Using hyperspectral indices to measure the effect of mine dust on the growth of three typical desert plants.

To examine the influence of coal dust from mining on vegetative growth, three typical plants from near an open-pit coalmine in an arid region were selected, and their spectral signals were determined. The present study was conducted near the Wucaiwan open-pit coalmine in the East Junggar Basin in Xinjiang. We extracted nineteen vegetation indices and examined their correlation with the dust flux. The objective was to determine which parameters that quantify vegetation damage could provide a basis for environmental monitoring in arid regions. The results indicate that when coal dust damages vegetation, both chlorophyll and moisture are reduced, and the amount of carotenoids increases with increasing coal dust. The pigment-specific normalized difference (PSNDb), structure-insensitive pigment index (SIPI) and plant water index (PWI) were the most sensitive indices, and sacsaoul was most sensitive to coal-dust pollution.

[Discussion on hyperspectral index for the estimation of cotton canopy water content].

Proper vegetation indices have decisive influences on the precision of hyperspectral estimation models for surface parameters. In the present paper, in order to find the proper hyperspectral indices for cotton canopy water content estimation, two water parameters for cotton canopy water content (EWT(canopy), equivalent water thickness; VWC, vegetation water content) and corresponding hyperspectra data were analyzed. A rigorous search procedure was used to determine the best index predictors of cotton canopy water. In the procedure, all possible ratio indices and normalized difference indices were derived from the canopy hyperspectra, involving all the two-band combinations between 350 nm and 2500 nm. Then the correlation between two water parameters and all combination indices were analyzed, and the best indices which produced maximum correlation coefficients were determined. Finally, the indices were compared with the published water indices for their performances in estimation of cotton canopy water content. The results showed that for the estimation of EWT(canopy), the new developed ratio index R1 475/R1 424 and normalized difference index (R1 475 -R1 424)/(R1 475 + R1 424) was the most proper one, and the correlation coefficient of the estimated and measured EWT(canopy) reached 0.849. For the estimation of VWC, the performance of published index was better than new developed index, the best suitable water indices for VWC estimation were (R835 - R1 650)/(R835 + R1 650), and the correlation coefficient of the estimated and measured VWC was 0.849.

[Developing biomass estimation models of young trees in typical plantation on the Qinghai-Tibet Plateau, China.] / é’è—é«˜åŽŸå ¸åž‹äººå·¥æž—å¹¼æ ‘ç”Ÿç‰©é‡æ¨¡åž‹æž„å»º.

Calculation of forest biomass is the basis for global carbon stock estimation, which has been included in national forest inventory projects. The volume-derived biomass method is generally used for trees with diameter at breast height (DBH) larger than 5 cm in most forest carbon sink measurement, which omits young trees (diameter at breast height <6 cm, height >0.3 m) and thus may underestimate ecosystem carbon sink capacity. Based on the biomass data of 137 young trees in five typical plantations on the Tibetan Plateau, independent biomass models were developed using the weighted generalized least squares method, with basic diameter as the predictor instead of DBH. Additive biomass models of controlling directly by proportion functions and controlling by the sum of equations were selected. Additive biomass models for the whole plant and each component were developed by applying weighted nonlinear seemingly uncorrelated regression. The results showed that the binary additive biomass model (R2 reached 0.90-0.99) performed better than the monadic biomass models and independent biomass models for the estimation of total biomass. For different tree species, two forms of the additive models had their own advantages, with neglectable difference in accuracy. From the perspective of forestry production, models of controlling directly by proportion functions were more practical. From the perspective of predictors extraction by remote sensing technology, suitable young tree biomass models were developed for remote sensing estimation. In this study, the additive model had high overall fitting accuracy and could accurately estimate the whole plant and component biomass of young trees in similar climatic environments.

[A remote sensing band simulation approach based on image spectral library].

The authors proposed an image spectral library based band simulation method. Firstly, the authors clustered the reference image which has the same class composition with the target image by using its pixel spectrum similarity. Secondly, the authors fetched sample from the reference image base on the former cluster image, and then built the image spectral library. Thirdly, the authors fetched the same count of each type of samples to train the simulation model. Finally, the authors simulated the target band of the target image. The experiment results show that: firstly, this method can be more precise to simulate TM blue band, and increase more than 1.2 RMSE value than that of the "Spectral Library-image" model and more than 0.6 RMSE value than that of the "image-image" model. On the other hand, our method is more stable and reliable than the "image-image" and "Spectral Library-Image" simulation model; finally, this method can be successfully applied to the blue band simulation that SPOT and MSS lacked.

[Coverage extraction and up-scaling of sparse desert vegetation in arid area].

Five kinds of remote sensing inversion models, i.e., linear spectral un-mixing model, sub-pixel un-mixing model, maximal gradient difference model, and two modified maximal gradient difference models, were used to derive f(c) from remote sensing data, and the results were compared with those measured in field, aimed to select appropriate model for deriving the data of the coverage of sparse desert vegetation in arid area. The virtual multi-scale coverage images were generated by using the simple mean scale extending method to verify the inversion information from MODIS data. It was shown that linear un-mixing spectral model had a higher precision than the other models, being applicable for deriving the data of the coverage of sparse desert vegetation, but the selection of end member was rather difficult and affected the application of the model. Sub-pixel un-mixing model was universal, high precision could be obtained based on finely detailed vegetation map, but needed to measure lots of parameters. Maximal gradient difference model was simple and easy to perform, by which, the values of the coverage of crops and bare land predicted with the original model were close to the field-measured results, but the values of the coverage of sparse vegetation were underestimated. The results predicted by the modified three-band maximal gradient difference models were close to the field-measured values, and the inversed results of vegetation coverage under different scales were ideal, indicating that these models were reliable to effectively extract the information of the coverage of sparse vegetation in arid area.