Tree species classification in an extensive forest area using airborne hyperspectral data under varying light conditions

Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive process based on multi-flightline airborne hyperspectral data is lacking over large,forested areas influenced by both the effects of bidirectional reflectance distribution function(BRDF)and cloud shadow contamination.In this study,hyperspectral data were collected over the Mengjiagang Forest Farm in Northeast China in the summer of 2017 using the Chinese Academy of Forestry's LiDAR,CCD,and hyperspectral systems(CAF-LiCHy).After BRDF correction and cloud shadow detection processing,a tree species classification workflow was developed for sunlit and cloud-shaded forest areas with input features of minimum noise fraction reduced bands,spectral vegetation indices,and texture information.Results indicate that BRDF-corrected sunlit hyperspectral data can provide a stable and high classification accuracy based on representative training data.Cloud-shaded pixels also have good spectral separability for species classification.The red-edge spectral information and ratio-based spectral indices with high importance scores are recommended as input features for species classification under varying light conditions.According to the classification accuracies through field survey data at multiple spatial scales,it was found that species classification within an extensive forest area using airborne hyperspectral data under various illuminations can be successfully carried out using the effective radiometric consistency process and feature selection strategy.

Progress of Geological Survey Using Airborne Hyperspectral Remote Sensing Data in the Gansu and Qinghai Regions

Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.

New Identification of Sericite Subclass Minerals Using Airborne Hyperspectral Data in the Xitan Region of Gansu Province and its Significance in Gold Ore Prospecting

Objective Hyperspectral remote sensing has attracted much attention in remote sensing research during recent years. It can elaborately identiry spectral characteristics of different objects by acquiring continuous spectral curves of ground objects, and can thus provide more information for geological research （Zhao Yingjun et al., 2015）. With the deepening hyperspectral remote sensing research, scholars have focused from the classification of alteration minerals to the identification of subclass minerals in order to explore their significance fbr ore prospecting. This work utilized hyperspectral remote sensing technology in the Xitan region of Gansu Province to identify limonite and two types of sericite subclass minerals, and conducted field verification and geochemical survey. In addition, we analyzed the geological environment of subclass sericite minerals （Van Ruitenbeek et al., 2006） to provide evidence for gold ore prospecting.

An airborne pushbroom hyperspectral imager with wide field of view

An airborne pushbroom hyperspectrai imager （APHI） with wide field （42° field of view） is presented. It is composed of two 22° field of view （FOV） imagers and can provide 1304 pixels in spatial dimension, 124 bands in spectral dimension in one frame. APHI has a bandwidth ranging from 400 to 900 nm. The spectral resolution is 5 nm and the spatial resolution is 0.6 m at 1000-m height. The implementation of this system is helpful to overcome the restriction of FOV in pushbroom hyperspectral imaging in a more feasible way. The electronic and optical designs axe also introduced in detail.

Assessing cotton defoliation, regrowth control and root rot infection using remote sensing technology

Cotton defoliation and post-harvest destruction are important cultural practices for cotton production.Cotton root rot is a serious and destructive disease that affects cotton yield and lint quality.This paper presents an overview and summary of the methodologies and results on the use of remote sensing technology for evaluating cotton defoliation and regrowth control methods and for assessing cotton root rot infection based on published studies.Ground reflectance spectra and airborne multispectral and hyperspectral imagery were used in these studies.Ground reflectance spectra effectively separated different levels of defoliation and airborne multispectral imagery permitted both visual and quantitative differentiations among defoliation treatments.Both ground reflectance and airborne imagery were able to differentiate cotton regrowth among different herbicide treatments for cotton stalk destruction.Airborne multispectral and hyperspectral imagery accurately identified root rot-infected areas within cotton fields.Results from these studies indicate that remote sensing can be a useful tool for evaluating the effectiveness of cotton defoliation and regrowth control strategies and for detecting and mapping root rot damage in cotton fields.Compared with traditional visual observations and ground measurements,remote sensing techniques have the potential for effective and accurate assessments of various cotton production operations and pest conditions.