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Automated Estimation of Forest Inventories using Terrestrial LiDARAuthor: Suraj Reddy Date: 2019-03-06 Report no: IIIT/TH/2019/23 Advisor:K S Rajan AbstractForests play a key role in carbon cycle by sequestering the atmospheric carbon dioxide from various sources. Approximately 33% of the world’s land area is covered by forest ecosystems. It is thus essential to monitor forests at a regular interval for better management. Remote sensing datasets (both satellite and aerial) forms an key sources for providing a synoptic view of forest cover at landscape level to describe land use changes for monitoring purposes. However, the complexities of the forest ecosytems are not well understood with the traditional 2D remote sensing datasets (optical and radar remote sensing images) and need for the 3D structural assessment of forest ecosystems are essential. Terrestrial Laser Scanning (TLS) or Terrestrial LiDAR (Light Detection And Ranging), is an active remote sensing based ground measurement technique capable of capturing 3D information of the scene of interest with milli-meter accuracy. It transmits the laser pulse and analyses the return pulse to position the reflected object in 3D space. The main goal of this thesis is to evaluate the potential of TLS for forest biomass estimation and to develop the relevant methods to extract relevant information from TLS data. Accurate estimations of forest aboveground biomass at regional and national scales heavily depend on the field data and are upscaled using remote sensing datasets (both optical and radar). However,Field data collection of forest inventory parameters (mainly species name, tree count, tree diameter, height etc.,) using conventional methods are both labour-intensive and time consuming. So, firstly, we target the feasibility of TLS to extract forest inventory parameters accurately using the automated algorithm. We provide an automated solution to extract forest inventory parameters at individual tree level from 3D TLS data. The algorithm is tested over deciduous forests of Betul, Madhya Pradesh. Data filtering followed by a RANSAC based circle fitting algorithm was used to extract tree diameter and tree heights. An overall detection rate of 97% along with high correlation of field based easurements and TLS based measurements (Diameter R2 = 0.99 & Height R2 = 0.98) was obtained. The estimated RMSE was found to be 2.2 cm in diameter and 1.65 m in height. Further, to envisage the potential of TLS in diverse forestry applications, it was desirable to identify individual trees in the 3D TLS data to better describe tree architecture. So the focus was made to segment individual trees from the 3D point cloud by employing supervoxels and graph-cut based segmentation methods. High dense point cloud was first converted into supervoxels to reduce the data complexity and a graph-cut was performed to extract individual tree objects. The individual trees paved way for the advanced inventory parameter extraction such as crown dimensions (diameter and height) and branching patterns which would have been near impossible using conventional field measurements. Also, they form as the basic ground information for high resolution image based studies focusing on crown detection and delineations. Next, trees with relatively high completeness are selected and are described by highly accurate piecewise cylinders based on branching structure. This method helped to estimate the tree volume as the sum of the volumes of individual cylinders. Thus, an attempt has been made to accurately describe the actual tree volume in a non-destructive way. The volumes derived for selected trees were used to construct local volume equations of the study site along with the uncertainty in the total volume estimate. Tree volumes are a direct proxy of tree biomass (biomass is generally calculated by multiplying tree volumes with wood density), which help in carbon stock assessment of the forest area. Based on our estimates, tree volumes derived from conventional volume equations have underestimated the actual TLS volume by 28%. However, this estimate needs to be substantiated with destructive sampling. Also, the volume equations constructed using TLS along with uncertainty estimates are used in upscaling the field measurements to the regional biomass estimates using satellite remote sensing datasets. The standard way of producing a regional biomass map is by using a regression model using the field biomass estimates with the remote sensing variable (optical or radar). However, the uncertainty of this measurement at the spatial scale is often not calculated accurately. Both field level and the remote sensing model level errors attribute to the uncertainty of the spatial biomass estimate. Accurate assessment of biomass is essential to several social and economic incentive schemes of United Nations Framework Convention on Climate Change (UNFCCC). In this context, a possible framework to integrate uncertainties from field level measurements to regional-level estimates was established and spatial biomass maps along with uncertainty maps are generated based on random error simulations. This thesis contributes scientifically to the development and testing of new automatic methods exploring the use of terrestrial LiDAR in practical forestry applications (forest inventories). Further testing of these methods in different forest types and densities is required in order to assess their true operational capabilities. Full thesis: pdf Centre for Others |
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