Integration of field sampling and LiDAR data in forest inventories: comparison of area-based approach and (lognormal) universal kriging

2021 ◽  
Vol 78 (2) ◽  
Author(s):  
Isabel Aulló-Maestro ◽  
Cristina Gómez ◽  
Eva Marino ◽  
Miguel Cabrera ◽  
Antonio Vázquez De La Cueva ◽  
...  
Keyword(s):  
Universal Kriging ◽  
Lidar Data ◽  
Field Sampling ◽  
Forest Inventories

Presenting the GeForse approach to create synthetic LiDAR data from simulated forest stands to optimize forest inventories

2021 ◽  
Author(s):  
Fabian E. Fassnacht ◽  
Jannika Schäfer ◽  
Hannah Weiser ◽  
Lukas Winiwarter ◽  
Nina Krašovec ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Forest Growth ◽  
Sensitivity Analyses ◽  
Future Application ◽  
Lidar Data ◽  
Real Point ◽  
Entire Area ◽  
Single Tree ◽  
Forest Inventories ◽  
Tree Models

<p>LiDAR-based forest inventories focusing on estimating and mapping structure-related forest inventory variables across large areas have reached operationality. In the commonly applied area-based approach, a set of field-measured inventory plots is combined with spatially co-located airborne laserscanning data to train empirical models that can then be used to predict the target metric over the entire area covered by LiDAR data.</p><p>The area-based approach was found to produce reliable estimates for structure-related forest inventory metrics such as wood volume and biomass across many forest types. However, the current workflows still leave space for improvement that may result in cost-reduction with respect to data acquisition or improved accuracies. This is particularly relevant as the area-based approach is increasingly used in operational forestry settings. To further optimize existing workflows, experiments are required that need large amounts of forest inventory data (e.g., to examine the effect of sample size or the field inventory design on the model performances) or multiple LiDAR acquisitions (e.g., to identify optimal/cost-efficient acquisition settings). The acquisition of these types of data is cost-intensive and is hence often limited to small extents within scientific experiments.</p><p>Here, we present the ”GeForse - Generating Synthetic Forest Remote Sensing Data” approach to create synthetic LiDAR datasets suitable for such optimization studies. GeForse combines a database of single-tree models consisting of point clouds extracted from real LiDAR data with the outputs of a spatially explicit, single tree-based forest growth simulator (in this case SILVA). For each simulated tree, we insert a real point-cloud tree with properties (species, crown diameter, height) matching the properties of the simulated tree. This results in a synthetic 3D forest with a realistic 3D-structure where the inventory metrics of each tree are known. This 3D forest then serves as input to the “Heidelberg LiDAR Operations Simulator” (HELIOS++, https://github.com/3dgeo-heidelberg/helios) and thereby enables the simulation of LiDAR acquisition flights with varying acquisition settings and flight trajectories. In combination with the “full inventory” of all trees in the simulated forest, this enables a wide variety of sensitivity analyses.</p><p>In this contribution, we give an overview of the complete GeForse approach from extracting the tree models, to generating the 3D forest and simulating LiDAR flights over the 3D forest using HELIOS++. Further, we present a brief case-study where this approach was applied to optimize certain aspects of area-based forest inventory approaches using LiDAR data from a forest area in central Europe. Finally, we provide an outlook on future application fields of the GeForse approach.</p>

A Test of Cluster Sampling in Forest Inventories

1975 ◽  
Vol 5 (2) ◽  
pp. 269-272 ◽  
Author(s):  
G. M. Bonnor
Keyword(s):  
Random Sampling ◽  
Sampling Design ◽  
Simple Random Sampling ◽  
Cluster Sampling ◽  
Field Sampling ◽  
Forest Inventories ◽  
Straight Line ◽  
Time Required ◽  
Variance Estimates ◽  
The Given

During a recent pilot survey in Newfoundland, forest data were collected using a stratified, one-stage cluster-sampling design. The data were analyzed to determine if, within the stratified framework, cluster sampling would be more efficient than simple random sampling. Each cluster consisted of five point-samples located in a straight line. For the analysis, volume and variance estimates were determined from clusters of 1,2,3,4 and 5 points. During the survey, records were kept of the time required to complete various field-sampling tasks. These were used in lieu of cost data in the analysis. Results indicated that, for the given conditions, simple random sampling was more efficient than cluster sampling. However, relatively small changes in the conditions would make cluster sampling more efficient.

New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based Monte Carlo approach with applications to small-area estimation

2013 ◽  
Vol 43 (11) ◽  
pp. 1023-1031 ◽  
Author(s):  
Daniel Mandallaz ◽  
Jochen Breschan ◽  
Andreas Hill
Keyword(s):  
Auxiliary Information ◽  
Cluster Sampling ◽  
Lidar Data ◽  
Second Phase ◽  
Regression Estimator ◽  
Two Phase ◽  
Sampling Schemes ◽  
Forest Inventories ◽  
Phase Sampling ◽  
Two Phase Sampling

We consider two-phase sampling schemes where one component of the auxiliary information is known in every point (“wall-to-wall”) and a second component is available only in the large sample of the first phase, whereas the second phase yields a subsample with the terrestrial inventory. This setup is of growing interest in forest inventory thanks to the recent advances in remote sensing, in particular, the availability of LiDAR data. We propose a new two-phase regression estimator for global and local estimation and derive its asymptotic design-based variance. The new estimator performs better than the classical regression estimator. Furthermore, it can be generalized to cluster sampling and two-stage tree sampling within plots. Simulations and a case study with LiDAR data illustrate the theory.

scholarly journals Integrating field sampling, geostatistics and remote sensing to map wetland vegetation in the Pantanal, Brazil

2011 ◽  
Vol 8 (3) ◽  
pp. 667-686 ◽  
Author(s):  
J. Arieira ◽  
D. Karssenberg ◽  
S. M. de Jong ◽  
E. A. Addink ◽  
E. G. Couto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Cross Validation ◽  
Remotely Sensed ◽  
Wetland Vegetation ◽  
Universal Kriging ◽  
Field Sampling ◽  
Remotely Sensed Data ◽  
Vegetation Communities

Abstract. Development of efficient methodologies for mapping wetland vegetation is of key importance to wetland conservation. Here we propose the integration of a number of statistical techniques, in particular cluster analysis, universal kriging and error propagation modelling, to integrate observations from remote sensing and field sampling for mapping vegetation communities and estimating uncertainty. The approach results in seven vegetation communities with a known floral composition that can be mapped over large areas using remotely sensed data. The relationship between remotely sensed data and vegetation patterns, captured in four factorial axes, were described using multiple linear regression models. There were then used in a universal kriging procedure to reduce the mapping uncertainty. Cross-validation procedures and Monte Carlo simulations were used to quantify the uncertainty in the resulting map. Cross-validation showed that accuracy in classification varies according with the community type, as a result of sampling density and configuration. A map of uncertainty derived from Monte Carlo simulations revealed significant spatial variation in classification, but this had little impact on the proportion and arrangement of the communities observed. These results suggested that mapping improvement could be achieved by increasing the number of field observations of those communities with a scattered and small patch size distribution; or by including a larger number of digital images as explanatory variables in the model. Comparison of the resulting plant community map with a flood duration map, revealed that flooding duration is an important driver of vegetation zonation. This mapping approach is able to integrate field point data and high-resolution remote-sensing images, providing a new basis to map wetland vegetation and allow its future application in habitat management, conservation assessment and long-term ecological monitoring in wetland landscapes.

scholarly journals Using Airborne Lidar to Discern Age Classes of Cottonwood Trees in a Riparian Area

2006 ◽  
Vol 21 (3) ◽  
pp. 149-158 ◽  
Author(s):  
A. Farid ◽  
D.C. Goodrich ◽  
S. Sorooshian
Keyword(s):  
Canopy Cover ◽  
Laser Pulses ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Linear Regression Models ◽  
Age Classes ◽  
Forest Inventories ◽  
Cottonwood Trees ◽  
Airborne Lidar Data ◽  
Remote Sensing Methods

Abstract Airborne lidar (light detecting and ranging) is a useful tool for probing the structure of forest canopies. Such information is not readily available from other remote sensing methods and is essential for modern forest inventories. In this study, small-footprint lidar data were used to estimate biophysical properties of young, mature, and old cottonwood trees in the San Pedro River basin near Benson, Arizona. The lidar data were acquired in June 2004, using Optech’s 1233 ALTM during flyovers conducted at an altitude of 600 m. Canopy height, crown diameter, stem dbh, canopy cover, and mean intensity of return laser pulses from the canopy surface were estimated for the cottonwood trees from the data. Linear regression models were used to develop equations relating lidar-derived tree characteristics with corresponding field acquired data for each age class of cottonwoods. The lidar estimates show a good degree of correlation with ground-based measurements. This study also shows that other parameters of young, mature, and old cottonwood trees such as height and canopy cover, when derived from lidar, are significantly different (P < 0.05). Additionally, mean crown diameters of mature and young trees are not statistically different at the study site (P = 0.31). The results illustrate the potential of airborne lidar data to differentiate different age classes of cottonwood trees for riparian areas quickly and quantitatively. West. J. Appl. For. 21(3):149–158.

scholarly journals Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

2021 ◽  
Vol 13 (24) ◽  
pp. 5113
Author(s):  
Elias Ayrey ◽  
Daniel J. Hayes ◽  
John B. Kilbride ◽  
Shawn Fraver ◽  
John A. Kershaw ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Regional Scale ◽  
Lidar Data ◽  
Stem Density ◽  
Calibration Data ◽  
Eastern United States ◽  
Forest Inventories ◽  
Public Data ◽  
Forest Attributes

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

ABOVEGROUND BIOMASS AND CARBON STOCK ESTIMATION USING DOUBLE SAMPLING APPROACH AND REMOTELY-SENSED DATA

2016 ◽  
Vol 78 (5-4) ◽  
Author(s):  
Nurul Ain Mohd Zaki ◽  
Zulkiflee Abd Latif ◽  
Mohd Zainee Zainal
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Carbon Stock ◽  
Field Data ◽  
Double Sampling ◽  
Lidar Data ◽  
Field Sampling ◽  
Remotely Sensed Data ◽  
Sampling Approach ◽  
Carbon Inventory

Tropical forest embraces a large stock of carbon and contributes to the enormous amount of aboveground biomass (AGB) in the global carbon cycle. In order to quantify the carbon inventory, field data is vital for accurately determining the forest parameter such as diameter at the breast height (DBH), height  of the tree (h) ,crown diameter (CD) and tree species. The merging of the multi-sensory remote sensing which is LiDAR (Light Detection and Ranging) and very high resolution satellite imagery can reduce the labor intensive of field sampling for a large area of carbon inventory data. Double sampling approach which is combination of the field sampling plot measurement with ancillary remote sensing data used to improve the precision of AGB estimation compared by using field data alone. Hence, this study aims: (1) to describe the use of field data plots in a statistical way, and (2) to determine the potential of LiDAR data in a double sampling forest aboveground biomass and carbon stock inventories and (3) to compare the used of field data plot itself or combination with LiDAR data to quantify the aboveground biomass and carbon stock for upcoming inventories.

scholarly journals Assessing single photon LiDAR for operational implementation of an enhanced forest inventory in diverse mixedwood forests

2021 ◽  
Vol 97 (01) ◽  
pp. 78-96
Author(s):  
Joanne C. White ◽  
Margaret Penner ◽  
Murray Woods
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Forest Type ◽  
Single Photon ◽  
Basal Area ◽  
Stem Volume ◽  
Lidar Data ◽  
Large Area ◽  
Mixedwood Forests ◽  
Forest Inventories

Airborne laser scanning (ALS; LiDAR) data are an increasingly common data source for forest inventories, and approaches integrating ALS data with field plot measurements have become operational in several jurisdictions. As technology continues to evolve, different LiDAR sensors can provide new opportunities to incorporate LiDAR data into forest inventory workflows. Single photon LiDAR (SPL) enables efficient, large area data acquisition and merits further investigation for forest inventory applications. Herein, we investigated the capacity of leaf-on SPL data, combined with 269 field plots, for estimating forest inventory attributes in the Great Lakes–St. Lawrence mixedwood forests of southern Ontario, Canada. Inventory attribute estimates were validated at the stand level using independent reference data acquired for 27 intensively sampled stands. Top height, Lorey’s height, gross total volume for merchantable stems, merchantable stem volume, basal area, quadratic mean diameter, and total aboveground biomass were estimated with a relative RMSE of 13.52%, 7.24%, 14.61%, 16.27%, 14.42%, 12.25%, and 11.72%, respectively. Relative bias was < 1% for all attributes except top height (10.34%), merchantable volume (3.37%), and basal area (1.68%). Accuracy and bias varied by forest type and stand-level validation was important for assessing model performance in different stand conditions. SPL data can be used to generate accurate, area-based forest inventories in mixedwood forests that have a multitude of tree species and complex forest management histories.

Sign in / Sign up

Export Citation Format

Share Document