Fuzzy Based Approach for Moist Deciduous Forest Identification Using MODIS Temporal Data

Fuzzy based soft classification have been used immensely for handling the mixed pixel and hence to extract the single class of interest. The present research attempts to extract the moist deciduous forest from MODIS temporal data using the Possibilistic c-Means (PCM) soft classification approach. Temporal MODIS (7 dates) data were used to identify moist deciduous forest and temporal AWiFS (7 dates) data were used as reference data for testing. The Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Normalized Difference Vegetation Index (TNDVI) were used to generate the temporal vegetation indices for both the MODIS and the AWiFS datasets. It was observed from the research that the MODIS temporal NDVI data set1, which contain the minimum number of images and avoids the temporal images corresponding to the highest frequency stages of onset of greenness (OG) and end of senescence (ES) activity of moist deciduous forest have been found most suitable data set for identification of moist deciduous forest with the maximum fuzzy overall accuracy of 96.731 %.     

Estimation of Above Ground Biomass for Central Indian Deciduous Forests Using ALOS PALSAR L-Band Data

Reliable and accurate estimates of tropical forest above ground biomass (AGB) are important to reduce uncertainties in carbon budgeting. In the present study we estimated AGB of central Indian deciduous forests of Madhya Pradesh (M.P.) state, India, using Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) L-band data of year 2010 in conjunction with field based AGB estimates using empirical models. Digital numbers of gridded 1?×?1° dual polarization (HH & HV) PALSAR mosaics for the study area were converted to normalized radar cross section (sigma naught - σ⁰). A total of 415 sampling plots (0.1 ha) data collected over the study area during 2009–10 was used in the present study. Plot-level AGB estimates using volume equations representative to the study area were computed using field inventory data. The plot-level AGB estimates were empirically modeled with the PALSAR backscatter information in HH, HV and their ratios from different forest types of the study area. The HV backscatter information showed better relation with field based AGB estimates with a coefficient of determination (R²) of 0.509 which was used to estimate spatial AGB of the study area. Results suggested a total AGB of 367.4 Mt for forests of M.P. state. Further, validation of the model was carried out using observed vs. predicted AGB estimates, which suggested a root mean square error (RMSE) of ±19.32 t/ha. The model reported robust and defensible relation for observed vs. predicted AGB values of the study area.     

多源遥感数据支持下的区域性森林冠层高度估测

结合机载LiDAR数据,提出了一种改进的GLAS光斑点冠层高度地形校正模型,以校正后的GLAS光斑点作为输入样本,结合MODIS遥感影像,利用支持向量回归（SVR）的方法对研究区森林冠层高度进行分生态区估测,并利用野外调查数据和机载LiDAR冠层高度结果对估测结果进行验证。结果显示:研究区的坡度等级直接影响GLAS光斑点森林冠层高度估测精度,改进的地形校正模型可以较好的减小坡度对GLAS光斑点森林冠层高度估测的影响,模型精度RMSE稳定在3.25~3.48 m;不同生态分区的SVR模型估测精度较为稳定,其RMSE=6.41~7.56 m;与算数平均高相比,样地的Lorey's高与制图结果拟合最好,不同生态分区平均估测精度为80.3%。机载LiDAR冠层高度结果的验证平均精度为79.5%,和Lorey's高验证结果呈现较好的一致性。     

利用ESAR极化数据的复杂地形区森林地上生物量估算

利用机载E-SAR传感器获取的P-波段全极化SAR数据与实测林分样地数据,分析不同极化方式后向散射系数在地形起伏区与森林地上生物量（AGB）的响应关系,以改进的水云模型为基础,建立了融入地形因子的分析性模型。采用遗传算法确定模型的最优参数,并对模型在不同坡度情况下的可靠性、稳定性进行分析,同时通过与常用模型相对比,确定水云分析模型在复杂地形区估算AGB的优势。结果表明：在森林AGB处于较低值的情况下,后向散射系数（HH、HV、VV）变化趋势与AGB变化趋势保持一致,但随着AGB值的增大,这种一致性仅在HV极化方式下继续保持,因此相比之下,HV极化方式更适用于复杂地形区生物量的估算。地形对森林AGB的估算具有极大的影响,后向散射系数与AGB的相关性随着地形坡度的增加而减小。5种模型估算森林AGB的能力大小排序为：水云分析模型 > 二次模型 > 对数模型 > 指数模型 > 线性模型。地形起伏较小的地区估算稳定性排序为：水云分析模型 > 二次模型 > 对数模型 > 指数模型>线性模型。地形起伏较大的地区估算稳定性排序为。水云分析模型 > 二次模型 > 线性模型 > 指数模型 > 对数模型。利用水云分析模型对研究区AGB估算,其实测AGB与模型估算的生物量值决定系数为0.597,RMSE为30.876 t/hm²,拟合精度为77.40%。     

多参数SAR数据森林应用潜力分析

利用多参数机载全球雷达(GlobeSAR)数据和航天飞机成像雷达(SIR-C/X-SAR)数据,分别在我国南、北方两个试验区进行森林识别与分类,以及蓄积量估测的试验.为了更好地了解雷达后向散射与森林结构特征的关系,分别从雷达图像上提取了后向散射系数和强度,进行森林类型识别效果的分析,以及森林结构参数与雷达后向散射强度的相关分析.结果显示多波段、多极化SAR数据能有效地识别不同类型的森林.雷达的后向散射强度对森林的结构参数,尤其是森林的平均胸径和高度较为敏感,据此对试验区的森林蓄积量进行了估测,并分析了多参数SAR在森林应用中的潜力.     

Estimation of Seasonal Sun-Induced Fluorescence Dynamics of Indian Tropical Deciduous Forests using SCOPE and Sentinel-2 MSI

This paper presents a new approach to estimate spatial Sun-Induced Fluorescence (SIF) using the empirical relationship between simulated Canopy Chlorophyll Concentration (CCC) and simulated SIF. PROSAIL model [PROpriétésSPECTrales (PROSPECT) and Scattering by Arbitrarily Inclined Leaves (SAIL) models] was used to simulate CCC. CCC maps were generated through an Automated Radiative Transfer Model Operator (ARTMO) using the PROSAIL model and Sentinel-2 Multi-Spectral Imager (MSI) imagery. The Soil Canopy Observation, Photochemistry, and Energy fluxes (SCOPE) model was used to simulate SIF emitted at 740 nm (SIF₇₄₀), at 760 nm (SIF₇₆₀), and top of canopy (SIF_TOC) (640-850 nm). The SCOPE model, configured with the specification of the Sentinel-2 sensor, simulates SIF within the spectrum range of 640-850 nm. A non-linear logarithmic relationship (R²>0.9, p < 0.05) was observed between simulated SIF and simulated CCC. Simulated CCC was linearly related to observed CCC with R² 0.88, 0.92 and 0.89 and RMSE = 0.04, 0.17 and 0.09 gm/m² at p < 0.05 for summer, post-monsoon and early winter respectively. Whereas, the simulated CCC did not capture the full range of CCC variability for the post-monsoon season. Simulated SIF (SIF₇₆₀) was well correlated with SIF from Orbiting Carbon Observatory-2 (OCO-2) satellite with R² 0.68, 0.73 and 0.73 (RMSE = <1 W/m²/sr/μm, p < 0.05) for the month of summer (April), pre-monsoon (May) and early winter season (November) respectively. Temporal SIF_TOC effectively captured the seasonal variability associated with the phenology of deciduous tree species. Among various Sentinel-2 MSI derived VIs, Red Edge NDVI (RENDVI) exhibited maximum sensitivity with SIF (highest monthly average R²> 0.6, p < 0.05). The spatial SIF would serve as an useful link between airborne /satellite derived SIF and in-situ fluorescence measurements to understand multiscale SIF variability of terrestrial vegetation.     

应用MSS数字图像处理进行森林资源动态监测的探讨

本文探讨了应用多时相Landsat Mss图像进行森林动态监测的方法。研究了差值图像法、比值植被指数差值法、归一化植被指数差值法、多时相主分量分析法(垂直植被指数法)和分类比较法对提取森林动态变化信息的作用。结果表明,3种植被指数法都能较好地提取植被动态信息,可用于森林面积消长的动态监测。     

多源数据支持下的森林地上生物量估算方法

以Landsat8 OLI（operational land imager）为遥感数据源,森林资源二类调查和地理国情数据为主要辅助数据,对森林地上生物量（aboveground biomass,AGB）进行了反演和估算。以安徽省金寨县的天然林为研究对象,通过计算覆盖研究区Landsat8 OLI的光谱、纹理和地形特征,利用森林资源二类调查、地理国情普查与监测和外业调查数据建立AGB定量反演模型,以此为基础分析了不同特征对于AGB估算的影响。结果表明,基于所采用的方法得到的金寨县的森林地上生物量,最优反演模型的实测值与估算值相对误差为0.708 718,均方根误差为1.318 983,精度较高。依据该模型计算得到金寨县的生物总量为4 723 728 530 t,结果与实际情况符合。该研究对AGB定量反演和研究所采用的方法对于大范围监测森林资源具有可用性。     

利用升降轨道SAR数据获取DEM的试验研究

首先介绍了利用InSAR技术提取DEM的原理及方法,其次对利用ENVISAT卫星的升轨SAR数据和降轨SAR数据获取DEM,然后对其融合,并将融合前后的DEM与SRTM3 DEM进行比较,分析其精度。结果表明,与单独利用升轨SAR数据或降轨SAR数据获取的DEM相比,融合后的DEM能更好地显示地形起伏特征,高程精度得到明显提升,且羽化融合后的DEM精度最高,其与参考DEM─SRTM3 DEM高程差异标准差为±7.25,高程差异绝对值小于15 m的地区占95.48%。     

使用ERS-1/2干涉测量SAR数据生成DEM

干涉合成孔径雷达（ＩＮＳＡＲ）数据已被证明能生产精确的数据高程模型（ＤＥＭ），我们已开发了从单视ＳＡＲ复影像数据自动生成数字高程模型的新软件，基于ＳＡＲ多视强度影像的最小二乘曩像匹配被用于复影像对的配准，达到很高的配准精度（０．０１～０．０５像元精度）。一种新的计算目标点３维坐标（Ｘ，Ｙ，Ｚ）的方程还被提出，卫星轨道，姿态和基线参数以及相位常数被纳入在方程中并被表示了时间的线性函数，利用至少６个地面控制点能够同时估算这些参数，本文还给出了意大利埃特地区ＥＲＳ－１／２ＳＡＲ数据处理结果。     

Nonstationary Spatial Texture Estimation Applied to Adaptive Speckle Reduction of SAR Data

This letter proposes a new model for the second-order statistics of spatial texture in synthetic aperture radar images. The autocovariance function is locally approximated by a two-dimensional anisotropic Gaussian kernel (AGK) to characterize texture by its local orientation and anisotropy. The estimation of texture parameters at a given scale is based on the gradient structure tensor operator and does not require the explicit computation of the autocovariance. Finally, a new filter called AGK minimum mean square error (MMSE) that takes into account this spatial information is introduced and compared with the refined MMSE filter. The proposed filter has better performance in terms of texture preservation and structure enhancement     

利用SAR数据研究南极Grove山地区冰流运动特征

Grove山地区是我国南极PANDA断面考察的重点研究区域之一。本文利用35 d时间间隔的Envisat ASAR数据,分别采用DInSAR、偏移量跟踪以及两者相结合的方法提取了该区域冰面流速信息,描述了该区域复杂的冰流运动特征,并对Grove山核心区的冰流运动特征进行分析。利用GPS实测数据和MEaSUREs流速进行精度分析,说明了结果的可靠性,并利用2006、2007和2009年三期南极冬季SAR数据提取了Grove山地区冰流速,发现该区域冰流速无明显年际变化。     

Per-segment Aboveground Forest Biomass Estimation Using LIDAR-Derived Height Percentile Statistics

An outbreak of red oak borer, an insect infesting red oak trees, prompted the need for a biomass model of closed-canopy oak-hickory forests in the rugged terrain of the Arkansas Ozarks. Multiple height percentiles were calculated from small-footprint aerial LIDAR data, and image segmentation was employed to partition the LIDAR-derived surface into structurally homogeneous modeling units. In situ reference data were incorporated into a machine-learning algorithm that produced a regression-tree model for predicting aboveground woody biomass per segment. Model results on training data appear adequate for prediction purposes (mean error 2.38 kg/m², R ² = 0.83). Model performance on withheld test data reveals slightly lower accuracy (2.77 kg/m², R ² = 0.72).     

利用资源三号卫星数据提取经济林研究

选择柬埔寨吴哥窟北部库仑山国家森林公园为研究区,通过对资源三号卫星数据进行大气校正、多光谱与全色数据融合等处理,结合经济林固有的光谱、纹理等特征,利用面向对象的分类方法,构建了适于资源三号卫星数据的快速提取经济林信息的土地利用分类规则。通过野外实地调查和验证表明,资源三号卫星数据可以满足土地利用中经济林信息提取的需求,选择合适的阈值即可完整提取经济林及其空间分布,正确率可达85.97%;资源三号卫星数据在土地利用精细分类和信息提取中具有较大的应用潜力。     

Estimation of Snow Cover Area,Snow Physical Properties and Glacier Classification in Parts of Western Himalayas Using C-Band SAR Data

Snow physical properties, snow cover and glacier facies are important parameters which are used to quantify snowpack characteristics, glacier mass balance and seasonal snow and glacier melt. This study has been done using C-band synthetic aperture radar (SAR) data of Indian radar imaging satellite, radar imaging satellite-1 (RISAT)-1, to estimate the seasonal snow cover and retrieve snow physical properties (snow wetness and snow density), and glacier radar zones or facies classification in parts of North West Himalaya (NWH), India. Additional SAR data used are of Radarsat-2 (RS-2) satellite, which was used for glacier facies classification of Smudra Tapu glacier in Himachal Pradesh. RISAT-1 based snow cover area (SCA) mapping, snow wetness and snow density retrieval and glacier facies classification have been done for the first time in NWH region. SAR-based inversion models were used for finding out wet and dry snow dielectric constant, dry and wet SCA, snow wetness and snow density. RISAT-1 medium resolution scan-SAR mode (MRS) in HV polarization was used for first time in NWH for deriving time series of SCA maps in Beas and Bhagirathi river basins for years 2013–2014. The SAR-based inversion models were implemented separately for RISAT-1 quad pol. FRS2, for wet snow and dry snow permittivity retrieval. Masks for layover and shadow were considered in estimating final snow parameters. The overall accuracy in terms of R² value comes out to be 0.74 for snow wetness and 0.72 for snow density based on the limited ground truth data for subset area of Manali sub-basin of Beas River up to Manali for winter of 2014. Accuracy for SCA was estimated to be 95 % when compared with optical remote sensing based SCA maps with error of ±10 %. The time series data of RISAT-1 MRS and hybrid data in RH/RV mode based decompositions were also used for glacier radar zones classification for Gangotri and Samudra Tapu glaciers. The various glaciers radar zones or facies such as debris covered glacier ice, clean or bare glacier ice radar zone, percolation/refreeze radar zone and wet snow, ice wall etc., were identified. The accuracy of classified maps was estimated using ground truth data collected during 2013 and 2014 glacier field work to Samudra Tapu and Gangotri glaciers and overall accuracy was found to be in range of 82–90 %. This information of various glacier radar zones can be utilized in marking firn line of glaciers, which can be helpful for glacier mass balance studies.     

森林地上生物量遥感估测研究进展

森林生物量是衡量生态系统生产力的重要指标,也是研究森林生态系统物质循环的重要基础,其估测方法可以分为传统地面实测法、遥感监测法和综合模型法.随着生物量估测从样地研究发展到区域应用,空间尺度的增大导致宏观资料和参数的获取存在很多困难.在深入分析目前应用遥感技术估算森林生物量的方法及原理基础上,系统评述了统计模型、物理模型...     

基于ERS散射计数据的土壤水分反演方法

在全球能量与水循环研究中,地表土壤水分是非常关键的参数之一。ERS散射计因具有观测尺度大、重复周期短等优势而在地表土壤水分监测方面日益受到关注。采用目前最先进的理论模型AIEM(改进的积分方程模型),根据ERS散射计的参数设计模拟出一个涵盖较宽地表粗糙度和介电常数输入范围的数据库,利用这个数据库发展出一个参数化模型。该模型采用了一个综合均方根高度(RMS height)与相关长度(Correlation length)的粗糙度参数,该参数在每个入射角度下都可以用同一个函数来描述,解决了多角度数据情况下粗糙度参数的表达问题。应用新发展的参数化模型进行土壤水分的反演结果表明,该模型具有较高的精度。     

多源光学遥感数据估算桉树森林生物量

为了克服单个传感器影像在估算森林生物量的方面的局限性,采用多传感器遥感影像估算森林生物量成为目前的发展趋势。该研究根据光学遥感数据源比较多的特点,采用Landsat5 TM数据、ALOS AVNIR-2数据和CBERS-02B CCD数据估算东莞市桉树森林生物量,在对比分析单个传感器估算生物量能力的基础上,将3种传感器结合在一起估算东莞市桉树生物量,充分发挥不同光学传感器在光谱分辨率、辐射分辨率、空间分辨率和时间分辨率等方面的优点,避开各自的缺点,提高了遥感估算桉树生物量的精度,其调整系数R2达到0.65。该研究可为进一步研究大范围的森林生物量估算提供参考。     

遥感蒸散发模型研究进展

蒸散发是水圈、大气圈和生物圈水分和能量交换的主要过程,也是水循环中最重要的分量之一.遥感技术的应用使得区域尺度的蒸散发估算成为可能,由此涌现出许多旨在精确反演不同时空尺度蒸散发及地表通量的模型,以更好地服务于相关领域的研究和应用.根据模型建构思想和方法的不同,从基于能量平衡的单层和多层模型、基于能量平衡的彭曼类模型以及遥感经验模型3个方面入手,系统回顾国内外遥感蒸散发模型的算法,详细评述了目前应用较为广泛的SEBAL, SEBS, TSEB模型的原理和优劣,分析了蒸散发反演存在的时间拓展及不确定性、时空分辨率及尺度效应、模型选择及适用性判别、平流影响和精度检验5个问题.指出今后应加强陆面过程和地-气系统作用机制、模型输入参数精度、精度检验和尺度效应及误差传递规律等方面的研究.     

Comparison of ETM+ and MODIS Data for Tropical Forest Degradation Monitoring in the Peninsular Malaysia

This study was undertaken the use of course and moderate spatial resolution remote sensing data to assess the forest degradation in the Peninsular Malaysia. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used as coarse spatial resolution data, while Landsat Enhanced Thematic Mapper⁺ (ETM⁺) imagery was used as moderate spatial resolution to compare the accuracy. Geometric and radiometric correction and re-sampling were performed in pre-processing to enhance the analysis and results. Canopy fractional cover was used as an approach to assess the forest degradation in this study. Then, an optimum vegetation index was selected to apply on canopy fractional cover to enhance the detection of forest canopy damage. At the same time, accuracy assessment for the approach was referred to the location of Neobalanocarpus Heimii and correlate with global evapotranspiration rate. The forest degradation analysis was also applied and compared for all of the states in the Peninsular Malaysia. In conclusion, Landsat ETM⁺ imagery obtained higher accuracy compare to MODIS using canopy fractional cover approach for forest degradation assessment, and can be more broadly applicable to use for forest degradation investigation.