Forest inventories are critical for effective management of forest resources. Recently, the use of terrestrial laser scanning (TLS) to automatically extract forest inventory parameters at tree level ...(e.g. tree location, diameter at breast height (DBH) and height) has gained significant importance. TLS using both single-scan and multi-scan techniques, not only helps in detailed and accurate measurements of tree objects but also helps increase the measurement frequency. In the current study, we develop an automated solution to extract forest inventory parameters at individual tree level from TLS data by using random sample consensus (RANSAC)-based circle fitting algorithm. The method was evaluated on both single- and multiscan data by characterizing four circular plots of radius 20 m in dry deciduous forests of Betual, Madhya Pradesh (India). Over all the plots, tree detection rates of 75% and 97% were obtained using single- and multi-scan TLS data respectively. Tree detection rates were significantly affected by increase in distance from the scanner, in single-scan approach when compared to multi-scan approach. Field based DBH measurements correlated well using both single (R2 = 0.96) and multiple scans (R2 = 0.99). The DBH estimates from multi-scan TLS data resulted in low root-mean-square error (RMSE) of 2.2 cm compared to that of 4.1 cm using single-scan. Further, tree heights were extracted from TLS data and validated with selectively measured trees on field (R2 = 0.98; N = 65). The RMSE of tree height was estimated to be 1.65 m. The current results show the potential use of TLS in automatically deriving forest inventory parameters with reliable accuracy at individual tree level.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
This document provides design principles for concrete beams reinforced with glass fiber reinforced polymer (GFRP) bars per the ACI 440.1R-15 regulation. One of the main advantages of using glass ...fiber reinforced polymer rods instead of traditional steel reinforced rods is their lighter weight and higher corrosion resistance. However, the bending failure mode of FRP reinforced concrete (FRP-RC) beams is brittle rather than ductile because the elasticity of fiber reinforced polymer (FRP) bars is linear until failure and the elongation at break is small. For FRP-RC elements, concrete crushing compression failure, which gives various warnings before failure, is the preferred failure mode. In other words, unlike the usual design practice for reinforced concrete (steel-RC) beams, for FRP-RC beams, an over-reinforced structure is preferable to an under-reinforced structure. In addition, since the FRP RC member has low rigidity of the FRP rod, it bends more and cracks larger than the steel RC member. These factors limit the field of application of FRP. Here is a design example of a rectangular beam with tension reinforcement according to ACI regulations.
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 (R
2
= 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 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.
Accurate estimates of spatial above ground biomass (AGB) in tropical forests are important in understanding the global carbon cycle. Microwave and optical remote sensing datasets have been used ...extensively for AGB estimation, but their uses are restricted due to saturation in high biomass region. To overcome saturation issues of single sensor based models, the current paper uses a non-parametric Random Forest based approach to spatially estimate biomass over Indian forests using field inventory data in combination with optical (MODIS) and Microwave (L-band ALOS-PALSAR) images along with other bio-climatic parameters (e.g., rainfall, temperature) which significantly influence the biomass accumulation in an ecosystem. Plot level biomass estimates for 6678 sample plots (0.1 ha size), inventoried as part of robustly designed National Forest Inventory (NFI), are computed using volumetric equations, wood density and biomass expansion factors. Spatial above ground biomass estimates were generated using random forest model over two scenarios. Firstly, a single nation-wide model using all the available plot data and secondly, physiographic zone (14 zones over India) wise models with plot data over respective zones. AGB stored in Indian forest is estimated as 7952.3 million tonnes (Carbon equivalent: 3737.58 TgC) with a root mean square error (RMSE) of 31.2% using national level model. Physiographic zone level models estimated the country's biomass as 7597.45 million tonnes (Carbon equivalent: 3570.8 TgC). The above ground biomass estimates from our study indicates that the estimation error in physiographic zone model varies from 25.24% to 54.15% depending upon the sample size and biomass range. We observed that L-band microwave saturated in 140–160 Mg ha−1 range and currently available microwave wavelengths alone is not sufficient to predict entire range of biomass over Indian forests. Inclusion of multisource data using random forest regression model increase the saturation range to 350 Mg ha−1 which is a significant improvement as 94.7% of Indian forests are covered in this range. Model estimation error reduces to 25.6% in AGB range up to 350 Mg ha−1.
•L-band ALOS PALSAR backscatter saturates in biomass range140–160 Mg ha−1.•Inclusion of multi source data improved the saturation point to ~350 Mg ha−1.•94.7% of Indian forest cover is covered in AGB range below 350 Mg ha−1.•Biomass stored in Indian forest estimated as 7952.3 Mt. (carbon equivalent: 3737.58 TgC).
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•Our results show that Indian forests are sequestering Carbon.•Forest cover as well as carbon density increases over study period.•Study highlighted the regional differences in forest ...C density in Indian forest.•National forest C for 1994 and 2010 is estimated as 3911.78 and 4368.03TgC.•Mean carbon density for 1994 and 2010 is estimated as 61.14 and 64.08Mgha−1.
Forest carbon (C) estimates are the key inputs to the understanding of the global C cycle. We report the estimates of forest carbon pool and its spatial distribution in the Indian forests for the years 1994 and 2010 at 5km grid level. This study improves upon earlier spatial estimates of Indian forest biomass carbon by using data from a robustly designed National Forest Inventory (NFI). The realized sampling intensity has addressed the large heterogeneity of the Indian forest types and allowed the computation of 5km grid level forest C, yielding a realistic estimate of forest biomass C in Indian forests. Forest cover density maps were intersected with 5km mesh and estimates of forest area, forest carbon density for each Agro-ecological sub region and forest carbon pools were linked to the 5km grid coverage of India. National forest carbon estimates for the years 1994 and 2010 are 3911.78 and 4368.03TgC respectively, and these estimates showed a net increase of 456.25TgC in 16years. Uncertainty of the estimates has been addressed spatially. Mean forest carbon density increased from 61.14Mgha−1 in 1994 to 64.08Mgha−1 in 2010. C densities for dense and open forest in 1994 estimated as 77.08 and 38.47Mgha−1 with total C pools of 2895.28TgC and 1016.50TgC which has increased to 80.24Mgha−1 and 41.69Mgha−1 with total C pools of 3176.48TgC and 1191.55TgC in 2010. This study provides the first 5km level C analysis for Indian forests. Spatial distribution of C shows large differences in C density over Indian forests indicating that estimates of the spatial distribution of C are even more important than the total C pool estimates of the country.
India is endowed with a rich forest cover. Over 21 % of country's area is covered by forest of varied composition and structure. Due to large amount of carbon stored in forests and their role in land ...surface and climatic processes, it is important to monitor forests for effective management and modeling studies. The disturbance regimes associated with forest regeneration and recovery, occurring in a heterogeneous matrix of confounding land covers makes forest monitoring an involved and complex task. Over a 13 year period (2000–2013), detection of forest cover loss at regional scale using a coarse resolution imaging sensor (MODIS −250m) in Indian forests is attempted in the present study. MODIS provides a rich basis for forest cover monitoring at regional scales on an interannual to decadal timescales due its huge database and high temporal frequency. Forest cover loss across different forest types in parts of Maharashtra, Odisha, Chhattisgarh and Telangana states were identified using a forest-likelihood and a multi-thresholding approach. The study reveals that considerable amount of deforested patches exist over the study areas during the 2000 to 2013. Results also suggested that the detection accuracy improved with the increase of fraction of deforestation in the MODIS pixel, but still relatively small changes were also detected.
Accurate plot-level estimates of above ground biomass (AGB) are crucial for reducing uncertainty in spatial AGB quantification, a key requirement for Reducing Emissions from Deforestation and Forest ...Degradation framework (REDD+) and achieving Intended Nationally Determined Contributions (INDC) goals. This study explores the impact of plot size, selection and use of height-diameter (H–D) model, and allometric models on plot-level AGB estimation in tropical Indian forests. We analyze a dataset of 8179 tree measurements from 51 one-hectare plots across four tropical sites to investigate these factors and improve estimation accuracy. Our findings highlight the importance of locally derived H–D models for superior accuracy. We propose two H–D models for improved plot-level AGB estimation accuracy in tropical Indian forests: a general model with a residual standard error (RSE) of 4.18 m and a forest type-specific model with a RSE of 3.65 m. These models outperform global models, which significantly underestimate tree height by up to 51.3% and overestimate by 43.4% across our sites. The developed H–D models were evaluated using spatial cross-validation to ensure broader applicability across Indian tropical forests. Further, we evaluated the impact of the choice of the allometric model on plot-level AGB estimation using the prevalent models (local destructive models by the Forest Survey of India and global pantropical models). Our results indicate minimal variability between the local destructive models and the pantropical model when height measurements are derived from this study's developed H–D models. We have also investigated the effect of plot size on plot-level AGB estimation using synthetic plots generated from 1-ha plot data. Notably, the relative error diminishes from approximately 22% to about 5% as plot size increases from 0.04 ha (20x20m) to 0.49 ha (70x70m) across all sites, and we recommend field plots of size ≥0.5 ha for AGB mapping over Tropical Indian Forests. By making this data publicly available, this study paves the way for more accurate spatial AGB mapping in tropical Indian forests, ultimately contributing to better forest management.
•Global Height-Diameter model does not predict tree height accurately over Indian Forests.•Height based allometric model gives better biomass estimation than simple diameter based models.•Biomass estimation accuracies increases with increasing plot size.•Plot size larger than 0.5 ha is recommended for forest biomass estimation over Tropical Indian forests.
Digital Elevation Model represents elevation in two dimensional varying raster. It provides significant analysis of the terrain characteristics and planning. Availability of high-resolution ...satellites helps extraction of DEM at different global scales from the stereo imagery. Cartosat-1 is an along track sensor which acquire stereo data acquired continuously with fore and aft cameras. Stereo data including Rational Polynomial Coefficients (RPCs) is used to generate DEM based on standard Rational Polynomial Model. This paper evaluates the DEM generated from Ames Stereo Pipeline (ASP) for Carosat-1 stereo pair with Shuttle Radar Topography Mission (SRTM) DEM. The accuracy of the DEM extracted from ASP is tested with the standard SRTM DEM. The performance analysis shows that correlation of AMES DEM with SRTM shown 0.97 and RMSE of order 13.64m. The AMES DEM has shown high correlation and low RMSE values with the standard SRTM DEM.
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