scholarly journals Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA

2019 ◽  
Vol 11 (15) ◽  
pp. 1803 ◽  
Author(s):  
John Hogland ◽  
Nathaniel Anderson ◽  
David L. R. Affleck ◽  
Joseph St. Peter
Keyword(s):  
United States ◽  
Forest Inventory ◽  
Forest Type ◽  
Longleaf Pine ◽  
Basal Area ◽  
Pinus Palustris ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Inventory Data ◽  
Forest Inventory Data

This study improved on previous efforts to map longleaf pine (Pinus palustris) over large areas in the southeastern United States of America by developing new methods that integrate forest inventory data, aerial photography and Landsat 8 imagery to model forest characteristics. Spatial, statistical and machine learning algorithms were used to relate United States Forest Service Forest Inventory and Analysis (FIA) field plot data to relatively normalized Landsat 8 imagery based texture. Modeling algorithms employed include softmax neural networks and multiple hurdle models that combine softmax neural network predictions with linear regression models to estimate key forest characteristics across 2.3 million ha in Georgia, USA. Forest metrics include forest type, basal area and stand density. Results show strong relationships between Landsat 8 imagery based texture and field data (map accuracy > 0.80; square root basal area per ha residual standard errors < 1; natural log transformed trees per ha < 1.081). Model estimates depicting spatially explicit, fine resolution raster surfaces of forest characteristics for multiple coniferous and deciduous species across the study area were created and made available to the public in an online raster database. These products can be integrated with existing tabular, vector and raster databases already being used to guide longleaf pine conservation and restoration in the region.

scholarly journals Forest Type Definitions by Cluster Analysis

2001 ◽  
Vol 16 (3) ◽  
pp. 101-105 ◽  
Author(s):  
Willem W.S. van Hees ◽  
Kevin Dobelbower ◽  
Kenneth Winterberger
Keyword(s):  
Cluster Analysis ◽  
Pacific Northwest ◽  
Forest Inventory ◽  
Forest Type ◽  
Research Station ◽  
Inventory Data ◽  
Southeast Alaska ◽  
Cluster Development ◽  
Forest Inventory Data

Abstract A method is presented to develop forest type definitions by using cluster analysis of forest inventory data collected in southeast Alaska from 1995 through 1998. Species stocking levels were used as variables for cluster development. Pacific Northwest Research Station forest inventory staff could not compute forest type for some forested conditions in southeast Alaska using then existing forest type definitions. Forest type definitions developed by cluster analysis improved computed assignment of forest type. West. J. Appl. For. 16(3):101–105.

scholarly journals Changes in rainfall interception along a secondary forest succession gradient in lowland Panama

2013 ◽  
Vol 17 (11) ◽  
pp. 4659-4670 ◽  
Author(s):  
B. Zimmermann ◽  
A. Zimmermann ◽  
H. L. Scheckenbach ◽  
T. Schmid ◽  
J. S. Hall ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Bayesian Model Averaging ◽  
Forest Succession ◽  
Secondary Forest ◽  
Basal Area ◽  
Secondary Forests ◽  
Inventory Data ◽  
Canopy Interception ◽  
Rainfall Interception ◽  
Forest Inventory Data

Abstract. Secondary forests are rapidly expanding in tropical regions. Yet, despite the importance of understanding the hydrological consequences of land-cover dynamics, the relationship between forest succession and canopy interception is poorly understood. This lack of knowledge is unfortunate because rainfall interception plays an important role in regional water cycles and needs to be quantified for many modeling purposes. To help close this knowledge gap, we designed a throughfall monitoring study along a secondary succession gradient in a tropical forest region of Panama. The investigated gradient comprised 20 forest patches 3 to 130 yr old. We sampled each patch with a minimum of 20 funnel-type throughfall collectors over a continuous 2-month period that had nearly 900 mm of rain. During the same period, we acquired forest inventory data and derived several forest structural attributes. We then applied simple and multiple regression models (Bayesian model averaging, BMA) and identified those vegetation parameters that had the strongest influence on the variation of canopy interception. Our analyses yielded three main findings. First, canopy interception changed rapidly during forest succession. After only a decade, throughfall volumes approached levels that are typical for mature forests. Second, a parsimonious (simple linear regression) model based on the ratio of the basal area of small stems to the total basal area outperformed more complex multivariate models (BMA approach). Third, based on complementary forest inventory data, we show that the influence of young secondary forests on interception in real-world fragmented landscapes might be detectable only in regions with a substantial fraction of young forests. Our results suggest that where entire catchments undergo forest regrowth, initial stages of succession may be associated with a substantial decrease of streamflow generation. Our results further highlight the need to study hydrological processes in all forest succession stages, including early ones.

scholarly journals Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 163 ◽  
Author(s):  
Yan Zhu ◽  
Zhongke Feng ◽  
Jing Lu ◽  
Jincheng Liu
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Forest Biomass ◽  
Mixed Forest ◽  
Accurate Estimation ◽  
Landsat 8 ◽  
Forest Types ◽  
Landsat 8 Oli ◽  
Inventory Data ◽  
Forest Inventory Data

Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha−1 and ranged from 19.1381–195.66 Mg ha−1. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high R 2 and a low root mean square error (RMSE). The R 2 values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha−1, The R 2 values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments.

scholarly journals Towards quantifying the increase of rainfall interception during secondary forest succession

2013 ◽  
Vol 10 (6) ◽  
pp. 7999-8029 ◽  
Author(s):  
B. Zimmermann ◽  
A. Zimmermann ◽  
H. L. Scheckenbach ◽  
T. Schmid ◽  
J. S. Hall ◽  
...  
Keyword(s):  
Land Cover ◽  
Forest Inventory ◽  
Bayesian Model Averaging ◽  
Forest Succession ◽  
Basal Area ◽  
Inventory Data ◽  
Canopy Interception ◽  
Rainfall Interception ◽  
Forest Inventory Data ◽  
Forest Regrowth

Abstract. Large scale forest regrowth is one aspect of modern land-cover change. Yet, despite the importance of understanding the hydrological consequences of land cover dynamics, the relation between forest succession and canopy interception is poorly understood. This lack of knowledge is unfortunate because rainfall interception plays an important role in regional water cycles and needs to be quantified for many modelling purposes. To help close this knowledge gap, we designed a throughfall monitoring study along a secondary succession gradient in a tropical forest region of Panama. The investigated gradient comprises 20 natural forest patches regrowing for 3 up to about 130 yr. We sampled each patch with a minimum of 20 funnel-type throughfall collectors over a continuous two-month period that had nearly 900 mm of rain. At the same time and locations, we acquired forest inventory data and derived several forest structural attributes. We then applied simple and multiple regression models (Bayesian Model Averaging, BMA) and identified those vegetation parameters that have the strongest influence on the variation of canopy interception. Our analyses provide three main findings. First, canopy interception changes rapidly during forest succession. After only a decade, throughfall volumes approach levels that are typical for mature forests. Second, a parsimonious (simple linear regression) model based on the ratio of the basal area of small stems to the total basal area outperformed more complex multivariate models (BMA approach). Third, based on complementary forest inventory data we show that the influence of young secondary forests on interception in real-world fragmented landscapes might be detectable only in regions with a substantial fraction of very young forests. In case entire catchments are subject to forest regrowth, initial stages may be associated with undesirable effects on streamflow generation. Our results further highlight the need to study all forest succession stages, including early ones.

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.

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