Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline

Abstract

With the advent of Sentinel-2, it is now possible to generate large-scale chlorophyll content maps with unprecedented spatial and temporal resolution, suitable for monitoring ecological processes such as vegetative stress and/or decline. However methodological gaps exist for adapting this technology to heterogeneous natural vegetation and for transferring it among vegetation species or plan functional types. In this study, we investigated the use of Sentinel-2A imagery for estimating needle chlorophyll (C a+b ) in a sparse pine forest undergoing significant needle loss and tree mortality. Sentinel-2A scenes were acquired under two extreme viewing geometries (June vs. December 2016) coincident with the acquisition of high-spatial resolution hyperspectral imagery, and field measurements of needle chlorophyll content and crown leaf area index. Using the high-resolution hyperspectral scenes acquired over 61 validation sites we found the CI chlorophyll index R 750 /R 710 and Macc index (which uses spectral bands centered at 680 nm, 710 nm and 780 nm) had the strongest relationship with needle chlorophyll content from individual tree crowns (r 2 = 0.61 and r 2 = 0.59, respectively; p < 0.001), while TCARI and TCARI/OSAVI, originally designed for uniform agricultural canopies, did not perform as well (r 2 = 0.21 and r 2 = 0.01, respectively). Using lower-resolution Sentinel-2A data validated against hyperspectral estimates and ground truth needle chlorophyll content, the red-edge index CI and the Sentinel-specific chlorophyll indices CI-Gitelson, NDRE1 and NDRE2 had the highest accuracy (with r 2 values >0.7 for June and >0.4 for December; p < 0.001). The retrieval of needle chlorophyll content from the entire Sentinel-2A bandset using the radiative transfer model INFORM yielded r 2 = 0.71 (RMSE = 8.1 μg/cm 2 ) for June, r 2 = 0.42 (RMSE = 12.2 μg/cm 2 ) for December, and r 2 = 0.6 (RMSE = 10.5 μg/cm 2 ) as overall performance using the June and December datasets together. This study demonstrates the retrieval of leaf C a+b with Sentinel-2A imagery by red-edge indices and by an inversion method based on a hybrid canopy reflectance model that accounts for tree density, background and shadow components common in sparse forest canopies.

Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling

“…Canopy LAI and biochemistry estimations using hyperspectral imagery have mostly been done over canopies with high LAI (for instance, see Banskota et al [16], le Maire et al [25], Ali et al [26], Darvishzadeh et al [27], Malenovský et al [28]). While various estimations have also specifically been done over open-canopy ecosystems (e.g., the works of Zarco-Tejada et al [29], Hernández-Clemente et al [30], Zarco-Tejada et al [31]), research concerning acceptable modeling methods within RTM is still ongoing [32,33]. The validity of a simplified representation of trees (simple forest representation, SFR) within the DART scene (such as the one done by Gascon et al [14]) when working with medium-resolution hyperspectral images of very sparse forests was tested in this study, based on the work presented in Widlowski et al [32].…”

Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest

“…Strictly speaking, the complex canopy structure of evergreens makes the application of 1D canopy RTMs such as PROSAIL difficult (Jacquemoud et al, 2009;Zarco-Tejada et al, 2019). Yet, Moorthy et al (2008); Ali et al (2016); Zarco-Tejada et al…”

Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar

“…Firstly, the object-based segmentation method was applied on HI using combined spectral and texture features to separate crowns from soil background and shadows [43]. Secondly, the binary watershed analysis and the Euclidian distance were used to separate overlapping crowns [44,45]. The segmentation accuracy was assessed by the single tree detection rate (STDR), which is the ratio between detected tree crown numbers and measured true value.…”

“…The range of leaf chlorophyll (Cab) and leaf area index (LAI) were defined from field data, while soil reflectance, viewing geometry and solar zenith angle were extracted from the hyperspectral image metadata. Others parameters (shown in Table 2) include, leaf mass per area (Cm), equivalent water thickness (Cw), leaf structure parameter (N), carotenoid content (Car), anthocyanin content (Canth), average leaf angle (ALA), and hot spot size were set according to the similar literature [45,52,62]. The parametrization of the LUT was based on the input parameters and range described in Table 2.…”

“…The parametrization of the LUT was based on the input parameters and range described in Table 2. Because PROSAIL simulates homogeneous canopies, we only used the spectral information of selected sunlit pixels to minimize the shadow influences [45,52]. In the process of LUT-based inversion, Cab was estimated by comparing the simulated spectra and UAV canopy spectra using root mean square error (RMSE) as the cost function, and each Cab estimation was based on the average of the 3000 closest matching LUT entries.…”