Abstract:We describe the design, implementation and performance of a novel airborne system, which integrates commercial waveform LiDAR, CCD (Charge-Coupled Device) camera and hyperspectral sensors into a common platform system. CAF's (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System is a unique system that permits simultaneous measurements of vegetation vertical structure, horizontal pattern, and foliar spectra from different view angles at very high spatial resolution (~1 m) on a wide range of airborne platforms. The horizontal geo-location accuracy of LiDAR and CCD is about 0.5 m, with LiDAR vertical resolution and accuracy 0.15 m and 0.3 m, respectively. The geo-location accuracy of hyperspectral image is within 2 pixels for nadir view observations and 5-7 pixels for large off-nadir observations of 55˝with multi-angle modular when comparing to LiDAR product. The complementary nature of LiCHy's sensors makes it an effective and comprehensive system for forest inventory, change detection, biodiversity monitoring, carbon accounting and ecosystem service evaluation. The LiCHy system has acquired more than 8000 km 2 of data over typical forests across China. These data are being used to investigate potential LiDAR and optical remote sensing applications in forest management, forest carbon accounting, biodiversity evaluation, and to aid in the development of similar satellite configurations. This paper describes the integration of the LiCHy system, the instrument performance and data processing workflow. We also demonstrate LiCHy's data characteristics, current coverage, and potential vegetation applications.

Forest precision classification products were the basic data for surveying of forest resource, updating forest subplot information, logging and management of forest. However, due to the diversity of stand structure, complexity of the forest growth environment, it is difficult to discriminate forest tree species using multi-spectral image. The airborne hyper-spectral images can obtain high spatial and spectral resolution imagery of forest canopy, so it may be useful for tree species level classification. The aim of this paper was to test the effective of combining spatial and spectral features in airborne hyper-spectral image classification. The CASI hyper spectral image data were acquired from Liangshui natural reserves area. First the MNF (minimum noise fraction) transform method for to reduce the hyperspectral image dimensionality and highlighting variation. Second, the grey level cooccurrence matrix (GLCM) is used to extract the texture features of forest tree canopy. Thirdly the texture and the spectral features of forest canopy were fused to classify the trees species using support vector machine (SVM) with different kernel functions. The results showed that when using the SVM classifier, MNF and texture-based features combined with linear kernel function can achieve the best overall accuracy which was 85.92 %. It also confirmed the belief that combined the spatial and spectral information can improve the accuracy of tree species classification.

Quantifying the response of landscape metrics to an altering observation scale is crucial to understanding environmental changes and managing ecosystem services. Whereas the scaling behaviors of landscape metrics in spatial heterogeneity analysis have been well identified by previous research, there remains a need to examine these effects in areas undergoing rapid change. Here, we aim to reveal the landscape scale effect in the Three Gorges Reservoir (TGR) area, China, using a case study on Zigui County. We applied a suite of common landscape metrics (12 indices at the class level and 17 indices at the landscape level) to characterize the landscape pattern and examine the response of the metrics to altering grain size using a series of land-use/land-cover data with gradient resolutions. The results reveal that significant scale effects exist in most pattern metrics in the TGR landscape. In addition, the different responses to the altering grain size occurred with different landscape metrics and various land-use/landcover types. With respect to changing grain size, all of the selected pattern metrics at the landscape level displayed high or medium sensitivity in response to changing grain size except the Fractal Dimension Index and the landscapediversity indices. The behavior of the metrics in response to altering grain size can be grouped into four types (Type 1, Type 2, Type 3, and Type 4). The class-level metrics with high sensitivity were Mean Patch Size, the Contiguity Index, the Euclidean Nearest-Neighbor Distance, the Perimeter-Area Ratio, and Patch Density for all landuse/land-cover types, whereas low sensitivities were detected in the response of the Fractal Dimension Index and the Largest Patch Index. Based on the response to the altering resolution of input data, the class-level metrics could be grouped into three types (Type a, Type b, and Type c). Considering the scaling behavior of landscape metrics, we suggest using a set of suitable remote-sensing images to quantify the landscape pattern in the TGR landscape and similar areas.