Estimation of Above Ground Biomass Using Texture Metrics Derived from IRS Cartosat-1 Panchromatic Data in Evergreen Forests of Western Ghats,India

Assessment of above ground forest biomass (AGB) is essential in carbon modelling studies to provide mitigation strategies as demonstrated by reducing emissions from deforestation and forest degradation. Several researchers have demonstrated the use of remote sensing data in spatial AGB estimation, in terms of spectral and radar backscatter based approaches at a landscape scale with several known limitations. However, these methods lacked the predictive ability at high biomass ranges due to saturation. The current study addresses the problem of saturation at high biomass ranges using canopy textural metric from high resolution optical data. Fourier transform based textural ordination (FOTO) technique, which involves deriving radial spectrum information via 2D fast Fourier transform and ordination through principal component analysis was used for characterizing the textural properties of forest canopies. In the current study, plot level estimated AGB from 15 (1 ha) plots was used to relate with texture derived information from very high resolution datasets (viz., IKONOS and Cartosat-1). In addition to the estimation of high biomass ranges, one of the prime objective of the current study is to understand the effects of spatial resolution on deriving textural-AGB relationship from 2.5 m IRS Cartosat data (Cartosat-A, viewing angle = ?5°) to that of IKONOS imagery with near nadir view. Further, since texture is impacted by several illumination geometry issues, the effect of viewing geometry on the relationship was evaluated using Cartosat-F (Viewing angle = 26°) imagery. The results show that the FOTO method using stereo Cartosat (A and F) images at 2.5 m resolution are able to perform well in characterizing high AGB values since the texture-biomass relationship is only subjected to 18 % relative error to that of 15 % in case of IKONOS and could aid in reduction of uncertainty in AGB estimation at a large landscape levels.     

Automatic Tree Identification and Diameter Estimation Using Single Scan Terrestrial Laser Scanner Data in Central Indian Forests

Forest inventory parameters, primarily tree diameter and height, are required for several management and planning activities. Currently, Terrestrial Laser Scanning (TLS) is a promising technology in automated measurements of tree parameters using dense 3D point clouds. In comparison with conventional manual field inventory methods, TLS systems would supplement field data with detailed and relatively higher degree of accurate measurements and increased measurement frequency. Although, multiple scans from TLS captures more area, they are resource and time consuming to ensure proper co-registration between the scans. On the other hand, Single scans provide a fast and recording of the data but are often affected by occlusions between the trees. The current study evaluates potential of single scan TLS data to (1) develop an automatic method for tree stem identification and diameter estimation (diameter at breast height—DBH) using random sample consensus (RANSAC) based circle fitting algorithm, (2) validate using field based measurements to derive accuracy estimates and (3) assess the influence of distance to scanner on detection and measurement accuracies. Tree detection and diameter measurements were validated for 5 circular plots of 20 m radius using single scans in dry deciduous forests of Betul, Madhya Pradesh. An overall tree detection accuracy of 85 and 70% was observed in the scanner range of 15 and 20 m respectively. The tree detection accuracies decreased with increased distance to the scanner due to the decrease in visible area. Also, estimated stem diameter using TLS was found to be in agreement with the field measured diameter (R² = 0.97). The RMSE of estimated DBH was found to be 3.5 cm (relative RMSE ~20%) over 202 trees detected over 5 plots. Results suggest that single scan approach suffices the cause of accuracy, reducing uncertainty and adds to increased sampling frequency in forest inventory and also implies that TLS has a seemingly high potential in forest management.     

ALOS PALSAR and Hyperion Data Fusion for Land Use Land Cover Feature Extraction

Detailed and enhanced land use land cover (LULC) feature extraction is possible by merging the information extracted from two different sensors of different capability. In this study different pixel level image fusion algorithms (PCA, Brovey, Multiplicative, Wavelet and combination of PCA & IHS) are used for integrating the derived information like texture, roughness, polarization from microwave data and high spectral information from hyperspectral data. Span image which is total intensity image generated from Advanced Land observing Satellite-Phase array L-band SAR (ALOS-PALSAR) quad polarization data and EO-1 Hyperion data (242 spectral bands) were used for fusion. Overall PCA fused images had shown better result than other fusion techniques used in this study. However, Brovey fusion method was found good for differentiating urban features. Classification using support vector machines was conducted for classifying Hyperion, ALOS PALSAR and fused images. It was observed that overall classification accuracy and kappa coefficient with PCA fused images was relatively better than other fusion techniques as it was able to discriminate various LULC features more clearly.     

Detection of Slope Instability in Hong Kong Based on Multi-baseline Differential SAR Interferometry Using ALOS PALSAR Data

The monitoring of slope instability requires detailed observations of mass movements, which generally cannot be obtained by geodetic methods or global positioning systems (GPS). Differential synthetic aperture radar (SAR) interferometry has proven to be an effective way of measuring land deformation with millimeter accuracy over wide areas. Using data from the newly launched L-band ALOS PALSAR interferometer and the multi-baseline differential SAR interferometry technique, slope instability in Hong Kong was analyzed by means of measured surface displacement along look vectors. Owing to its enhanced vegetation penetration, less temporal decorrelation enabled the L-band data to improve spaceborne radar sensor land-surface deformation measurements. The results were validated by ENVISAT ASAR-derived outcomes and other ground survey data.     

Tropical Dry Deciduous Forest Stand Variable Estimation Using SAR Data

Optical remote sensing data have been extensively used to derive biophysical properties that relate forest type and composition. However, stand density, stand height and stand volume cannot be estimated directly from optical remote sensing data owing to poor sensitivity between these parameters and spectral reflectance. The ability of microwave energy to penetrate within forest vegetation makes it possible to extract information on both the crown and trunk components from radar data. The type of polarization employed determines the radar response to the various shapes and orientations of the scattering mechanisms within the canopy or trunk. This study mainly presents experimental results obtained with airborne E-SAR using polarimetric C and L bands over the tropical dry deciduous forest of Chandrapur Forest Division, Maharashtra. A detailed documentation of the relationship between SAR C & L bands backscattering and forest stand variables has been provided in the present study through linear correlation. Linear correlation of the single channel SAR derived estimates with the field measured means show a good correlation between L HV backscattering coefficient with stand volume (r² = 0.71) and L HH backscattering coefficient with stand density (r² = 0.75). The results imply that SAR data has significant potential for stand menstruation in operational forestry.     

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.     

Mass Balance Estimation of Dokriani Glacier in Central Indian Himalaya Using Remote Sensing Data

Dokriani Glacier is regarded as one of the important glaciers of Bhagirathi River basin, which fed river Ganges. The length of the glacier is about 4.6 km, and snout elevation is about 4028 m m.s.l. The mass balance of this glacier was calculated using field-based measurements for few years during 1994 to 2000. However, due to remote and poor accessibility, the field-based measurements could not continue; thus, remote sensing-based methods become useful tool to estimate the long-term mass balance of the glacier. In this study, glacier mass balance has been determined using accumulation area ratio (AAR) method. Remote sensing data sets, e.g. Landsat TM, ETM?+?and OLI, have been used to estimate AAR for different years from 1994 to 2014. An attempt has also been made to develop a mathematical relationship between remote sensing-derived AAR and field-observed mass balance data of the glacier. Further, this relationship has been used to estimate mass balance of the glacier for different years using remote sensing-derived AAR. Estimated mass balance was validated from ground-observed mass balance for few years. The field-observed and remote sensing-derived mass balance data are compared and showed high correlation. It has been observed that AAR for the Dokriani Glacier varies from 0.64 to 0.71. Mass balance of the glacier was observed between ??15.54 cm and ??50.95 cm during the study period. The study highlights the application of remote sensing in mass balance study of the glaciers and impact of climate change in glaciers of Central Indian Himalaya.     

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。     

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%。     

Fusion of HJ1B and ALOS PALSAR data for land cover classification using machine learning methods

Image classification from remote sensing is becoming increasingly urgent for monitoring environmental changes. Exploring effective algorithms to increase classification accuracy is critical. This paper explores the use of multispectral HJ1B and ALOS (Advanced Land Observing Satellite) PALSAR L-band (Phased Array type L-band Synthetic Aperture Radar) for land cover classification using learning-based algorithms. Pixel-based and object-based image analysis approaches for classifying HJ1B data and the HJ1B and ALOS/PALSAR fused-images were compared using two machine learning algorithms, support vector machine (SVM) and random forest (RF), to test which algorithm can achieve the best classification accuracy in arid and semiarid regions. The overall accuracies of the pixel-based (Fused data: 79.0%; HJ1B data: 81.46%) and object-based classifications (Fused data: 80.0%; HJ1B data: 76.9%) were relatively close when using the SVM classifier. The pixel-based classification achieved a high overall accuracy (85.5%) using the RF algorithm for classifying the fused data, whereas the RF classifier using the object-based image analysis produced a lower overall accuracy (70.2%). The study demonstrates that the pixel-based classification utilized fewer variables and performed relatively better than the object-based classification using HJ1B imagery and the fused data. Generally, the integration of the HJ1B and ALOS/PALSAR imagery can improve the overall accuracy of 5.7% using the pixel-based image analysis and RF classifier.     

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 %.     

利用PALSAR数据反演2010年玉树地震断层的同震滑动分布

为深入了解2010年玉树地震引起的地表位移及发震断层的细节情况,对玉树地震震前、震后的PALSAR数据进行干涉处理,并针对轨道不精确引起的相位残差问题,采用最小二乘多项式模型拟合的方法对其进行去除,得到玉树地震地表的同震形变场.结果显示,形变最大地区发生在33.06°N、96.83°E附近,雷达视线方向形变最大值为-0.442 m.通过地质调查结果及合成孔径雷达干涉测量(InSAR)形变场的分析,对断层进行分段,基于弹性半空间位错模型对分段后的断层进行了同震滑动分布反演,并对反演结果的可靠性进行分析.结果表明,断层的最大位移为2.084 m,位于隆宝滩地表以下14 km处,反演结果对应的矩震级为Mw7.0,与地震学和地质调查的情况较吻合,且反演的结果较可靠.     

利用PALSAR数据获取拉奎拉地震同震形变场

2009年4月6日发生的意大利拉奎拉Mw6.3地震造成了地表的严重破坏,为了解地震引起的地表变化情况,本文利用ALOS/PALSAR影像数据,采用二轨差分干涉测量技术提取了该次地震的地表同震形变场。通过分析可知:地表在西北-东南方向上发生错动,形变主要发生在40×30 km的范围内,西南部出现了沿视线方向的下降,东北部出现了沿视线方向的抬升,最大值分别为0.337 m和0.122 m。结果表明,获得的同震形变场与地震地质调查的结果一致。     

Applications of ALOS PALSAR for monitoring biophysical parameters of a degraded black mangrove (Avicennia germinans) forest

Within the last few decades mangrove forests worldwide have been experiencing high annual rates of loss and many of those that remain have undergone considerable degradation. To understand the condition of these forests, various optical remote sensing platforms have been used to map and monitor these wetlands, including the use of these data for biophysical parameter mapping. For many mangrove forests a reliable source of optical imagery is not possible given their location in quasi-permanent cloud cover or smoke covered regions. In such cases it is recommended that Synthetic Aperture Radar (SAR) be considered. The purpose of this investigation was to examine the relationships between various ALOS-PALSAR modes, acquired from eight images, and mangrove biophysical parameter data collected from a black mangrove (Avicennia germinans) dominated forest that has experienced considerable degradation. In total, structural data were collected from 61 plots representing the four common stand types found in this degraded forest of the Mexican Pacific: tall healthy mangrove (n = 17), dwarf healthy mangrove (n = 15), poor condition mangrove (n = 13), and predominantly dead mangrove (n = 16).Based on backscatter coefficients, significant negative correlation coefficients were observed between filtered single polarization ALOS PALSAR (6.25 m) HH backscatter and Leaf Area Index (LAI). When the dead stands were excluded (n = 45) the strength of these relationships increased. Moreover, significant negative correlation coefficients were observed with stand height, Basal Area (BA) and to a lesser degree with stem density and mean DBH. With the coarser spatial resolution dual-polarization and quad polarization data (12.5 m) only a few, and weaker, correlation coefficients were calculated between the mangrove parameters and the filtered HH backscatter. However, significant negative values were once again calculated for the HH when the 16 dead mangrove stands were removed from the sample. Conversely, strong positive significant correlation coefficients were calculated between the cross-polarization HV backscatter and LAI when the dead mangrove stands were considered. Although fewer in comparison to the HH correlations, a number of VV backscatter based relationships with mangrove parameters were observed from the quad polarization mode and, to a lesser extent, with the one single VV polarization data.In addition to backscatter coefficients, stepwise multiple regression models of the mangrove biophysical parameter data were developed based on texture parameters derived from the grey level co-occurrence matrix (GLCM) of the ALOS data. A similar pattern to the backscatter relationships was observed for models based on the single polarization unfiltered data, with fairly strong coefficients of determination calculated for LAI and stem height when the dead stands were excluded. In contrast, similar coefficients of determination with biophysical parameters were observed for the dual and quad polarization multiple regression models when the dead stands were both included and excluded from the analyses. An estimated mangrove LAI map of the study area, derived from a multiple regression model of the quad polarization texture parameters, showed comparable spatial patterns of degradation to a map derived from higher spatial resolution optical satellite data.     

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。     

Parameterization of the Land Parameter Retrieval Model for L-Band Observations Using the NAFE'05 Data Set

The Land Parameter Retrieval Model (LPRM) has been successfully applied to retrieve soil moisture from space-borne passive microwave observations at C-, X-, or Ku-band and high incidence angles (50 $^{circ}$–55$^{circ}$ ). However, LPRM had never been applied to lower angles or to L-band observations. This letter describes the parameterization and performance of LPRM using aircraft and ground data from the National Airborne Field Experiment 2005. This experiment was undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. It was found that model convergence could only be achieved with a temporally dynamic roughness. The roughness was parameterized according to incidence angle and soil moisture. These findings were integrated in LPRM, resulting in one uniform parameterization for all sites. The parameterized LPRM correlated well with field observations at 5-cm depth ($r = 0.93$ based on all sites) with a negligible bias and an accuracy of 0.06 $hbox{m}^{3}cdot hbox{m}^{-3}$. These results demonstrate comparable retrieval accuracies as the official SMOS soil-moisture retrieval algorithm (L-MEB), but without the need for the ancillary data that are required by L-MEB. However, care should be taken when using the proposed dynamic roughness model as it is based on a limited data set, and a more thorough evaluation is necessary to test the validity of this new approach to a wider range of conditions.      

Potential CO<Subscript>2</Subscript> Emission Due to Loss of Above Ground Biomass from the Indian Sundarban Mangroves During the Last Four Decades

Sundarban, the largest single patch of mangrove forest of the world is shared by Bangladesh (~ 60 %) and India (~ 40 %). Loss of mangrove biomass and subsequent potential emission of carbon dioxide is reported from different parts of the world. We estimated the loss of above ground mangrove biomass and subsequent potential emission of carbon dioxide in the Indian part of the Sundarban during the last four decades. The loss of mangrove area has been estimated with the help of remotely sensed data and potential emission of carbon dioxide has been evaluated with the help of published above ground biomass data of Indian Sundarban. Total loss of mangrove area was found to be 107 km² between the year 1975 and 2013. Amongst the total loss ~60 % was washed away in the water by erosion, ~ 23 % was converted into barren lands and the rest were anthropogenically transformed into other landforms. The potential carbon dioxide emission due to the degradation of above ground biomass was estimated to be 1567.98 ± 551.69 Gg during this period, which may account to 64.29 million $ in terms of the social cost of carbon. About three-forth of the total mangrove loss was found in the peripheral islands which are much more prone to erosion. Climate induced changes and anthropogenic land use change could be the major driving force behind this loss of ‘blue carbon’.     

基于MODIS数据的小麦生物量估算模型研究

地表生物量对农作物估产、植被长势评估具有很重要的意义。随着遥感技术的发展与应用,遥感为生物量估算提供了一种新的手段。本文以唐山市为例,利用小麦种植区的MODIS遥感影像数据和同期野外调查获得的16组32个生物量数据,对比分析了归一化植被指数(NDVI)、增强型植被指数(EVI)与小麦生物量多个回归方程的相关系数,进而建立了NDVI、EVI与小麦生物量的线性回归模型。结果显示,使用MODIS数据的植被指数能够很好地对研究区地上生物量进行估算,其中使用EVI的三次函数模型拟合精度最高,并且对每组数据进行平均处理会使模型精度进一步提高。     

利用时间序列ALOS-2 PALSAR数据监测输电通道地表形变

有效监测西南地区输电线路的地表形变对我国电网的安全平稳运行具有重要意义.本文以特高压输电通道沿线为研究区域,基于时间序列卫星雷达差分干涉测量技术,利用2016年8月15日至2017年10月9日22景5 m分辨率升轨的ALOS-2 PALSAR雷达卫星数据,采用PS-InSAR处理方法,在植被覆盖茂密的西南地区对某输电通...     

基于ALOS PALSAR数据的山地冰川流速估算方法比较——以喀喇昆仑地区斯克洋坎力冰川为例

冰川表面流速是进行冰川动力学和物质平衡研究的关键参数之一。合成孔径雷达(SAR)影像作为能大范围提取山地冰川表面流速的重要数据源,利用其进行冰川流速估算目前主要有差分In SAR(D-In SAR)法、多孔径InSAR(MAI)法和SAR偏移量追踪(offset tracking)法3种。其中,MAI法是为了克服D-In SAR对雷达方位向(along-track)形变不敏感而发展的一种新的In SAR技术。以喀喇昆仑山中部地区的斯克洋坎力冰川为例,选取了2008年2景间隔46 d的L波段ALOS PALSAR数据,利用上述3种方法分别进行冰川流速提取实验,讨论了3种方法在山地冰川表面流速监测中的适用性和局限性。结果表明,D-In SAR和MAI方法都能够精确提取距离向和方位向的冰川流速信息,但对相干性均要求较高;在低相干区域,SAR偏移量追踪方法也能够获取更为可靠的方位向和距离向二维冰川流速的速度场,但该方法在冰川表面特征不明显的地区受到一定限制。