Wang Changyao

Dynamic of China’s cultivated land and landcover changes of its typical regions based on remote sensing data

Using the multi-temporal Landsat data and survey data of national resources, the authors studied the dynamics of cultivated land and landcover changes of typical ecological regions in China. The results of investigation showed that the whole distribution of the cultivated land shifted to Northeast and Northwest China, and as a result, the ecological quality of cultivated land dropped down. The seacoast and cultivated land in the area of Yellow River Mouth expanded by an increasing rate of 0.73 km·a−1, with a depositing rate of 2.1 km·a−1. The desertification area of the dynamic of Horqin Sandy Land increased from 60.02% of the total land area in 1970s to 64.82% in 1980s but decreased to 54.90% in early 1990s. As to the change of North Tibet lakes, the water area of the Namu Lake decreased by 38.58 km2 from year 1970 to 1988, with a decreasing rate of 2.14 km2·a−1.

Study on grid technology of agricultural climate-environmental information in China

The development GIS and RS technology makes it possible that agricultural climate resources in large scale can be calculated in tiny grid. In this paper, meteorological observatory network in China is set up first using GIS based on DEM data and data from all observatories; then the geographical and topographical factors are girded and parameterized by kriging interpolation to produce the meteorological data and elevation, slope and aspect data in 1 /spl times/ 1 km grid. The monthly surface albedo data and atmosphere total transmittance data in 2001 are reversal in national scale with TERRA/MODIS data. The impact on solar radiation from factors of slope, aspect and topographical shadow is analyzed quantitatively, and solar radiation calculation model in hillside is built and agricultural climate environmental information is girded. Agricultural climate-environmental information grid not only can greatly improve the observation materials in agricultural climate to share with, but also can facilitate understanding the spacious and temporal change law of agricultural climate resources.

The carbon flux estimation of China terrestrial ecosystems based on NOAA/AVHRR data

Net ecosystem productive (NEP) is defined as the net carbon dioxide flux to or from an ecosystem without natural or human disturbance, and integrates all ecosystems carbon sources and sinks: NEE=GPP-Ra-Rh. Here NPP was calculated with the light use efficiency model based on NOAA/AVHRR, and the soil respiration of the corresponding ecosystem came from references. On the whole, the NEP shows the terrestrial ecosystem in China is a carbon sink though there are great uncertainties. The geographic gradient of the NEP clearly shows more correlation with temperature on latitude gradient, and with precipitation on longitude. Three main sources of uncertainties were analyzed: (1) land cover classification based on remote sensing; (2) NPP modelling; (3) soil respiration modelling. The observation and modelling integrated with remote sensing will be a very important solution to the carbon source/sink at large spatial sale.

Study on GPS precipitation forecast system for river catchments based on GA-ANN model

Flood calamity was caused by continuity, heavy and lasting long atmospheric precipitation. However the distribution of atmospheric precipitation is not very uniformity on space-time. The precision of the flood forecast was influenced by the precipitation forecast directly, so precipitation forecast was reliable for prewarning and leading prerequisite to flood forecast. In this paper, obtained the real-time atmospheric water vapor data which it utilize GPS water vapor monitoring net in the trial zone, a new method forecast of precipitation has been probed into based on GPS water vapor data. It has developed GPS Precipitation Forecast System for River Catchments adopted the development of the module and system integration. The structure, function, running, and key technology of the system are introduced respectively. Every method of precipitation forecast is simply depicted. The artificial neural network (ANN) with the genetic algorithm (GA) and BP algorithm for river catchments precipitation forecast has been set up. The result shows that the GPS water vapor and the model of GA-BP can enhance the forecast precision, and its effectiveness and the reliability has been proved. The system can realize the information processing of different data sources and the precipitation forecast of the result is made by the data of river catchments GPS observing, high altitude, Satellite cloud atlas and numerical forecast product. It can service for prewarning predicts and prevent flood and offer the scientific meteorological decision basis for the flood.

Retrieving the crop coefficient spatial distribution for cotton under different growth status with Landsat ETM+ image

Crop water requirement was important in the irrigation scheduling. The crop coefficient is a parameter for estimating crop water requirement by multiplying with the reference crop evapotranspiration. Crop coefficient used to be approximated by crop developing days. The method must have some defect because the crop coefficient is a parameter related to crop status, climate condition and surface albedo. All the factors relating to the crop coefficient are spatially diverse and remote sensing has advantages in obtaining the distributing parameters for vegetation and climate factor. Based on the Penman-Monteith equation, the reference crop evapotranspiration and potential evapotranspiration for cotton under different growth status was estimated with measured meteorological data, then the crop coefficient for cotton was retrieved from a Landsat ETM+ image. And the sensitivity of crop coefficient to the influence factors were analysed. The results showed that the crop coefficient retrieved from the ETM+ image was greater than those suggested by FAO and the crop coefficient was influenced and decided by NDVI that represents crop growth status, while surface albedo that has a very larger variance for the sparse vegetation cover has scarcely any effect on crop coefficient and the climate factors has litter influence on crop coefficient too; with the vegetation cover fraction developing, the climate factor has a much more positive effect on the crop coefficient.