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Forests play an important role in global ecosystems, but natural disasters such as drought and disease and pest-induced damages often affect growth of trees and even lead to tree death. There have been various vegetation indices (VIs) developed to detect the damages of trees using remote sensing technologies. However, developing an effective and accurate VI for detecting forest damages at an earlier stage is still challenging. In this study, a total of 32 target trees and 17 reference trees were selected from one of Chinese fir plantations that account for 24% of the afforested area in China. The barks of the target trees at 20 cm and 70 cm above the ground were respectively peeled off in August of 2016 to let the trees die. The hyperspectral data were collected from the selected branches with leaves using a high-performance spectrometer HR-1024i (Spectra Vista Corporation) with 1024 bands covering the region of wavelengths from 350 nm to 2500 nm. The spectral data collection was conducted from both the target and reference trees by non-imaging monthly from August of 2016 to February of 2017 until the target trees died. Based on statistical analysis, a Green to Red Region Spectral anGle Index (GRRSGI) was proposed and compared with other ten widely used VIs to detect the early dying process of the damaged trees. The results showed that compared with the reference trees, the damaged trees reduced the reflectance peaks of green bands and the absorption valleys of red bands and the frequency distributions of spectral reflectance from the damaged trees turned from statistically normal to non-normal in the region of 550–640 nm. This indicated that the spectral bands in the region of green to red bands were sensitive to the dying process of the target trees. Moreover, the performance of the VIs for detecting the dying process of the damaged trees varied depending on the time period after the trees damaged. The proposed GRRSGI based on the spectral bands of 550–640 nm led to the performance similar to all other VIs after three months of the damages but significantly better than them in the first two months after the damages. Except for GRRSGI, the Green-Red Spectral Area Index (GRSAI) also performed better than other VIs. Compared with GRSAI, however, the proposed GRRSGI performed slight better when the spectral data of 550–640 nm were re-sampled using the spectral intervals of 5–40 nm with an increase of 5 nm and significantly better when the re-sampling of the hyperspectral data was conducted in the region of 550–670 nm. This implied the proposed GRRSGI provided the greater potential of detecting the early dying process of the damage trees. [ABSTRACT FROM AUTHOR]

Abstract: A great challenge the US military land managers are often faced with is how to optimize road networks in order to maintain roads (including all roads, trails, and paths) for the purpose of military training and reducing negative impacts on environment. In this study, a methodology was developed to identify superfluous roads for being closed in terms of both sustainable military land carrying capacity and environment for Fort Riley. In this method, Geographic Information Systems (GIS), remote sensing, and landscape analysis technologies were combined to derive various spatial data layers of factors that had significant impacts on both military training and environment. The factors included maintenance cost of roads, road access area, military training intensity, soil erosion, water quality, landscape fragmentation, and noise production. The factors were quantified and normalized. A spatial multicriteria decision was then developed to obtain the weights of the factors, combine the data layers, and derive a priority map of all the roads for being closed. This map summarized the negative and positive impacts of the factors on environment and military land carrying capacity and can provide the US military land managers with useful guidelines and tools for determining superfluous roads in terms of both sustainable military training and environment. It is expected this effort can provide a method to quickly ascertain which roads are most cost-effective for being closed without hindering the mission and at the same time with benefits for environmental protection and thus provide the land managers with a comprehensive analysis and assessment of alternatives at their disposal. [Copyright &y& Elsevier]

In the management of natural resources, multiple variables correlated with each other usually need to be mapped jointly. However, joint mapping and spatial uncertainty analyses are very difficult mainly because of interactions among variables and imperfection of existing methods. There is abundant evidence that considering interactions among variables and spatial information from neighbors can result in improved maps. This study presents a remote sensing-aided method for that purpose. The method is based on the integration of joint sequential co-simulation with Landsat TM image for mapping and polynomial regression for spatial uncertainty analysis. The method was applied to a case study in which ground cover (GC), canopy cover (CC), and vegetation height (VH) were jointly mapped to derive a map of the vegetation cover factor for predicting soil loss. The variance contributions from the variables, their interactions, and the spatial information from neighbors leading to uncertainty of predicted vegetation cover factor were assessed. The results showed that in addition to unbiased maps, this method reproduced the spatial variability of the variables and the spatial correlation among them, and successfully quantified the effect of variation from all the components on the prediction of the vegetation cover factor. [Copyright &y& Elsevier]

Traditional parametric methods for classification of land use and land cover (LULC) types using remote sensing imagery assume a global distribution model and fail to consider local variation of categorical variables. Differently, non-parametric methods do not make any statistical assumptions but are typically sensitive to the sample sizes of training sample data that usually require a high cost to collect in the field. Geostatistical classifiers, such as indicator kriging and simulation, are local variability-based methods that exhibit great potential for image-based classification of LULC types. However, variogram models required are highly sensitive to the spatial configuration of training samples as well as sample size given a study area. Moreover, when a large number of spectral variables are considered into kriging systems, modeling the variograms and cross-variograms would be problematic. To circumvent these issues, this study extended the geostatistical methods from a 2-dimensional geographic space to a m -dimensional image feature space to derive feature-space indicator variograms (FSIVs). Moreover, a novel stochastic simulation classification algorithm, Feature-Space Indicator Simulation (FSIS), was proposed and examined for classification of LULC types in Duolun County located in Inner Mongolia and in Huang-Feng-Qiao (HFQ) forest farm, Hunan of China. In Duolun, six LULC types were involved and in HFQ a complicated forest landscape consisting of nine forest types plus water, built-up area, and agricultural/bare soil, was classified. The classification results of FSIS were compared with another feature-space geostatistical classifier – feature-space indicator kriging (FSIK), a traditional parametric method – maximum likelihood (ML), a widely used nonparametric method – support vector machine (SVM), and a recently popular method – random forest (RF). The results showed that compared with ML, SVM and RF, in both study areas FSIS statistically significantly increased the accuracy of the classifications by 10.0–29.9% for percentage correct and 19.0–47.6% for Kappa statistic. Compared with FSIK, FSIS also improved the classification accuracy but the accuracy increases were relatively smaller with the percentages correct of 3.5% and 7.6% and the Kappa values of 4.6% and 8.6% for Duolun and HFQ, respectively. Moreover, FSIS led to the spatial uncertainties of the classification estimates as the quality measure of the estimates. In addition, the results also demonstrated that FSIVs were sensitive to the within-class heterogeneity but not very much to the size of training samples. Overall, FSIS exhibited the greater potential to improve the classification accuracy of LULC and forest types using remote sensing image. [ABSTRACT FROM AUTHOR]