天津滨海新区地表水悬浮物浓度遥感反演研究

针对传统的实地地表水水质监测测量方法费时费力,且存在监测布设点有限,无法反映水质的时空变化情况等问题,该文以天津滨海新区的Landsat-8卫星影像作为数据源,将实地水样采集、实验室测量分析和遥感影像不同波段像元亮度值结合,对滨海新区海河水体的悬浮物浓度,分别建立基于统计回归和神经网络的两种经验算法反演模型。结果表明,神经网络模型的反演结果能较准确地反映实际水体的悬浮物浓度分布情况,为该地区地表水水质监测提供一种可行的解决方案。     

Remote sensing of spider mite damage in California peach orchards

Remote sensing techniques can decrease pest monitoring costs in orchards. To evaluate the feasibility of detecting spider mite damage in orchards, we measured visible and near infrared reflectance of 1153 leaves and 392 canopies in 11 peach orchards in California. Pairs of significant wavelengths, identified by Partial Least Squares regression, were combined into normalized difference indices. These and 9 previously published indices were evaluated for correlation with mite damage.     

Land surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems

Forest fires are one of the most important causes of environmental alteration in Mediterranean countries. Discrimination of different degrees of burn severity is critical for improving management of fire-affected areas. This paper aims to evaluate the usefulness of land surface temperature (LST) as potential indicator of burn severity. We used a large convention-dominated wildfire, which occurred on 19–21 September, 2012 in Northwestern Spain. From this area, a 1-year series of six LST images were generated from Landsat 7 Enhanced Thematic Mapper (ETM+) data using a single channel algorithm. Further, the Composite Burn Index (CBI) was measured in 111 field plots to identify the burn severity level (low, moderate, and high). Evaluation of the potential relationship between post-fire LST and ground measured CBI was performed by both correlation analysis and regression models. Correlation coefficients were higher in the immediate post-fire LST images, but decreased during the fall of 2012 and increased again with a second maximum value in summer, 2013. A linear regression model between post-fire LST and CBI allowed us to represent spatially predicted CBI (R-squared_adj > 85%). After performing an analysis of variance (ANOVA) between post-fire LST and CBI, a Fisher's least significant difference test determined that two burn severity levels (low-moderate and high) could be statistically distinguished. The identification of such burn severity levels is sufficient and useful to forest managers. We conclude that summer post-fire LST from moderate resolution satellite data may be considered as a valuable indicator of burn severity for large fires in Mediterranean forest ecosytems.     

Evaluating the robustness of models developed from field spectral data in predicting African grass foliar nitrogen concentration using WorldView-2 image as an independent test dataset

In this paper, we evaluate the extent to which the resampled field spectra compare with the actual image spectra of the new generation multispectral WorldView-2 (WV-2) satellite. This was achieved by developing models from resampled field spectra data and testing them on an actual WV-2 image of the study area. We evaluated the performance of reflectance ratios (RI), normalized difference indices (NDI) and random forest (RF) regression model in predicting foliar nitrogen concentration in a grassland environment. The field measured spectra were used to calibrate the RF model using a randomly selected training (n = 70%) nitrogen data set. The model developed from the field spectra resampled to WV-2 wavebands was validated on an independent field spectral test dataset as well as on the actual WV-2 image of the same area (n = 30%, bootstrapped a 100 times). The results show that the model developed using RI could predict nitrogen with a mean R² of 0.74 and 0.65 on an independent field spectral test data set and on the actual WV-2 image, respectively. The root mean square error of prediction (RMSE %) was 0.17 and 0.22 for the field test data set and the WV-2 image, respectively. Results provide an insight on the magnitude of errors that are expected when up-scaling field spectral models to airborne or satellite image data. The prediction also indicates the unceasing relevance of field spectroscopy studies to better understand the spectral models critical for vegetation quality assessment.