Forest ecosystem is the main component of the terrestrial biosphere, and the estimation of forest biomass is a necessary condition for the analysis of carbon cycle and regional carbon budget in land ecosystem. As an active detection method, space borne Lidar can directly obtain the information of vertical structure of vegetation, and its retrieval of forest biomass has obvious advantages compared with optical and microwave remote sensing methods. The Lidar obtains the forest three-dimensional structure through the discrete radiation of the echo data in the spot, and then collectively invert the amount of the forest biomass and its spatial distribution pattern through a number of discrete sampling data in a certain region. Obviously, the density of Lidar discrete sampling points is an important factor that determines the fine degree of forest biomass and its spatial distribution pattern. The first generation vegetation measurement Lidar uses linear detection system, which has a large power consumption, limited power supply capacity, and volume and heat dissipation of the satellite platform. The density of the observation point is low, usually 1 to 2 points per square kilometer, which leads to the poor mapping ability of the regional forest biomass. In order to solve the low density of observation points and improve the mapping ability of Lidar in the region of forest biomass, this paper proposes a kind of second generation of vegetation Lidar based on few-photon mode. The application background, the principle of small photon detection and the composition of the system is discussed. The demand for the energy of the laser emission pulse is lower than that of the linear system. Under the condition of the same resource on the satellite, the density of the sampling point is higher than that of the linear system. Therefore, the second generation vegetation Lidar can better depict the number and spatial distribution pattern of regional forest biomass.
The forest ecosystem in Northeastern China (NEC) is approximately 25% proportion of total forested area of China,
which has been undergoing dramatic changes due to massive loggings and forest fires in the last several decades and
successively intensive manual afforestation and closing protective recovery since 1990s. It is a hot region for scientific
research in carbon balance. In this paper, national land cover GIS data, moderate resolution imaging spectroradiometer
(MODIS) imagery, and vertical waveform of Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and
Land Elevation Satellite (ICESAT) were combined together to map forest aboveground biomass (AGB) in the NEC.
Firstly, GLAS waveform has the advantage of three dimensional observations and can play the role as sampling
footprints for forest biomes. The estimation algorithm was developed between field survey samples and height profile
indices of GLAS waveform to predict forest AGB by neural net regression model. The correlation coefficient R2 between
GLAS forest AGB and field-investigated ones was 0.73. Secondly, MODIS data affords spatially continuous images and
can be used to stratify forested regions as statistical districts. one hundred of spectral clusters were derived from MODIS
phenological curve of enhanced vegetation index (EVI) and near infrared (NIR) channel by K-Means method and
stratified for the statistics of GLAS forest AGB samples. The result illustrates spatial pattern forest AGB and explores its
total amount in the NEC.