Qian Le-xiang

Assessment of biochemical concentrations of vegetation using remote sensing technology

The main biochemicals (such as lignin, protein, cellulose, sugar, starch, chlorophyll and water) of vegetation are directly or indirectly involved in major ecological processes, such as the functions of terrestrial ecosystems (i.e., nutrient-cycling processes, primary production, and decomposition). Remote sensing techniques provide a very convenient way of data acquisition capable of covering a large area several times during one season, so it can play a unique and essential role provided that we can relate remote sensing measurements to the biochemical characteristics of the Earth surface in a reliable and operational way. The application of remote sensing techniques for the estimation of canopy biochemicals was reviewed. Three methods of estimating biochemical concentrations of vegetation were included in this paper: index, stepwise multiple linear regression, and stepwise multiple linear regression based on a model of the forest crown. In addition, the vitality and potential applying value are stressed.

Forest landscape patterns dynamics of Yihe-Luohe river basin

Based on the information from forest resources distribution maps of Luoning County of 1983 and 1999, six indices were used to analyze spatial patterns and dynamics of forest landscapes of the typical region in the middle of the Yihe-Luohe river basin. These indices include patch number, mean patch area, fragment index, patch extension index, etc. The results showed that: (1) There was a rapid increase in the number of patch and total area from 1983 to 1999 in the study area. The fragment degree became very high. (2) The area of all the forest patch types had witnessed great changes. The fractal degree of each forest patch type became big from 1983 to 1999. The mean extension index ofRobinia pseudoacacia forest, non-forest, shrub forest, sparse forest, andQuercus species forest increased rapidly, but that of economic forest became zero. The fractal dimension each showed that forest coverage has been promoted. (3) The changes of landscape patterns were different in different geomorphic regions. From 1983 to 1999 the vegetation cover area, the gross number and the density of patch, diversity and evenness of landscape were all reduced greatly in gullies and ravines, but the maximum area and the mean area of patch types were increased. In hilly region, both the forest cover area and the number of patch increased from 1983 to 1999, but the mean area of patch was reduced greatly. In mountain region, even though the area under forest canopy reduced from 1983 to 1999, the patch number was increased greatly, the mean area of all patch types was reduced, the extension index, diversity index and evenness index of landscape were all increased. Furthermore, because of different types of land use, human activity and terrain, the vegetation changes on northern and southern mountain slopes were different. According to these analyses, the main driving forces, such as the policies of management, market economy, influence of human activities etc. are brought out.

Forest landscape pattern dynamics of Luoning County in Henan Province and its driving forces

With the help of ARC/INFOR and ERDAS software, based on the information from forest resources distribution maps and TM images, four indices were chosen to analyze spatial pattern changes of forest landscape of Luoning County, Henan Province from 1983 to 1999. The results showed that: 1) The number and total area of patches were rapidly increased with time changes. The fragmentation degree of the landscape was increasing greatly. 2) The area of some forest patch types, especially shrub forest, economic forest, Populus spp. forest, Quercus spp. forest, sparse forest, deserted grassland etc. had been greatly changed. 3) The fragemation degree of each forest patch type became greater from 1983 to 1999. 4) The transition probabilities of deserted forest, economic forest, Pinus tabulaeformis forest, Populus spp. forest exceed 85%, Robinia pseuoacacia forest, deserted grassland, 65% and Quercus spp. forest, non-forest, shrub forest had smaller ones, which were 26.5%, 29.1% and 45.3%, respectively. The main transition trends of various patches were non-forest and Quercus spp. forest. During the course of transition, the types that 50% of area was remained were Quercus spp. forest, non-forest and shrub forest. According to above analyses, the main driving forces, such as the management policies, market economy factors and influences of human activities etc. were brought out.