Estimation of forest parameters based on TM imagery and statistical analysis

One of the primary forestry research interests lies in estimating forest stand parameters by applying empirical or semi-empirical model to establish the relationship between the forest stand parameters and remote sensing data. Using remote sensing image and the inventory data from 2 compartments in northeast Florida, U.S.A., this paper explored the correlation between forest stand parameters and Landsat TM spectral digital number （DN） value. Results showed that less than 50% of the total variance could be explained by linear regression models with only either a single band or such vegetation indices as vegetation index （VI） or normalized difference vegetation index （NDVI） as predicators. In consequence, multi-linear regression models which synthesized more predicators were introduced to estimate forest parameters. Regression results were tested in terms of the other group of data, and verification showed a better capability of explaining over 75% variance except for forest density. The weakness and further improvement of prediction models were also discussed in the article. This paper is expected to provide a better understanding of the relationship between TM spectral and forest characteristics

An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image

Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field surveys.Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image.Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.

A model for the retrieval of suspended sediment concentrations in Taihu Lake from TM images

A study was conducted in the Taihu Lake with the aim of deriving a model for the retrieval of suspended sediment （SS） concentrations from Landsat TM images and in situ sampled data. The correlation between suspended sediment concentrations of lake and the reflectance obtained from the TM images is significant. By TM images and in situ sampled data in summer and winter, we obtained a comparative uniform model for the retrieval of suspended sediment concentrations in the Taihu Lake, that is lnSS = a＊（R3/R1） ＋ b, where lnSS is the natural logarithm of the suspended sediment concentration, Rl and R3 are the reflectance coincident with the 1st band and the 3rd band in TM images, a and b are the regression coefficients. Furthermore, we analysed the errors particularly to make sure the model is valid. The model is accurate to within 0.33（RMSE）, suggesting that this model may be applicable to predict suspended sediment in the Taihu Lake from TM image throughout the year.

Improved Geological Interpretation Using Landsat TM Data in Lancang-Jinghong Area, Yunnan Province, China

Landsat TM digital spectral data of Lancang Jinghong area (Yunnan P ro vince) has been used for the purpose of geological interpretation. To meet this object, different image processing techniques including selected band color comp osites, principal component analysis and IHS decorrelation stretching are used t o improve the discrimination of different lithological and structural features i n the area.It was found that IHS decorrelation stretching images obtained from t he transformation of false color composite 741 (in red, green and blue) prov ided the best results based on the original data.By combining the characteristic s of images produced by different approaches and other canonically transformed i mages with available geological data and surface observations, the geological in terpretation could be done with satisfactory degree of accuracy.

Delineating suspended sediment concentration patterns in surface waters of the Changjiang Estuary by remote sensing analysis

Three Landsat TM imageries （taken on 18 May 1987,4 August 1998 and 28 July 2007） were used as the data source to identify the spatial and temporal variations of the suspended sediment concentration （SSC） in surface waters of the Changjiang Estuary.Atmospheric correction was carried out to determine the water-leaving reflectance using the FLAASH module.A regression equation between surveyed SSC and suspended sediment index was chosen to retrieve the SSC from the Landsat TM images.In addition,tidal harmonic analysis was performed to calculate tidal conditions corresponding to the acquisition time of satellite images.The results show that the SSC spatial patterns are similar to the in situ observation results,which show the highest SSC in the region of turbidity maximum zone in the Changjiang Estuary.For the period of 1987 to 2007,the SSC pattern is controlled mainly by tidal dynamic conditions and wind speeds,rather than sediment discharges from the river.

Application of an expert knowledge system in the study of forest spatial patterns

For the sake of exploring how the pattern of Chinese pine （Pinus massoniana Lamb） community changed after the invasion of the pine wood nematode （Bursaphelenchus xylophilus （Steiner ＆ Buhrer） Niclde） in Zhoushan, Zhejiang Province, we established a test area in the local Chinese pine community. Landsat5 TM images from 1991 and 2006 were integrated with auxiliary data from field investigation and spectral data as additional sources of information. A method of expert knowledge classifier was applied to establish the expert knowledge dataset of the main vegetation cover types from which we obtained a forest type distribution map. The spatial patterns and stability of the forest, before and after the invasion of the pine wood nematode, were analyzed in terms of community patterns. The results indicated that the predominant coniferous forest type changed to a mixed forest. As a result, the forest structure became complex and the interaction between coniferous forest patches became weakened over the period from 1991 to 2006. Therefore, the resistance of the forest eco-system to plant diseases and insect pests and the stability of forest eco-system enhanced.