文献类型：期刊文献

中文题名：结合机载LiDAR和LANDSAT ETM+数据的温带森林郁闭度估测

英文题名：Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data

作者：张瑞英[1,2] 庞勇[2] 李增元[2] 包玉海[1]

第一作者：张瑞英

机构：[1]内蒙古师范大学地理科学学院;[2]中国林业科学研究院资源信息研究所

年份：2016

卷号：40

期号：2

起止页码：102-115

中文期刊名：植物生态学报

外文期刊名：Chinese Journal of Plant Ecology

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家高技术研究发展计划(863计划)(2012AA12A306);国家重点基础研究发展计划(973计划)(2013CB733404)

语种：中文

中文关键词：LANDSAT;ETM+;机载激光雷达;森林郁闭度;植被指数;多元逐步回归;随机森林;Cubist

外文关键词：LANDSAT ETM＋; airborne laser scanning （ALS）; forest canopy closure; vegetation index; multi-variable stepwise regression; random forest; Cubist

分类号：S127

摘要：森林郁闭度是森林资源调查中的一个重要因子,在森林生态系统管理中具有重要作用。研究如何有效地将激光雷达数据应用于森林郁闭度遥感估测具有重大意义。激光雷达数据的应用能够有效地弥补传统地面调查耗时、费力等不足,不仅可以快速、准确地获取郁闭度遥感估测的模型训练数据和验证数据,还有助于进一步推广应用于大区域的森林郁闭度反演,为林业资源调查提供有力的依据。该研究结合激光雷达数据和LANDSAT ETM+数据估测温带森林郁闭度。以高密度机载激光雷达(ALS)点云数据估算的郁闭度作为模型训练数据和验证数据,通过LANDSAT ETM+影像数据计算得到的8种植被指数作为自变量,使用多元逐步回归(MSR)、随机森林(RF)和Cubist 3种模型,对内蒙古大兴安岭根河林区森林郁闭度进行估测。经验证,Cubist模型的效果比较好(决定系数R2=0.722,均方根误差RMSE=0.126,相对均方根误差r RMSE=0.209,估计精度EA=79.883%)。结果表明,结合激光雷达数据和LANDSAT ETM+影像数据估算温带森林郁闭度非常有潜力。但要将其推广应用于更大区域尺度的森林郁闭度遥感估测,模型的预测能力还有待进一步改进和提高;自变量应尝试加入更多种类遥感数据和其他遥感因子参与建模,例如采用地形因子、高分辨率遥感影像提取纹理特征等,最大可能地减少光学影像、植被指数、地形阴影等带来的影响,提高反演精度;激光雷达数据计算得到的郁闭度的准确性和可靠性还需进一步验证。
Aims Forest canopy closure is one of the essential factors in forest survey, and plays an important role in forest ecosystem management. It is of great significance to study how to apply LiDAR （light detection and ranging） data efficiently in remote sensing estimation of forest canopy closure. LiDAR can be used to obtain data fast and accu- rately and therefore be used as training and validation data to estimate forest canopy closure in large spatial scale. It can compensate for the insufficiency （e.g. labor-intensive, time-consuming） of conventional ground survey, and provide foundations to forest inventory. Methods In this study, we estimated canopy closure of a temperate forest in Genhe forest of Da Hinggan Ling area, Nei Mongol, China, using LiDAR and LANDSAT ETM＋ data. Firstly, we calculated the canopy closure from ALS （Airborne Laser Scanning） high density point cloud data. Then, the estimated canopy closure from ALS data was used as training and validation data to modeling and inversion from eight vegetation indices computed from LANDSAT ETM＋ data. Three approaches, multi-variable stepwise regression （MSR）, random forest （RF） and Cubist, were developed and tested to estimate canopy closure from these vegetation indices, respectively. Important findings The validation results showed that the Cubist model yielded the highest accuracy compared to the other two models （determination coefficient （R2） = 0.722, root mean square error （RMSE） = 0.126, relative root mean square error （rRMSE） = 0.209, estimation accuracy （EA） = 79.883%）. The combination of LiDAR dataand LANDSAT ETM＋ showed great potential to accurately estimate the canopy closure of the temperate forest. However, the model prediction capability needs to be further improved in order to be applied in larger spatial scale. More independent variables from other remotely sensed datasets, e.g. topographic data, texture information from high-resolution imagery, should be added into the model. These variables can help to reduce the influence of optical image, vegetation indices, terrain and shadow and so on. Moreover, the accuracy of the LiDAR-derived canopy closure needs to be further validated in future studies.