areas [4,6,10,20]. On the other hand, there are methods to evaluate physiological processes to quantify the response of trees to stress, one of them being chlorophyll fluorescence (Fv/Fm).…”

“…However, a feasible method for studying tree crowns is the use of unmanned aerial vehicles (UAVs), since being equipped with high-resolution multispectral sensors allows them to obtain precise information from large areas, reducing the time necessary for the analysis of various biophysical parameters compared to traditional methods [9,20,[23][24][25][26][27]. Recent vegetation studies have made use of spectral bands to determine vegetation indices (VIs), among other applications; this remote sensing technology allows for the classification and estimation of the health conditions of vegetation in different ecosystems, as well as in urban areas [28][29][30][31].…”

“…Recent vegetation studies have made use of spectral bands to determine vegetation indices (VIs), among other applications; this remote sensing technology allows for the classification and estimation of the health conditions of vegetation in different ecosystems, as well as in urban areas [28][29][30][31]. Among the vegetation indices used in research are the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the green-normalized difference vegetation index (GNDVI), the blue-normalized difference vegetation index (BNDVI), the red-green-blue vegetation index (RGBVI) and the green-red vegetation index (GRVI) [5,9,20,26,[32][33][34][35].…”