Zhang H.-B.,Academy of Water Resources Conservation Forests in Qilian Mountains of Gansu Province |
Meng H.-J.,Academy of Water Resources Conservation Forests in Qilian Mountains of Gansu Province |
Meng H.-J.,Key Laboratory of Hydrology and Water Resources of Forest Ecology |
Liu X.-D.,Academy of Water Resources Conservation Forests in Qilian Mountains of Gansu Province |
And 4 more authors.
Wetland Science | Year: 2012
The vegetation characteristics of typical degradation wetlands in the middle reaches of Heihe River basin in Zhangye city, Gansu province were studied in this study. The main influence factors of vegetation degradation were analyzed and the restoration technology of the degradation vegetation was discussed in the paper. The results indicated that there were 45 families, 123 genera and 196 species of the plants distributed in the study area. The main types of plant species composition in the degradation wetlands were humidogene, halophytes and mesophyte, and the main types of plant communities included Com. Acorus calamus, Com. Halerpestes sarmentosa, Com. Phragmites australis, Com. Rhizoma Scirpi Yagarae, Com. Tamarix chinensis, and Com. Potamogeton pusillus-Myriophyllum verticillatum. The technology of ecological water supplement, enclosure of the degradation wetlands, planting grasses artificially and cultivated plants of the wetlands was put forward to; and the projects of the ecological agriculture and 'to return farmlands to wetlands' were carried out for the restoration of degradation wetlands.
Xu J.-M.,Chinese Academy of Forestry |
Lu J.-X.,Chinese Academy of Forestry |
Bao F.-C.,Chinese Academy of Forestry |
Huang R.-F.,Chinese Academy of Forestry |
And 3 more authors.
Beijing Linye Daxue Xuebao/Journal of Beijing Forestry University | Year: 2011
To investigate the response of wood density to climate change, wood density of Picea crassifolia trees at lower tree line in the middle Qilian Mountains, northwestern China was measured using Silviscan-3. Chronologies of annual, earlywood, latewood, maximum and minimum density were established by dendrochronological methods. Relationships of chronologies to monthly mean, maximum, minimum temperatures and monthly precipitation were analyzed. The results indicated that wood density of P.crassifolia trees positively correlated with temperatures and negatively correlated with precipitation. Annual and earlywood density significantly correlated with monthly mean temperatures in June to September, with monthly maximum temperatures in June, July and September, and with monthly minimum temperatures in October in previous year and in July and September in current year. Minimum density significantly correlated with temperature in June to August, with monthly maximum temperatures in June and July, and with monthly minimum temperatures in July. Annul density significantly correlated with precipitation in March. Earlywood and minimum density significantly correlated with precipitation in June. Latewood and maximum density did not show significant relationships with temperature and precipitation, latewood density was less sensitive to climate than earlywood density. Those results indicate that temperature has directly influence and precipitation has indirectly impact on wood density, temperature in summer is main climate factor influencing wood density of P.crassifolia in Qilian Mountains, northwestern China.
Du J.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute |
He Z.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute |
Chen L.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute |
Yang J.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute |
And 2 more authors.
International Journal of Remote Sensing | Year: 2015
Accurate forest carbon accounting forms a basis for promoting the development of ecosystem service markets including forest carbon sinks. However, carbon assessments over large forest areas are challenging. Difficulties are compounded by the lack of adequate field observations especially in mountainous regions. In this study, we describe the development of a two-phase sampling framework to evaluate regional aboveground carbon density (ACD) of subalpine temperate forests in northwestern China that includes integrating ground plots, airborne lidar metrics, and Landsat images. During the first phase, an accurate, lidar-derived, ACD inventory network of a representative forested zone (Dayekou Basin) was established on the basis of a modified allometric model by adding crown coverage (CC) as a supplementary variable; cross-validated R2 was 0.88 and root mean square error (RMSE) was 14.7 Mg C ha−1. The outcomes of this step enabled the extension of quasi-field plots required for the representative carbon evaluations and the amplification of the range of observed values. Further integration of lidar measures and optical Landsat data by using the partial least squares regression (PLSR) method was conducted in the subsequent phase. The final model developed for broad-scale estimates explained 76% of the variance in forest ACD and produced a mean bias error of 27.9 Mg C ha−1. Aboveground carbon stocks for the whole ecoregion averaged 77.2 Mg ha−1, which generated an uncertainty of 13%. Visual patterns revealed a systematic overestimation for low ACD values and an underestimation in those regions with high carbon density. Potential errors in our carbon estimates could be associated with the saturation of optical signals, accuracy of land-cover map, and effects of topographic conditions. Overall, the double-sampling method demonstrated promising means for carbon accounting over large areas in a spatially-explicit manner and provided a good first approximation of carbon quantities for the forests in the ecoregion. Our study illustrated the potential for the use of lidar sampling in facilitating scaling of field surveys to a larger spatial extent than ground-based practices by supplying accurate biophysical measurements (e.g. heights). © 2015 Taylor & Francis.