[Variation of leaf area index estimation in forests based on remote sensing images of different spatial scales.] / 基于不同空间尺度遥感影像估算森林叶面积指数的差异.

There are several important issues in quantitative remote sensing and product authenticity testing, including how well do the ground measurement points represent the remote sensing pixels, how to obtain the relative truth value of pixels, and how much spatial resolution can truly reflect fore-st leaf area index (LAI). In this study, the measured space scope of two plant canopy analyzers [LAI-2200 and tracing radiation and architecture of canopies (TRAC)] were calculated, which were combined with remote sensing images with three different spatial resolutions: GF-2 with 4.1 m spatial resolution, the Sentinel-2 with 10 m spatial resolution, and Landsat-8 OLI with 30 m spatial resolution, to get the relative true value of pixel at each scale. Under the condition of keeping the real observed area consistent with that obtained by remote sensing, the effects of different spatial resolution images for estimating forest LAI were compared and analyzed based on the unary exponential and multiple regression statistical models. Moreover, the optimal statistical models of the three images were tested on 30 m and 100 m scales and the spatial representation of dataset were evaluated, to find the most suitable scale for the description of forest LAI in the study area. The results showed that high resolution did not necessarily fully reflect LAI of forests. The statistical model based on three kinds of resolution images could well estimate forest LAI. Among the three models, the model based on the Sentinel-2 image had the highest accuracy, and the one based on the GF-2 images had the lowest. The test results at 30 and 100 m scales indicated that the forest LAI was overestimated by the GF-2 inversion model, and underestimated by the Landsat-8 inversion model. The statistical model based on Sentinel-2 could well estimated forest LAI in the study area.

[Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.] / 基于面向对象的QuickBird遥感影像林隙分割与分类.

Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province. In the process of object-oriented classification, 10 scales (10-100, with a step length of 10) were adopted to segment QuickBird remote sensing image; and the intersection area of reference object (RAor) and intersection area of segmented object (RAos) were adopted to evaluate the segmentation result at each scale. For segmentation result at each scale, 16 spectral characteristics and support vector machine classifier (SVM) were further used to classify forest gaps, non-forest gaps and others. The results showed that the optimal segmentation scale was 40 when RAor was equal to RAos. The accuracy difference between the maximum and minimum at different segmentation scales was 22%. At optimal scale, the overall classification accuracy was 88% (Kappa=0.82) based on SVM classifier. Combining high resolution remote sensing image data with object-oriented classification method could replace the traditional field investigation and artificial interpretation method to identify and classify forest gaps at regional scale.

[Object-oriented stand type classification based on the combination of multi-source remote sen-sing data]. / 基于多源遥感数据的面向对象林分类型识别.

The recognition of forest type is one of the key problems in forest resource monitoring. The Radarsat-2 data and QuickBird remote sensing image were used for object-based classification to study the object-based forest type classification and recognition based on the combination of multi-source remote sensing data. In the process of object-based classification, three segmentation schemes (segmentation with QuickBird remote sensing image only, segmentation with Radarsat-2 data only, segmentation with combination of QuickBird and Radarsat-2) were adopted. For the three segmentation schemes, ten segmentation scale parameters were adopted (25-250, step 25), and modified Euclidean distance 3 index was further used to evaluate the segmented results to determine the optimal segmentation scheme and segmentation scale. Based on the optimal segmented result, three forest types of Chinese fir, Masson pine and broad-leaved forest were classified and recognized using Support Vector Machine (SVM) classifier with Radial Basis Foundation (RBF) kernel according to different feature combinations of topography, height, spectrum and common features. The results showed that the combination of Radarsat-2 data and QuickBird remote sensing image had its advantages of object-based forest type classification over using Radarsat-2 data or QuickBird remote sensing image only. The optimal scale parameter for QuickBirdRadarsat-2 segmentation was 100, and at the optimal scale, the accuracy of object-based forest type classification was the highest (OA=86%, Kappa=0.86), when using all features which were extracted from two kinds of data resources. This study could not only provide a reference for forest type recognition using multi-source remote sensing data, but also had a practical significance for forest resource investigation and monitoring.

[Foliage clumping index of main vegetation types in Daxing'an Mountains, Northeast China]. / å¤§å ´å®‰å²­åœ°åŒºä¸»è¦æ¤è¢«ç±»åž‹çš„å¶ç‰‡èšé›†åº¦ç³»æ•°.

The foliage clumping index quantifies the cluster degree of the leaf spatial distribution under random canopy. It is of comparable importance for establishment of ecological models. MODIS BRDF model parameter products (MCD43A1 data) and land cover types (MCD12Q1 data) were used in this study to simulate the reflectivity of the hot spots and dark spots, and calculate the normalized difference between hotspot and darkspot (NDHD) based on the Ross-Li semi-empirical model. Least square method was then used to simulate the relationship between NDHD and the foliage clumping index and foliage clumping index products of 500-m resolution in August 2014 were retrieved. Measurements of the foliage clumping index in Daxing'an Mountains were conducted by using the TRAC (Tracing Radiation and Architecture of Canopies) sampling instrument for mo-del validation and analysis. Results showed that it was a feasible algorithm to retrieve clumping index from MCD43A1 product with the correlation of simulated data and the measured data of significance (R2=0.8879). The MODIS near infrared wave band was more sensitive than that on red band to foliage clumping index change. With the increase of the solar zenith angle, the clumping index retrieved by Ross-Li model had a linear increase (R2=0.9699), which indicated that the foliage clumping index related to the solar zenith angle.

[Quantitative driving analysis of forest biomass changes in Changbai Mountain forest region].

Based on the forest inventory data and single tree biomass model, the forest biomass in the sampling plots in Changbai Mountain forest region was calculated, and, by using the estimated forest biomass from four periods' remote sensing data and based on high accuracy remote sensing models, the changes of regional forest biomass were analyzed. In the meanwhile, the driving factors such as meteorological factors, management factors, and socio-economic factors that caused forest biomass change were selected by bootstrap method, and the driving model of forest biomass change in different time period was set up by using partial least-squares method. The Variable Importance in Projection (VIP) values representing the importance of each of the factors affecting the forest biomass change in study region were calculated. The results showed that the influence of human activity factors (VIP values) on Changhai Mountain forest biomass changes was less than that of natural factors, suggesting that the national forest protection policy for forest regions had played an obvious role. Our research broadened the content of forest biomass change driving analysis, and the introduction of calculating VIP value, which can quantitatively represent the influence of driving factors to forest biomass change, provided a new way for the quantitative analysis on forest biomass change.