Estimation of forest volumes by integrating Landsat TM imagery and forest inventory data

Accurate information about forest volumes is essential for forest management planning. The survey interval of the Forest Resource Inventory of China (FRIC) is too long to meet the demand for timely decision-making required for forest protection, management, and utilization. Analysis of satellite imagery provides good potential for more frequent reporting of forest parameters. In this study, we describe an application of the k-nearest neighbors (kNN) method to Landsat TM imagery for improving estimation of forest volumes. Several spectral features were tested and compared in forest volume estimations, including normalized difference vegetation index, environmental vegetation index, and the combination of the spectral features. The combined index resulted in the most accurate volume estimations. The kNN estimator and the combined index were then used in forest volume estimation. The estimation error (RMSE) of the total volume was 44.2%, much lower than those for Larix forest (the RMSE was 51.7%) and those for the Korean pine and broadleaved forests (the estimation errors were over 71.7% and 88.19%, respectively). This preliminary study demonstrates the potential of forest volume estimations with remote sensing data to provide useful information for forest management if only limited ground information is available.     

热成像型森林火灾报警系统设计及其图像处理方法研究

本文介绍了一种新型的森林火灾监测系统,着重对其图像处理方法进行了研究。该系统采用红外成像技术、数字图像处理技术、无线通信技术、计算机控制技术对现场数据进行快速处理,具有很好的实时性,对森林火灾的预防与及时扑救起到了很重要作用。     

林用电子外业手簿的开发与应用

电子外业手簿(简称为EFB)专门用来记录和处理外业测量数据，它体积小，携带方便，有信息存储和通讯等功能，配有特殊的专业软件包，且有：BASIC编程环境供林业工作者编写合适的程序。本文以EFB其中一代产品PC—E500为工具，论述了它配合罗盘在森林经理中的应用，列出了几种应用实例．在测立木材积时，只要输入坡度、罗盘测距时的上下丝读数、测树的俯仰角度以及该树的胸径和树干形数，PC—E500就能自动计算出数高及材积，并存储这些数据直到测量结束(文中给出了测立木材积的代码)。在测量林中不规则地块面积时，在边缘选好特征点并立好标杆，输入每次测量的上下丝读数、方位角及坡度值，PC—E500可在测量结束时计算出地块面积，文末叙及了PC—E500与微机的通讯方法及优点，展望了它在林业应用中的前景。     

森林资源破坏的危害及森林资源的恢复与重建

分析我国森林在自然因素和人为因素的干扰下,造成生态环境恶化,自然灾害频发,水土流失严重,土壤肥力下降,物种减少等一系列不利影响,着重研究了森林在国民经济发展中的巨大作用.根据1998年洪灾造成3 290亿元巨大损失的实际情况,因地制宜地提出几条对策.以恢复与重建森林资源.     

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation

The Moderate Resolution Imaging Radiometer (MODIS) is the primary instrument in the NASA Earth Observing System for monitoring the seasonality of global terrestrial vegetation. Estimates of 8-day mean daily gross primary production (GPP) at the 1 km spatial resolution are now operationally produced by the MODIS Land Science Team for the global terrestrial surface using a production efficiency approach. In this study, the 2001 MODIS GPP product was compared with scaled GPP estimates (25 km²) based on ground measurements at two forested sites. The ground-based GPP scaling approach relied on a carbon cycle process model run in a spatially distributed mode. Land cover classification and maximum annual leaf area index, as derived from Landsat ETM+ imagery, were used in model initiation. The model was driven by daily meteorological observations from an eddy covariance flux tower situated at the center of each site. Model simulated GPPs were corroborated with daily GPP estimates from the flux tower. At the hardwood forest site, the MODIS GPP phenology started earlier than was indicated by the scaled GPP, and the summertime GPP from MODIS was generally lower than the scaled GPP values. The fall-off in production at the end of the growing season was similar to the validation data. At the boreal forest site, the GPP phenologies generally agreed because both responded to the strong signal associated with minimum temperature. The midsummer MODIS GPP there was generally higher than the ground-based GPP. The differences between the MODIS GPP products and the ground-based GPPs were driven by differences in the timing of FPAR and the magnitude of light use efficiency as well as by differences in other inputs to the MODIS GPP algorithm—daily incident PAR, minimum temperature, and vapor pressure deficit. Ground-based scaling of GPP has the potential to improve the parameterization of light use efficiency in satellite-based GPP monitoring algorithms.     

容许两个盘故障的磁盘阵列数据布局与图分解的条件和存在性研究

从一个新的途径讨论容许两个盘故障的磁盘阵列数据布局：把由数据单元和通过“异或”运算得到的校验单元组成的校验组用一个图表示，把校验组容许两个盘故障的阵列布局归结为校验组的单元集合的划分，进而转化为校验组的图的顶点和边组成集合的满足一定条件的分解．证明了校验组容许两个盘故障的单元集合划分的充分必要条件及存在性；讨论了优化阵列布局方案性能的条件；给出了阵列布局的步骤．从而为设计具有最优性能的容许两个盘故障的磁盘阵列数据布局方案提供了有效的途径．     

森林林冠对降雨量截留的实验分析

以实测资料为基础，分析了林冠对降雨量的截留量。     

Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest

Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine (Pinus ponderosa) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAI_e) (r²=0.55, p<0.001) and with the lidar-derived estimate of tree occurrence (r²=0.79, p<0.001). The enhanced vegetation index (EVI) calculated from IKONOS imagery showed a negative correlation with the field measured tree canopy effective LAI and lidar tree cover response (r²=0.30, r=−0.55 and r²=0.41, r=−0.64, respectively; p<0.001) and further analyses indicate a strong linear relationship between EVI and the IKONOS-derived grass fraction (r²=0.99, p<0.001). We also found that using EVI resulted in better agreement with the subpixel vegetation fractions in this ecosystem than using normalized difference of vegetation index (NDVI). Coarsening the IKONOS data to 30 m resolution imagery revealed a stronger relationship with lidar tree measures (r²=0.77, p<0.001) than at 4 m resolution (r²=0.58, p<0.001). Unmixed tree/shade fractions derived from 30 m resolution ETM+ imagery also showed a significant correlation with the lidar data (r²=0.66, p<0.001). These results demonstrate the power of using high resolution lidar data to validate spectral unmixing results of satellite imagery, and indicate that IKONOS data and Landsat 7 ETM+ data both can serve to make the important distinction between tree/shade coverage and exposed understory grass coverage during peak summertime greenness in a ponderosa pine forest ecosystem.     

Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests

We evaluated whether satellite radar remote sensing of landscape seasonal freeze-thaw cycles provides an effective measure of active growing season timing and duration for boreal and subalpine evergreen forests. Landscape daily radar backscatter measurements from the SeaWinds scatterometer on-board the QuikSCAT satellite were evaluated across a regional network of North American coniferous forest sites for 2000 and 2001. Radar remote sensing measurements of the initiation and length of the growing season corresponded closely with both site measurements and ecosystem process model (BIOME-BGC) simulations of these parameters because of the sensitivity of the K_u-band scatterometer to snow cover freeze-thaw dynamics and associated linkages between growing season initiation and the timing of seasonal snowmelt. In contrast, remote sensing estimates of the timing of growing season termination were either weakly or not significantly associated with site measurements and model simulation results, due to the relative importance of light availability and other environmental controls on stand phenology in the fall. Regional patterns of estimated annual net primary production (NPP) and component photosynthetic and autotrophic respiration rates for the evergreen forest sites also corresponded favorably with remote sensing estimates of the seasonal timing of spring thaw and associated growing season length, indicating the importance of these parameters in determining spatial and temporal patterns of NPP and the potential utility of satellite radar remote sensing for regional monitoring of the terrestrial biosphere.     

Radar remote sensing of the spring thaw transition across a boreal landscape

The seasonal transition of the boreal forest between frozen and non-frozen conditions affects a number of ecosystem processes that cycle between winter dormant and summer active states. The relatively short K_u-band wavelength (2.14 cm) of the space-borne NASA scatterometer (NSCAT) is sensitive to changes in dielectric properties, associated with large-scale changes in the relative abundance and phase (frozen or thawed) of canopy and surface water. We used a temporal change detection analysis of NSCAT daily radar backscatter measurements to characterize the 1997 seasonal spring thaw transition period across the 10⁶ km² BOREAS study region of central Canada. In the spring, air temperature transitions from frozen to non-frozen conditions and surface observations of seasonal snow cover depletion were generally coincident with decreases in radar backscatter of more than 2.9 dB, regardless of regional landcover characteristics. We used a temporal classification of NSCAT daily differences from 5-day smoothed backscatter values to derive three simple indices describing the initiation, primary event and completion of the spring thaw transition period. Several factors had a negative impact on the relative accuracy of NSCAT-based results, including periodic gaps in NSCAT daily time-series information and a large (i.e., >2 cm day⁻¹) spring rainfall event. However, these results were generally successful in capturing the seasonal transition of the region from frozen to non-frozen conditions, based on comparisons with regional weather station network information. These results illustrate the potential for improved assessment of springtime phenology and associated ecosystem dynamics across high latitude regions, where field based and optical remote-sensing methods are substantially degraded by frequent cloud cover, low solar illumination and sparse surface weather station networks.