Ni Huang

Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2

Abstract New optical and microwave integrated vegetation indices (VIs) were designed based on observations from both field experiments and satellite (HJ-1 and RADARSAT-2) data. It was found that these VIs perform better in estimating the structure parameters of maize, such as Leaf Area Index (LAI), height and biomass, than the original ones. This investigation focused on the difference of interaction between the multispectral reflectance and microwave backscattering signatures with the maize growth variables. Because the maize was near the heading stage with large vegetation coverage in the experiment, the reflectance of the near-infrared band of HJ-1 was much less sensitive to the structure variables than that of the visible-light band. Thus, the optical VIs formulated using those bands were saturated to estimate the structure parameters. With respect to the RADARSAT-2 data, there was a relatively strong relationship between the HV cross-polarization and the volume scattering of the maize, which was mostly determined by the crown structure. The modified VIs were designed using both the VIs of HJ-1 and the HV cross-polarization of RADARSAT-2 to overcome the saturation limitation. The validation showed that this integrated method of determining VIs is a good alternative to that using only the optical or microwave observation.

Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling

Forest canopy cover (CC) and above-ground biomass (AGB) are important ecological indicators for forest monitoring and geoscience applications. This study aimed to estimate temperate forest CC and AGB by integrating airborne LiDAR data with wall-to-wall space-borne SPOT-6 data through geostatistical modeling. Our study involved the following approach: (1) reference maps of CC and AGB were derived from wall-to-wall LiDAR data and calibrated by field measurements; (2) twelve discrete LiDAR flights were simulated by assuming that LiDAR data were only available beneath these flights; (3) training/testing samples of CC and AGB were extracted from the reference maps inside and outside the simulated flights using stratified random sampling; (4) The simple linear regression, ordinary kriging and regression kriging model were used to extend the sparsely sampled CC/AGB data to the entire study area by incorporating a selection of SPOT-6 variables, including vegetation indices and texture variables. The regression kriging model was superior at estimating and mapping the spatial distribution of CC and AGB, as it featured the lowest mean absolute error (MAE; 11.295% and 18.929 t/ha for CC and AGB, respectively) and root mean squared error (RMSE; 17.361% and 21.351 t/ha for CC and AGB, respectively). The predicted and reference values of both CC and AGB were highly correlated for the entire study area based on the estimation histograms and error maps. Finally, we concluded that the regression kriging model was superior and more effective at estimating LiDAR-derived CC and AGB values using the spatially-reduced samples and the SPOT-6 variables. The presented modeling workflow will greatly facilitate future forest growth monitoring and carbon stock assessments for large areas of temperate forest in northeast China. It also provides guidance on how to take full advantage of future sparsely collected LiDAR data in cases where wall-to-wall LiDAR coverage is not available from the perspective of geostatistics.

Spatial-temporal dynamics of carbon emissions and carbon sinks in economically developed areas of China: a case study of Guangdong Province

This study analysed spatial-temporal dynamics of carbon emissions and carbon sinks in Guangdong Province, South China. The methodology was based on land use/land cover data interpreted from continuous high-resolution satellite images and energy consumption statistics, using carbon emission/sink factor method. The results indicated that: (1) From 2005 to 2013, different land use/land cover types in Guangdong experienced varying degrees of change in area, primarily the expansion of built-up land and shrinkage of forest land and grassland; (2) Total carbon emissions increased sharply, from 76.11 to 140.19 TgC yr−1 at the provincial level, with an average annual growth rate of 10.52%, while vegetation carbon sinks declined slightly, from 54.52 to 53.20 TgC yr−1. Both factors showed significant regional differences, with Pearl River Delta and North Guangdong contributing over 50% to provincial carbon emissions and carbon sinks, respectively; (3) Correlation analysis showed social-economic factors (GDP per capita and permanent resident population) have significant positive impacts on carbon emissions at the provincial and city levels; (4) The relationship between economic growth and carbon emission intensity suggests that carbon emission efficiency in Guangdong improves with economic growth. This study provides new insight for Guangdong to achieve carbon reduction goals and realize low-carbon development.