A 3D approach to reconstruct continuous optical images using lidar and MODIS

HuaGuo Huang,Jun Lian

doi:10.1186/s40663-015-0044-5

Abstract

Monitoring forest health and biomass for changes over time in the global environment requires the provision of continuous satellite images. However, optical images of land surfaces are generally contaminated when clouds are present or rain occurs. To estimate the actual reflectance of land surfaces masked by clouds and potential rain, 3D simulations by the RAPID radiative transfer model were proposed and conducted on a forest farm dominated by birch and larch in Genhe City, DaXing’AnLing Mountain in Inner Mongolia, China. The canopy height model (CHM) from lidar data were used to extract individual tree structures (location, height, crown width). Field measurements related tree height to diameter of breast height (DBH), lowest branch height and leaf area index (LAI). Series of Landsat images were used to classify tree species and land cover. MODIS LAI products were used to estimate the LAI of individual trees. Combining all these input variables to drive RAPID, high-resolution optical remote sensing images were simulated and validated with available satellite images. Evaluations on spatial texture, spectral values and directional reflectance were conducted to show comparable results. The study provides a proof-of-concept approach to link lidar and MODIS data in the parameterization of RAPID models for high temporal and spatial resolutions of image reconstruction in forest dominated areas.

Highlights

Monitoring forest health and biomass for changes over time in the global environment requires the provision of continuous satellite images
Landsat images have been blended with MODerateresolution Imaging Spectroradiometer (MODIS) data to create spatial and temporal fusion data (Gao et al 2006; Hilker et al 2009; Wu et al 2012)
Our main objective was to create and test how to couple lidar data and temporal optical data MODIS in order to simulate high-resolution optical satellite images

Summary

Introduction

Monitoring forest health and biomass for changes over time in the global environment requires the provision of continuous satellite images. Optical images of land surfaces are generally contaminated when clouds are present or rain occurs. Optical remote sensing images have been widely used in monitoring forest ecosystems. In forested area, temporal resolution is generally reduced by frequent rains or cloud covers, which prevents users from continuously acquiring clear optical remote sensing images. Continuous satellite images are important for forest monitoring (Lunetta et al 2004; Masek et al 2008; Nitze et al 2015) since forest reflectance varies with seasonality (Kobayashi et al 2007; Xu et al 2013). Radiative transfer models have been used to simulate a series of high temporal resolution images for future space earth observation missions (Inglada et al 2011). Shrub leaves were assumed to have the same optical parameters as birch leaves

Methods

Results

Discussion

Conclusion