Zhu Wenquan

Simulation of maximum light use efficiency for some typical vegetation types in China

Maximum light use efficiency (εmax) is a key parameter for the estimation of net primary productivity (NPP) derived from remote sensing data. There are still many divergences about its value for each vegetation type. The εmax for some typical vegetation types in China is simulated using a modified least squares function based on NOAA/AVHRR remote sensing data and field-observed NPP data. The vegetation classification accuracy is introduced to the process. The sensitivity analysis of εmax to vegetation classification accuracy is also conducted. The results show that the simulated values of εmax are greater than the value used in CASA model, and less than the values simulated with BIOME-BGC model. This is consistent with some other studies. The relative error of εmax resulting from classification accuracy is −5.5%–8.0%. This indicates that the simulated values of εmax are reliable and stable.

Comprehensive analysis of the impact of climatic changes on Chinese terrestrial net primary productivity

Recent climatic changes have affected terrestrial net primary productivity (NPP). This paper presents an investigation of the impact of climatic changes on Chinese terrestrial vegetation NPP by analyzing 18 years’ (1982 to 1999) climatic data and satellite observations of vegetation activity. Results indicate that climatic changes in China have eased some critical climatic constraint on plant growth. (1) From 1982 to 1999, modeled NPP increased by 1.42%·a−1 in water-limited regions of Northwest China, 1.46%·a−1 in temperature-limited regions of Northeast China and Tibet Plateau, and 0.99%·a−1 in radiation-limited regions of South China and East China. (2) NPP increased by 24.2%, i.e. 0.76 petagram of carbon (Pg C) over 18 years in China. Changes in climate (with constant vegetation) directly contributed nearly 11.5% (0.36 Pg C). Changes in vegetation (with constant climate) contributed 12.4% (0.40 Pg C), possibly as a result of climate-vegetation feedbacks, changes in land use, and growth stimulation from other mechanisms. (3) Globally, NPP declined during all three major El Niño events (1982 to 1983, 1987 to 1988, and 1997 to 1998) between 1982 and 2000, but Chinese vegetation productivity responded differently to them because of the monsoon dynamics. In the first three events (1982 to 1983, 1987 to 1988, and 1992), Chinese vegetation NPP declined, while in the later two events (1993, 1997 to 1998) increasing obviously.

Spatio-temporal distribution of net primary productivity along the Northeast China Transect and its response to climatic change

An improved Carnegie Ames Stanford Approach model (CASA model) was used to estimate the net primary productivity (NPP) of the Northeast China Transect (NECT) every month from 1982 to 2000. The spatial-temporal distribution of NPP along NECT and its response to climatic change were also analyzed. Results showed that the change tendency of NPP spatial distribution in NECT is quite similar to that of precipitation and their spatial correlation coefficient is up to 0.84 (P < 0.01). The inter-annual variation of NPP in NECT is mainly affected by the change of the aestival NPP every year, which accounts for 67.6% of the inter-annual increase in NPP and their spatial correlation coefficient is 0.95 (P < 0.01). The NPP in NECT is mainly cumulated between May and September, which accounts for 89.8% of the annual NPP. The NPP in summer (June to August) accounts for 65.9% of the annual NPP and is the lowest in winter. Recent climate changes have enhanced plant growth in NECT. The mean NPP increased 14.3% from 1980s to 1990s. The inter-annual linear trend of NPP is 4.6 gC·m−2·a−1, and the relative trend is 1.17%, which owns mainly to the increasing temperature.

Comparison of Winter Wheat Classification using Multi-Temporal IRS-P6 Images

The objective of this study is to measure the area of the winter wheat based on the multi-temporal middle resolution remote sensing images and the database of farmland parcel, and then compare the classification accuracy under the different amount of information. The study firstly analyzed the different crop samples in the support of field data, and then classified the images by the three different ways of NDVI threshold segmentation. The classification accuracies were compared in the three conditions of information amount: single-temporal image in November 19 in 2007, three-temporal image and three-temporal image with database of farmland parcel in Beijing in 2006. The results indicate that single-temporal image reveals the low classification accuracy. The multi-temporal images efficiently distinguish wheat from others plants. Moreover, the accuracy could be further improved if the database of farmland parcel was used to rule out the non-cultivated land.