Classification of forest land attributes using multi-source remotely sensed data

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008–2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.     

太湖地区HJ-1卫星CCD数据反演气溶胶及大气校正

针对HJ-1卫星CCD数据,利用改进的暗像元法反演气溶胶光学厚度(AOD),再利用反演的AOD对其进行大气校正。将反演的气溶胶与地基太阳光度计数据进行对比验证,发现当反演的AOD值大于0.2时,反演值与地基观测值的相关系数为0.964,符合MODIS业务化反演AOD的精度要求。再将反演得到的气溶胶带入6S辐射传输模型中,对HJ-1卫星CCD数据进行大气校正实验。结果表明,该方法能有效提高HJ-1卫星CCD数据大气校正的精度,更好地复原地物的真实光谱信息。     

植被生物量高光谱遥感监测研究进展

植被生物量的评估对于研究全球碳循环具有重大意义,而高光谱遥感技术为精确反演地表属性提供了重要的数据支持。针对如何更好地应用高光谱遥感技术进行植被生物量精确反演的问题,该文详细阐述了国内外应用高光谱技术估测植被生物量的研究进展。对反演植被生物量所涉及的数据源、反演模型的构建方法及其模型特点、反演模型应用对象等内容进行了综合评述,并通过分析认为,高光谱遥感技术较传统的多光谱遥感技术在生物量反演精度上有了显著的提高。同时,对建模方法、多源遥感数据融合以及模型通用性等方面的研究进行了展望,以达到在大尺度范围内对植被生物量进行准确反演的目的。     

利用端元光谱内插的青海植被覆盖度估算研究

针对利用像元二分模型估算植被覆盖度的精度不高的问题,该文基于OSAVI,提出了选定模型参数(OSAVIs和OSAVIv)的方法,并将该方法应用于青海省植被覆盖度估算。该方法通过高分辨率影像在研究区内选取纯裸地和纯植被样点,并将纯裸地样点的OSAVI作为纯裸地样点像元的OSAVIs,将纯植被样点的OSAVI作为纯植被样点像元的OSAVIv,利用样点像元的OSAVIs和OSAVIv值,通过普通克里金内插法,求得研究区每个像元对应的OSAVIs和OSAVIv。经精度验证结果表明:此方法较常规的参数选取方法,RMSE由0.170降至0.156,MAE由0.137降至0.124。经进一步分析表明,此方法对边缘验证点和非边缘验证点的估算精度都有所提高,由于配准误差和周围地表漫反射的影响,边缘验证点的估算精度低于对非边缘验证点的估算精度。     

An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.     

Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R² = 0.65, whereas VWSI showed overestimated values with mean RD 4%.     

Review on Montane Forest Eco-hydrology in Arid Area

Since the forest eco-hydrology of arid area shows a well sensitivity of the global climate change, the relationship between forest and water and the hydrological function has attracted the attention of academic communites and management departments. This paper expounds the research progress in arid mountain forest eco-hydrology, and analyses the formation and stable mechanism of forest patch pattern, the relationship between forest and water yield and the response of forest eco-hydrology to climate change from three aspects: Forest spatial pattern, hydrological process and its response to climate change. In addition, combined with the current research progress, the research emphases in the future are put forward: Strengthening the research on the coupling of vegetation pattern and hydrological process; solving the scale problem by using remote sensing technique and model; enhancing the understanding towards the hydrological function of forest; determining the suitable forest scale which can balance the relationship of ecology and hydrological effect.     

基于SPOT6遥感影像的滩涂湿地入侵种互花米草植株高度的反演研究

本文以SPOT6 高空间分辨率遥感影像为数据源,通过植被覆盖度和地上生物量两个参数进行滩涂湿地入侵种互花米草植株高度的估算研究。结果表明,三沙湾滩涂湿地互花米草植株高度平均值为2.04 m,以1~2 m和2~3 m植株为主要分布高度,分布面积分别为6.83 km2和10.31 km2,占研究区互花米草总面积的33.83%和51.06%,小于1 m和大于3 m的互花米草仅占9.26%和5.84%。估算值与真实值之间的均方根误差为0.204,绝对误差为0.04~0.37 m。该方法是对高空间分辨率光学影像应用研究的重要尝试,其反演方法具有较好的可行性,可较为准确的获取滩涂湿地植株高度信息。     

关岭-贞丰石漠化治理示范区植被覆盖变化及其对气候因子的响应

为揭示喀斯特石漠化治理示范区植被覆盖变化以及气候因子对植被覆盖变化的影响,利用2006—2015年Landsat 30 m/16 d分辨率影像数据,采用最大合成法、NDVI差值指数和相关、偏相关分析法,系统分析示范区归一化植被指数的时空变化特征及其与气候因子的关系。结果表明:(1)2006—2015年最大NDVI平均值为0.39,NDVI较高覆盖区域在示范区南北边界,而较低覆区域以花江南岸为主;(2)2006年以来示范区极低(-1.210)、低(-0.669)和中等(-0.729)植被覆盖度呈减少趋势,高(1.359)和极高(1.247)植被覆盖度增加,整体上呈显著增加趋势;(3)本月NDVI与本月、上月、上上月降雨量和气温的相关性均通过显著水平0.05检验,且本月NDVI与本月降雨量相关性高于本月气温(RNDVI降雨 =0.782),本月NDVI与上月气温相关性高于上月降雨量(RNDVI气温 =0.771);(4)在月尺度上,示范区植被生长对降雨量无滞后期,而对气温存在1个月的滞后期。      

基于Hyperion高光谱数据和随机森林方法的岩性分类与分析

探索利用高光谱数据的岩性填图新方法是遥感地质应用领域的重要需求之一。本文运用随机森林方法和EO-1Hyperion高光谱数据,对新疆塔里木西北部柯坪地区的局部区域进行岩性分类,并对相关问题进行分析。分别利用光谱特征以及加入光谱一阶导数特征进行岩性分类,并对不同特征对岩性分类的重要性进行分析,同时与现有的基于光谱角制图方法(SAM)进行比较。结果表明,与SAM方法相比,随机森林方法得到了更高精度的岩性分类结果,是一种有效可行的岩性分类方法。根据特征重要性的排序,蓝绿光波段、短波红外波段以及相应的一阶导数特征对研究区Hyperion数据的沉积岩岩性分类贡献更大。