Abstract
Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests.
Highlights
As the main body of terrestrial ecosystems, forests are one of the most important vegetation types in the world and play a key role in the global carbon cycle [1,2]
We adopted parameter sensitivity analysis to analyze the degree of influence and sensitive wavelength range of the different model parameters to the spectral characteristics and more accurately determine the value ranges of the model input parameters
The simulation results suggested that the broadleaf leaf structure parameter (N) and chlorophyll a + b content (Cab) from the PROSPECT model had a significant influence on the leaf spectral reflectance
Summary
As the main body of terrestrial ecosystems, forests are one of the most important vegetation types in the world and play a key role in the global carbon cycle [1,2]. Estimating the composition of forest canopy structures using remote sensing is of great significance [3,4]. Mixed broadleaf-conifer forests are composed of coniferous and broadleaved trees, which are essentially different forest types [6]. There are significant differences between broadleaf and conifer trees in terms of their leaf inclination angles, leaf morphologies, and leaf clumping; these differences compound at the canopy scale, such that their crown morphologies are significantly different [3,6,7,8]. The different compositions of coniferous and broad-leaved tree species directly change the spectral reflectance of the forest canopy, as reflected by remote sensing images with significant differences in spectral characteristics [7,9]
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