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.     

Assessment of various parameters on 3D semantic object-based point cloud labelling on urban LiDAR dataset

Semantic labelling of LiDAR point cloud is critical for effective utilization of 3D points in numerous applications. 3D segmentation, incorporation of ancillary data, feature extraction and classification are the key stages in object-based point cloud labelling. The choice of algorithms and tuning parameters adopted in these stages has substantial impact on the quality of results from object-based point cloud labelling. This paper critically evaluates the performance of object-based point cloud labelling as a function of different 3D segmentation approaches, incorporation of spectral data and computational complexity of the point cloud. The designed experiments are implemented on the datasets provided by the ISPRS and the results are independently validated by the ISPRS. Results indicate that aggregation of dense point cloud into higher-level object analogue (e.g. supervoxels) before 3D segmentation stage offers superior labelling results and best computational performance compared to the popular surface growing-based approaches.     

Climate change adaptation cost in the US: what do we know?

Researchers and policy makers increasingly recognize the need to adapt to future changes in climate, given that past emissions of greenhouse gases have already committed the world to some level of climate change. However, the current understanding of the costs and benefits of adaptation measures is still fairly rudimentary, and far from comprehensive. An assessment is presented of the current state of knowledge on the magnitude of adaptation costs in the United States. While incomplete, the studies suggest that adaptation cost could be as high as tens or hundreds of billions of dollars per year by the middle of this century. Key studies are identified in each sector, and the cost estimates and approaches to cost estimation are surveyed. Methodological issues are highlighted in interpreting, comparing, and aggregating adaptation cost estimates. Policy recommendations are made along with appropriate steps to make future adaptation cost studies more comparable within and across sectors and more accessible and relevant to policy and decision makers.

Policy relevance

Designing and implementing climate change adaptation policy requires good information about the effectiveness and cost of available adaptive options. The current state of knowledge on adaptation costs in the United States is assessed and significant gaps in the literature are highlighted – particularly in terms of sectoral and geographic coverage – as well as inconsistencies in methodologies and assumptions that hamper comparison across studies. Critical steps are identified that can be taken to make adaptation cost studies more accessible and useful to decision makers. The findings and recommendations are relevant to adaptation cost studies globally, not just in the United States.     

Skill assessment of global climate model wind speed from CMIP5 and CMIP6 and evaluation of projections for the Bay of Bengal

Climate Dynamics - Atmospheric and oceanic parameters derived from global climate model (GCM) simulations have received wide global attention and importance in representing the future world under...     

River and basin morphometric indexes to detect tectonic activity: a case study of selected river basins in the South Indian Granulite Terrain (SIGT)

Abstract

The effects and influence of tectonic processes on the Anjarakandy, Thalassery, Mahe, and Kuttiyadi watersheds and rivers of the South Indian Granulite Terrain in Kerala were examined to determine their spatial heterogeneity. Drainage basin asymmetry (Af), transverse topographic symmetry factor (T), hypsometric integral and curve (HI), longitudinal profile, stream length gradient index (SL), and stream concavity index (SCI) suggest heterogeneity in tectonic influence. Clusters of geomorphic anomalies in similar lithology rule out lithologic control on drainage development. However, the orientations of the drainage networks and predominant fractures/lineaments compare closely and reveal strong tectonic influence. Though the watersheds are considered to be in an advanced stage of erosion by the low HI (<30) and high values of SCI, variations in the shape of the hypsometric curves and differences in the SCI values indicate the different influence of tectonic process from watersheds in the north to the south. Among the watersheds, the Mahe and Kuttiyadi are more sensitive to tectonic processes than the Anjarakandy and Thalassery and indicate spatial heterogeneity in the influence of tectonic activity, confirming the grouping of watersheds based on structural and drainage patterns.     

Field Treatment of MTBE‐Contaminated Groundwater Using Ozone/UV Oxidation

Methyl‐tertiary butyl ether (MTBE) is often found in groundwater as a result of gasoline spills and leaking underground storage tanks. An extrapolation of occurrence data in 2008 estimated at least one detection of MTBE in approximately 165 small and large public water systems serving 896,000 people nationally (United States Environmental Protection Agency [U.S. EPA] 2008). The objective of this collaborative field study was to evaluate a small groundwater treatment system to determine the effectiveness of ultraviolet (UV)/ozone treatment in removing MTBE from contaminated drinking water wells. A pilot‐scale advanced oxidation process (AOP) system was tested to evaluate the oxidation efficiency of MTBE and intermediates under field conditions. This system used ozone as an oxidizer in the presence of UV light at hydraulic retention times varying from 1 to 3 min. MTBE removal efficiencies approaching 97% were possible with this system, even with low retention times. The intermediate t‐butyl alcohol (TBA) was removed to a lesser extent (71%) under the same test conditions. The main intermediate formed in the oxidation process of the contaminated groundwater in these studies was acetone. The concentrations of the other anticipated intermediates t‐butyl formate (TBF), isopropyl alcohol (IPA), methyl acetate (MAc), and possible co‐occurring aromatics (BTEX) in the effluent were negligible.     

Recognizing Correlated Patterns from Remotely Sensed Images: A New Learning Rule Based on Spin Glass Theory

Classification of remotely sensed images is a rich research field wherein techniques from conventional statistics to recent developments such as Artificial Neural Network, Fuzzy logic etc. has wide applications. Conventionally remotely sensed image classification referred to pixel classification based on broad categories such as vegetation and water bodies. With the availability of high-resolution imageries, shape analysis of macro structures contained in images becomes an important and difficult task. Although conventional statistical pattern recognition techniques give a reasonable result, Artificial neural network methods seem to be giving better results. In this paper, we give a survey of feed-forward neural network used for shape classification and a Hopfield model with an improved learning rule, for a typical shape analysis problem.     

Pre-processing considerations of IRS-P6 LISS-4 imagery

Wide image swath with a high geometric resolution is required for photogrammetric applications. Both demands can be satisfied using staggered line arrays. Different bands of IRS-P6 LISS-4 sensor use staggered arrays for imaging. This paper describes a method for computing the offset for geometric alignment of odd and even lines of the staggered array of IRS-P6 LISS-4 imagery. The odd and even pixel rows are separated by 35 μm (equal to 5 pixels) in the focal plane in the along-track direction. Slightly different viewing angles of both lines of a staggered array can result in a variable sampling pattern on the ground because of the attitude fluctuations, satellite movement, terrain topography, PSM steering and small variations in the angular placement of the CCD lines (from the pre-launch values) in the focal plane. Non-accounting of this variable sampling value during the video data alignment will introduce deterioration of image quality and geometric discontinuity of features. The stagger parameters can be computed by the reconstruction of the viewing geometry with a calibrated camera geometry model and a public domain DEM. The impact of the line separation in the focal plane during imaging for different viewing configurations and terrain heights are studied and reported in this paper. Computed values from the model are in good agreement with what is observed in the raw image for different view angles. The results verify the model and are representative of the stability of the platform.     

Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection

The urban land cover mapping and automated extraction of building boundaries is a crucial step in generating three-dimensional city models. This study proposes an object-based point cloud labelling technique to semantically label light detection and ranging (LiDAR) data captured over an urban scene. Spectral data from multispectral images are also used to complement the geometrical information from LiDAR data. Initial object primitives are created using a modified colour-based region growing technique. Multiple classifier system is then applied on the features extracted from the segments for classification and also for reducing the subjectivity involved in the selection of classifier and improving the precision of the results. The proposed methodology produces two outputs: (i) urban land cover classes and (ii) buildings masks which are further reconstructed and vectorized into three-dimensional buildings footprints. Experiments carried out on three airborne LiDAR datasets show that the proposed technique successfully discriminates urban land covers and detect urban buildings.