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.     

融合地面激光扫描和摄影测量技术的三维重建

提出了地面激光扫描和摄影测量两种技术相融合的方法,对地物进行三维重建,获取了包含地物纹理信息的影像,并通过进一步的处理生成密集点云,最终实现了对地物的三维重建和可视化.     

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.     

Processing of Laser Scanner Data and Extraction of Structure Lines Using Methods of the Image Processing

激光扫描数据提供了一种新的手段用于获取高精度的数字地形表面模型. 原始的航空激光扫描数据表达的是一些非规则分布的"点云", 这些非规则分布的点需要进行有效的事后处理. 这种事后处理有2个目的:一是将那些分布在地表面上的点(即地面点)与分布在非地表面上的点(譬如树木、房屋或汽车上的点, 即非地面点)进行有效的分离;二是从分离后的地面点中提取结构线, 用于建立高精度的数字地面模型. 作者发展了一系列的基于数字形态学理论和稳健参数估计理论的方法用于分离和探测地面点. 这里所介绍和开发的提取结构线的算法建立在数字图像处理和表面曲率理论的基础上. 这些算法同样可以扩展地用于其他领域. 所介绍的基于数字图像处理理论处理原始的航空激光扫瞄数据和提取结构线的方法取得了很好的结果. 这一结论可以在本文中通过一系列的插图得到有力的证明.     

Tree Detection in Urban Regions Using Aerial Lidar and Image Data

In this letter, we present an approach to detecting trees in registered aerial image and range data obtained via lidar. The motivation for this problem comes from automated 3-D city modeling, in which such data are used to generate the models. Representing the trees in these models is problematic because the data are usually too sparsely sampled in tree regions to create an accurate 3-D model of the trees. Furthermore, including the tree data points interferes with the polygonization step of the building roof top models. Therefore, it is advantageous to detect and remove points that represent trees in both lidar and aerial imagery. In this letter, we propose a two-step method for tree detection consisting of segmentation followed by classification. The segmentation is done using a simple region-growing algorithm using weighted features from aerial image and lidar, such as height, texture map, height variation, and normal vector estimates. The weights for the features are determined using a learning method on random walks. The classification is done using the weighted support vector machines, allowing us to control the misclassification rate. The overall problem is formulated as a binary detection problem, and the results presented as receiver operating characteristic curves are shown to validate our approach     

利用GRACE、GPS和绝对重力数据监测斯堪的纳维亚陆地水储量变化

重力卫星GRACE(gravity recovery and climate experiment)监测斯堪的纳维亚半岛陆地水储量变化会受到冰川均衡调整(GIA)信号的严重影响。首先根据该地区绝对重力和GPS并址观测数据计算了GIA重力和垂直位移的实测线性比值,利用该比值和GPS网观测的垂直位移速度场得到了GIA重力。然后,对GRACE观测的重力变化速率进行GIA重力改正,进而可分离陆地水储量变化趋势,避免了使用GIA模型所带来的巨大不确定性,并根据观测数据完整估计了所得结果的不确定性。最后与水文模型作对比分析。结果表明,实测的GIA重力-垂直位移线性比值为0.148±0.020μGal/mm(1Gal=10-2 m/s2),该结果检验了Wahr的理论近似值且与北美实测的结果非常接近。2003年1月至2011年3月期间,斯堪的纳维亚半岛陆地水储量存在明显的增加趋势,信号的主体位于半岛南端的维纳恩湖附近,总的水量增加速率为4.6±2.1km3/a,数据观测期间的累积增加水量为38±17km3。研究结果与WGHM水文模型的结果有较好的一致性,相关系数达到0.69,而与GLDAS水文模型的相关性略小。     

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.     

Estimation of Crown Closure and Tree Density Using Landsat TM Satellite Images in Mixed Forest Stands

The objective of this study was to investigate the relationship between crown closure and tree density in mixed forest stands using Landsat Thematic Mapper (TM) reflectance values (TM 1- TM 5 and TM 7) and six vegetation indices (SR, DVI, SAVI, NDVI, TVI and NLI). In this study, multiple regression analysis was used to estimate the relationships between the crown closure and tree density (number of tree stems per hectare) using reflectance values and vegetation indices (VIs). The results demonstrated that the model that used SR and DVI had the best performances in terms of crown closure (R²?=?0.674) and the model that used the DVI and SAVI had the best performances in terms of tree density (R²?=?0.702). The regression model that used TM 1, TM 3 together with TM 4 showed the performances of the crown closure (R²?=?0.610) and the regression model that used TM 1 showed the performances of the tree density (0.613). Results obtained from this research show that vegetation indices (VIs) were a better predictor of crown closure and tree density than other TM bands.     

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.     

Visualising Crime Clusters in a Space‐time Cube: An Exploratory Data‐analysis Approach Using Space‐time Kernel Density Estimation and Scan Statistics

For an effective interpretation of spatio‐temporal patterns of crime clusters/hotspots, we explore the possibility of three‐dimensional mapping of crime events in a space‐time cube with the aid of space‐time variants of kernel density estimation and scan statistics. Using the crime occurrence dataset of snatch‐and‐run offences in Kyoto City from 2003 to 2004, we confirm that the proposed methodology enables simultaneous visualisation of the geographical extent and duration of crime clusters, by which stable and transient space‐time crime clusters can be intuitively differentiated. Also, the combined use of the two statistical techniques revealed temporal inter‐cluster associations showing that transient clusters alternatively appeared in a pair of hotspot regions, suggesting a new type of “displacement” phenomenon of crime. Highlighting the complementary aspects of the two space‐time statistical approaches, we conclude that combining these approaches in a space‐time cube display is particularly valuable for a spatio‐temporal exploratory data analysis of clusters to extract new knowledge of crime epidemiology from a data set of space‐time crime events.     

Spatially Explicit Estimation of Leaf Area Index Using EO-1 Hyperion and Landsat ETM+ Data: Implications of Spectral Bandwidth and Shortwave Infrared Data on Prediction Accuracy in a Tropical Montane Environment

This study evaluated the utility of narrowband (EO-1 Hyperion) and broadband (Landsat ETM+) remote sensing data for the estimation of leaf area index (LAI) in a tropical environment in Sulawesi, Indonesia. LAI was inferred from canopy gap fraction measurements taken in natural tropical forest and cocoa plantations. Single and multiple spectral bands and spectral indices were used as predictor variables in reduced major axis (RMA) and ordinary least squares (OLS) regression models. The predictive power of most regression models was notably higher when employing narrowband data instead of broadband data. Highly significant relationships between LAI and spectral reflectance were observed near the red-edge region and in most shortwave infrared (SWIR) bands. In contrast to most near-infrared (NIR) narrow bands, the correlation between SWIR reflectance and LAI was not confounded when including both vegetation types and did not suffer from saturation. The results demonstrate that leaf area index of a challenging tropical environment can be estimated with satisfactory accuracy from hyperspectral remote sensing data.