Using forest inventory data to predict winter habitat use by fisherMartes pennanti in British Columbia, Canada

2006 ◽  
Vol 51 (3) ◽  
pp. 275-282 ◽  
Author(s):  
Gilbert Proulx
Keyword(s):  
British Columbia ◽  
Habitat Use ◽  
Forest Inventory ◽  
Inventory Data ◽  
Winter Habitat ◽  
Forest Inventory Data

scholarly journals Rapid Assessment of Tree Damage Resulting from a 2020 Windstorm in Iowa, USA

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 555
Author(s):  
Thomas C. Goff ◽  
Mark D. Nelson ◽  
Greg C. Liknes ◽  
Tivon E. Feeley ◽  
Scott A. Pugh ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Damage Zone ◽  
Satellite Image ◽  
Rapid Assessment ◽  
Aerial Survey ◽  
Inventory Data ◽  
Tree Damage ◽  
Forest Inventory Data ◽  
Damage Susceptibility ◽  
The Impact

A need to quantify the impact of a particular wind disturbance on forest resources may require rapid yet reliable estimates of damage. We present an approach for combining pre-disturbance forest inventory data with post-disturbance aerial survey data to produce design-based estimates of affected forest area and number and volume of trees damaged or killed. The approach borrows strength from an indirect estimator to adjust estimates from a direct estimator when post-disturbance remeasurement data are unavailable. We demonstrate this approach with an example application from a recent windstorm, known as the 2020 Midwest Derecho, which struck Iowa, USA, and adjacent states on 10–11 August 2020, delivering catastrophic damage to structures, crops, and trees. We estimate that 2.67 million trees and 1.67 million m3 of sound bole volume were damaged or killed on 23 thousand ha of Iowa forest land affected by the 2020 derecho. Damage rates for volume were slightly higher than for number of trees, and damage on live trees due to stem breakage was more prevalent than branch breakage, both likely due to higher damage probability in the dominant canopy of larger trees. The absence of post-storm observations in the damage zone limited direct estimation of storm impacts. Further analysis of forest inventory data will improve understanding of tree damage susceptibility under varying levels of storm severity. We recommend approaches for improving estimates, including increasing spatial or temporal extents of reference data used for indirect estimation, and incorporating ancillary satellite image-based products.

scholarly journals Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing

2021 ◽  
Vol 13 (8) ◽  
pp. 1592
Author(s):  
Nikolai Knapp ◽  
Andreas Huth ◽  
Rico Fischer
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Canopy Height ◽  
Biomass Estimation ◽  
Inventory Data ◽  
Border Effects ◽  
Tree Crowns ◽  
Forest Inventory Data ◽  
Estimation Uncertainty ◽  
Voxel Space

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.

Ecological forecasting of tree growth: Regional fusion of tree‐ring and forest inventory data to quantify drivers and characterize uncertainty

2022 ◽  
Author(s):  
Kelly A. Heilman ◽  
Michael C. Dietze ◽  
Alexis A. Arizpe ◽  
Jacob Aragon ◽  
Andrew Gray ◽  
...  
Keyword(s):  
Tree Ring ◽  
Tree Growth ◽  
Forest Inventory ◽  
Inventory Data ◽  
Ecological Forecasting ◽  
Forest Inventory Data

scholarly journals Evaluating the Condition of Selectively Logged Production Forests in Myanmar: An Analysis Using Large-scale Forest Inventory Data for Yedashe Township

2018 ◽  
Vol 23 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Zar Chi Win ◽  
Nobuya Mizoue ◽  
Tetsuji Ota ◽  
Tsuyoshi Kajisa ◽  
Shigejiro Yoshida ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Large Scale ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
Production Forests

Forest Biomass Estimation Based on Forest Inventory Data in Middle and Last Scales

2011 ◽  
Vol 183-185 ◽  
pp. 220-224
Author(s):  
Ming Ze Li ◽  
Wen Yi Fan ◽  
Ying Yu
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Inventory Data ◽  
Biomass Equation ◽  
Tree Species Composition ◽  
Forest Inventory Data ◽  
Ecosystem Carbon

The forest biomass (which is referred to the arbor aboveground biomass in this research) is one of the most primary factors to determine the forest ecosystem carbon storages. There are many kinds of estimating methods adapted to various scales. It is a suitable method to estimate forest biomass of the farm or the forestry bureau in middle and last scales. First each subcompartment forest biomass should be estimated, and then the farm or the forestry bureau forest biomass was estimated. In this research, based on maoershan farm region, first the single tree biomass equation of main tree species was established or collected. The biomass of each specie was calculated according to the materials of tally, such as height, diameter and so on in the forest inventory data. Secondly, each specie’s biomass and total biomass in subcompartment were calculated according to the tree species composition in forest management investigation data. Thus the forest biomass spatial distribution was obtained by taking subcompartment as a unit. And last the forest total biomass was estimated.

scholarly journals Use of artificial neural networks and geographic objects for classifying remote sensing imagery

CERNE ◽  
2014 ◽  
Vol 20 (2) ◽  
pp. 267-276 ◽  
Author(s):  
Pedro Resende Silva ◽  
Fausto Weimar Acerbi Júnior ◽  
Luis Marcelo Tavares de Carvalho ◽  
José Roberto Soares Scolforo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Image Segmentation ◽  
Artificial Neural Networks ◽  
Forest Inventory ◽  
Landsat Tm ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
Artificial Neural ◽  
Test Use

The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1) to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2) to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3) to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.

scholarly journals High-resolution forest carbon flux mapping and monitoring in the Pacific Northwest with time since disturbance and disturbance legacies inferred from remote sensing and inventory data

2016 ◽  
Author(s):  
Huan Gu ◽  
Christopher A. Williams ◽  
Bardan Ghimire ◽  
Feng Zhao ◽  
Chengquan Huang
Keyword(s):  
Remote Sensing ◽  
Pacific Northwest ◽  
Forest Inventory ◽  
Stand Age ◽  
Net Ecosystem Productivity ◽  
Fine Scale ◽  
Ecosystem Productivity ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
The Pacific

Abstract. Assessment of forest carbon storage and uptake is central to understanding the role forests play in the global carbon cycle and policy-making aimed at mitigating climate change. Current U.S. carbon stocks and fluxes are monitored and reported at fine-scale regionally, or coarse-scale nationally. We proposed a new methodology of quantifying carbon uptake and release across forested landscapes in the Pacific Northwest (PNW) at a fine scale (30 m) by combining remote-sensing based disturbance year, disturbance type, and aboveground biomass with forest inventory data in a carbon modelling framework. Time since disturbance is a key intermediate determinant that aided the assessment of disturbance-driven carbon emissions and removals legacies. When a recent disturbance was detected, time since disturbance can be directly determined by remote sensing-derived disturbance products; and if not, time since last stand-clearing was inferred from remote sensing-derived 30 m biomass map and field inventory-derived species-specific biomass regrowth curves. Net ecosystem productivity (NEP) was further mapped based on carbon stock and flux trajectories that described how NEP changes with time following harvest, fire, or bark beetle disturbances of varying severity. Uncertainties from biomass map and forest inventory data were propagated by probabilistic sampling to provide a probabilistic, statistical distribution of stand age and NEP for each forest pixel. We mapped mean, standard deviation and statistical distribution of stand age and NEP at 30 m in the PNW region. Our map indicated a net ecosystem productivity of 5.2 Tg C y−1 for forestlands circa 2010 in the study area, with net uptake in relatively mature (> 24 year old) forests (13.6 Tg C y−1) overwhelming net negative NEP from tracts that have seen recent harvest (−6.4 Tg C y−1), fires (−0.5 Tg C y−1), and bark beetle outbreaks (−1.4 Tg C y−1). The approach will be applied to forestlands in other regions of the conterminous U.S. to advance a more comprehensive monitoring, mapping and reporting the carbon consequences of forest change across the U.S.

Estimation of the North–South Transect of Eastern China forest biomass using remote sensing and forest inventory data

2013 ◽  
Vol 34 (15) ◽  
pp. 5598-5610 ◽  
Author(s):  
Yanhua Gao ◽  
Xinxin Liu ◽  
Chengcheng Min ◽  
Honglin He ◽  
Guirui Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Eastern China ◽  
Forest Biomass ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
The North
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