Remote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data

Abstract

& Key message We estimated the leaf area index (LAI) and canopy chlorophyll content (CC) of Moso bamboo forest by using statistical models based on MODIS data and field measurements. Results showed that the statistical model driven by MODIS data has the potential to accurately estimate LAI and CC, while the structure of the calibration models varied between on-and off-years because of the different leaf change and bamboo shoot production characteristics between these types of years. & Context LAI and CC (gram per square meter of ground area) are important parameters for determining carbon exchange between Moso bamboo forest (Phyllostachys edulis (Carrière) J. Houz.) and the atmosphere. & Aims This study evaluated the ability of a statistical model driven by MODIS data to accurately estimate the LAI and CC in Moso bamboo forest, and differences in the LAI and CC between on-years (years with great shoot production) and off-years (years with less shoot production) were analyzed. & Methods The LAI and CC measurements were collected in Anji County, Zhejiang Province, China. Indicators of LAI and CC were calculated from MODIS data. Then, a regression analysis was used to build relationships between the LAI and CC and various indicators on the basis of leaf change and bamboo shoot production characteristics of Moso bamboo forest. & Results LAI and CC were accurately estimated by using the regression analysis driven by MODIS-derived indicators with a relative root mean squared error (RMSEr) of 9.04 and 13.1%, respectively. The structure of the calibration models varied between on-and offyears. Long-term time series analysis from 2000 to 2015 showed that LAI and CC differed largely between on-and off-years. & Conclusion This study demonstrates that LAI and CC of Moso bamboo forest can be estimated accurately by using a statistical model driven by MODIS-derived indicators, but attention should be paid to differences in the calibration models between on-and off-years.

Estimating Crown Structure Parameters of Moso Bamboo: Leaf Area and Leaf Angle Distribution

“…Recently, much attention has been paid to estimate the LAI of MB canopies based on satellite remote sensing techniques. Xu et al [5,16] developed empirical and correction models, respectively, to estimate LAI for MB canopies using the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Li et al [17] and Mao et al [18] developed assimilation methods to improve the MODIS LAI time series of MB forests.…”

“…Houz., abbreviated as MB) has reached 4.43 million hectares, which is 73.71% of the bamboo forested area in China [3]. Many efforts have been made to quantify ecological properties of MB, such as stand-scale transpiration [4], canopy chlorophyll content [5], and soil respiration of MB forests [6]. Characterized by fast growth and strong carbon sequestration abilities, MB forests show great potential to be a substantial carbon sink and, thus, play a significant role in addressing global climate change [4,[7][8][9][10][11]].…”

Quantitative Inversion of Chlorophyll a Concentration and Analysis of Temporal and Spatial Characteristics in Hongkong Offshore Waters Based on Time-Series Hj-1a/B Multispectral Data

“…Due to the shortcomings of scattered sampling points, high cost and long times cycle, the traditional water quality monitoring methods can not meet the needs of marine fishing ground prediction and red tide detection for rapid acquisition of chlorophyll a concentration distribution (Sid'ko, et al 2017). Remote sensing inversion of chlorophyll a concentration can meet the needs of water quality monitoring and eutrophication evaluation, marine productivity and other studies to quickly obtain large-scale ocean chlorophyll a concentration distribution, and make up for the shortage of observed sample points in traditional marine monitoring (Watanabe, et al 2018;Xu, et al 2018), which makes it possible to monitor the change of chlorophyll a concentration in long time series.…”