scholarly journals Detecting Change in Forest Structure with Simulated GEDI Lidar Waveforms: A Case Study of the Hemlock Woolly Adelgid (HWA; Adelges tsugae) Infestation

2020 ◽  
Vol 12 (8) ◽  
pp. 1304 ◽  
Author(s):  
Peter Brehm Boucher ◽  
Steven Hancock ◽  
David A Orwig ◽  
Laura Duncanson ◽  
John Armston ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Structural Changes ◽  
Hemlock Woolly Adelgid ◽  
Adelges Tsugae ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Forest Plot ◽  
Lidar Data ◽  
Spatial Coverage ◽  
Plant Area

The hemlock woolly adelgid (HWA; Adelges tsugae) is an invasive insect infestation that is spreading into the forests of the northeastern United States, driven by the warmer winter temperatures associated with climate change. The initial stages of this disturbance are difficult to detect with passive optical remote sensing, since the insect often causes its host species, eastern hemlock trees (Tsuga canadensis), to defoliate in the midstory and understory before showing impacts in the overstory. New active remote sensing technologies—such as the recently launched NASA Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar—can address this limitation by penetrating canopy gaps and recording lower canopy structural changes. This study explores new opportunities for monitoring the HWA infestation with airborne lidar scanning (ALS) and GEDI spaceborne lidar data. GEDI waveforms were simulated using airborne lidar datasets from an HWA-infested forest plot at the Harvard Forest ForestGEO site in central Massachusetts. Two airborne lidar instruments, the NASA G-LiHT and the NEON AOP, overflew the site in 2012 and 2016. GEDI waveforms were simulated from each airborne lidar dataset, and the change in waveform metrics from 2012 to 2016 was compared to field-derived hemlock mortality at the ForestGEO site. Hemlock plots were shown to be undergoing dynamic changes as a result of the HWA infestation, losing substantial plant area in the middle canopy, while still growing in the upper canopy. Changes in midstory plant area (PAI 11–12 m above ground) and overall canopy permeability (indicated by RH10) accounted for 60% of the variation in hemlock mortality in a logistic regression model. The robustness of these structure-condition relationships held even when simulated waveforms were treated as real GEDI data with added noise and sparse spatial coverage. These results show promise for future disturbance monitoring studies with ALS and GEDI lidar data.

scholarly journals The Past, Present, and Future of the Hemlock Woolly Adelgid (Adelges tsugae) and Its Ecological Interactions with Eastern Hemlock (Tsuga canadensis) Forests

Insects ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 172 ◽  
Author(s):  
Aaron Ellison ◽  
David Orwig ◽  
Matthew Fitzpatrick ◽  
Evan Preisser
Keyword(s):  
Biological Diversity ◽  
Carbon Sources ◽  
Hemlock Woolly Adelgid ◽  
Adelges Tsugae ◽  
Ecosystem Dynamics ◽  
Eastern Hemlock ◽  
Carbon Sinks ◽  
The North ◽  
Spread Dynamics ◽  
Landscape Scales

The nonnative hemlock woolly adelgid is steadily killing eastern hemlock trees in many parts of eastern North America. We summarize impacts of the adelgid on these forest foundation species; review previous models and analyses of adelgid spread dynamics; and examine how previous forecasts of adelgid spread and ecosystem dynamics compare with current conditions. The adelgid has reset successional sequences, homogenized biological diversity at landscape scales, altered hydrological dynamics, and changed forest stands from carbon sinks into carbon sources. A new model better predicts spread of the adelgid in the south and west of the range of hemlock, but still under-predicts its spread in the north and east. Whether these underpredictions result from inadequately modeling accelerating climate change or accounting for people inadvertently moving the adelgid into new locales needs further study. Ecosystem models of adelgid-driven hemlock dynamics have consistently forecast that forest carbon stocks will be little affected by the shift from hemlock to early-successional mixed hardwood stands, but these forecasts have assumed that the intermediate stages will remain carbon sinks. New forecasting models of adelgid-driven hemlock decline should account for observed abrupt changes in carbon flux and ongoing and accelerating human-driven land-use and climatic changes.

scholarly journals Estimating Time Since the Last Stand-Replacing Disturbance (TSD) from Spaceborne Simulated GEDI Data: A Feasibility Study

2020 ◽  
Vol 12 (21) ◽  
pp. 3506
Author(s):  
Nuria Sanchez-Lopez ◽  
Luigi Boschetti ◽  
Andrew T. Hudak ◽  
Steven Hancock ◽  
Laura I. Duncanson
Keyword(s):  
Data Fusion ◽  
Large Scale ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Training Dataset ◽  
Lidar Data ◽  
Landsat 8 ◽  
Image Objects ◽  
Airborne Lidar Data

Stand-level maps of past forest disturbances (expressed as time since disturbance, TSD) are needed to model forest ecosystem processes, but the conventional approaches based on remotely sensed satellite data can only extend as far back as the first available satellite observations. Stand-level analysis of airborne LiDAR data has been demonstrated to accurately estimate long-term TSD (~100 years), but large-scale coverage of airborne LiDAR remains costly. NASA’s spaceborne LiDAR Global Ecosystem Dynamics Investigation (GEDI) instrument, launched in December 2018, is providing billions of measurements of tropical and temperate forest canopies around the globe. GEDI is a spatial sampling instrument and, as such, does not provide wall-to-wall data. GEDI’s lasers illuminate ground footprints, which are separated by ~600 m across-track and ~60 m along-track, so new approaches are needed to generate wall-to-wall maps from the discrete measurements. In this paper, we studied the feasibility of a data fusion approach between GEDI and Landsat for wall-to-wall mapping of TSD. We tested the methodology on a ~52,500-ha area located in central Idaho (USA), where an extensive record of stand-replacing disturbances is available, starting in 1870. GEDI data were simulated over the nominal two-year planned mission lifetime from airborne LiDAR data and used for TSD estimation using a random forest (RF) classifier. Image segmentation was performed on Landsat-8 data, obtaining image-objects representing forest stands needed for the spatial extrapolation of estimated TSD from the discrete GEDI locations. We quantified the influence of (1) the forest stand map delineation, (2) the sample size of the training dataset, and (3) the number of GEDI footprints per stand on the accuracy of estimated TSD. The results show that GEDI-Landsat data fusion would allow for TSD estimation in stands covering ~95% of the study area, having the potential to reconstruct the long-term disturbance history of temperate even-aged forests with accuracy (median root mean square deviation = 22.14 years, median BIAS = 1.70 years, 60.13% of stands classified within 10 years of the reference disturbance date) comparable to the results obtained in the same study area with airborne LiDAR.

scholarly journals Hierarchical Geographic Object-Based Vegetation Type Extraction Based on Multi-Source Remote Sensing Data

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1271
Author(s):  
Xuegang Mao ◽  
Yueqing Deng ◽  
Liang Zhu ◽  
Yao Yao
Keyword(s):  
Remote Sensing ◽  
Forest Type ◽  
Vegetation Type ◽  
Remote Sensing Data ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Sensing Data ◽  
Object Based ◽  
Four Levels ◽  
Airborne Lidar Data

Providing vegetation type information with accurate surface distribution is one of the important tasks of remote sensing of the ecological environment. Many studies have explored ecosystem structure information at specific spatial scales based on specific remote sensing data, but it is still rare to extract vegetation information at various landscape levels from a variety of remote sensing data. Based on Gaofen-1 satellite (GF-1) Wide-Field-View (WFV) data (16 m), Ziyuan-3 satellite (ZY-3) and airborne LiDAR data, this study comparatively analyzed the four levels of vegetation information by using the geographic object-based image analysis method (GEOBIA) on the typical natural secondary forest in Northeast China. The four levels of vegetation information include vegetation/non-vegetation (L1), vegetation type (L2), forest type (L3) and canopy and canopy gap (L4). The results showed that vegetation height and density provided by airborne LiDAR data could extract vegetation features and categories more effectively than the spectral information provided by GF-1 and ZY-3 images. Only 0.5 m LiDAR data can extract four levels of vegetation information (L1–L4); and from L1 to L4, the total accuracy of the classification decreased orderly 98%, 93%, 80% and 69%. Comparing with 2.1 m ZY-3, the total classification accuracy of L1, L2 and L3 extracted by 2.1 m LiDAR data increased by 3%, 17% and 43%, respectively. At the vegetation/non-vegetation level, the spatial resolution of data plays a leading role, and the data types used at the vegetation type and forest type level become the main influencing factors. This study will provide reference for data selection and mapping strategies for hierarchical multi-scale vegetation type extraction.

scholarly journals Assessing Potential Safety Zone Suitability Using a New Online Mapping Tool

Fire ◽  
2022 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Michael J. Campbell ◽  
Philip E. Dennison ◽  
Matthew P. Thompson ◽  
Bret W. Butler
Keyword(s):  
Burn Injury ◽  
Wildland Fire ◽  
Satellite Image ◽  
Safety Information ◽  
Separation Distance ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Lidar Data ◽  
Landsat 8 ◽  
Vegetation Height

Safety zones (SZs) are critical tools that can be used by wildland firefighters to avoid injury or fatality when engaging a fire. Effective SZs provide safe separation distance (SSD) from surrounding flames, ensuring that a fire’s heat cannot cause burn injury to firefighters within the SZ. Evaluating SSD on the ground can be challenging, and underestimating SSD can be fatal. We introduce a new online tool for mapping SSD based on vegetation height, terrain slope, wind speed, and burning condition: the Safe Separation Distance Evaluator (SSDE). It allows users to draw a potential SZ polygon and estimate SSD and the extent to which that SZ polygon may be suitable, given the local landscape, weather, and fire conditions. We begin by describing the algorithm that underlies SSDE. Given the importance of vegetation height for assessing SSD, we then describe an analysis that compares LANDFIRE Existing Vegetation Height and a recent Global Ecosystem Dynamics Investigation (GEDI) and Landsat 8 Operational Land Imager (OLI) satellite image-driven forest height dataset to vegetation heights derived from airborne lidar data in three areas of the Western US. This analysis revealed that both LANDFIRE and GEDI/Landsat tended to underestimate vegetation heights, which translates into an underestimation of SSD. To rectify this underestimation, we performed a bias-correction procedure that adjusted vegetation heights to more closely resemble those of the lidar data. SSDE is a tool that can provide valuable safety information to wildland fire personnel who are charged with the critical responsibility of protecting the public and landscapes from increasingly intense and frequent fires in a changing climate. However, as it is based on data that possess inherent uncertainty, it is essential that all SZ polygons evaluated using SSDE are validated on the ground prior to use.

scholarly journals Crossing the Great Divide: Bridging the Researcher–Practitioner Gap to Maximize the Utility of Remote Sensing for Invasive Species Monitoring and Management

2021 ◽  
Vol 13 (20) ◽  
pp. 4142
Author(s):  
Kelsey Parker ◽  
Arthur Elmes ◽  
Peter Boucher ◽  
Richard A. Hallett ◽  
John E. Thompson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Invasive Species ◽  
Forest Ecosystems ◽  
Forest Health ◽  
Hemlock Woolly Adelgid ◽  
Adelges Tsugae ◽  
Forest Pest ◽  
Field Surveys ◽  
Health Decline ◽  
Symbiotic Mutualism

Invasive species are increasingly present in our ecosystems and pose a threat to the health of forest ecosystems. Practitioners are tasked with locating these invasive species and finding ways to mitigate their spread and impacts, often through costly field surveys. Meanwhile, researchers are developing remote sensing products to detect the changes in vegetation health and structure that are caused by invasive species, which could aid in early detection and monitoring efforts. Although both groups are working towards similar goals and field data are essential for validating RS products, these groups often work independently. In this paper, we, a group of researchers and practitioners, discuss the challenges to bridging the gap between researchers and practitioners and summarize the literature on this topic. We also draw from our experiences collaborating with each other to advance detection, monitoring, and management of the Hemlock Woolly Adelgid (Adelges tsugae; HWA), an invasive forest pest in the eastern U.S. We conclude by (1) highlighting the synergies and symbiotic mutualism of researcher–practitioner collaborations and (2) providing a framework for facilitating researcher–practitioner collaborations that advance fundamental science while maximizing the capacity of RS technologies in monitoring and management of complex drivers of forest health decline such as invasive species.

scholarly journals Preliminary archaeological survey and remote-sensing of shell mounds at Kwokkunum, Albatross Bay, Cape York Peninsula, Australia

2020 ◽  
Vol 23 ◽  
pp. 9-24 ◽  
Author(s):  
Joshua Emmitt ◽  
Kasey Allely ◽  
Benjamin Davies ◽  
Eloise Hoffman ◽  
Simon J. Holdaway
Keyword(s):  
Remote Sensing ◽  
Airborne Lidar ◽  
Initial Assessment ◽  
Lidar Data ◽  
Archaeological Survey ◽  
Archaeological Record ◽  
Short Period ◽  
Shell Mounds ◽  
Cape York ◽  
Airborne Lidar Data

Shell mounds are a prominent part of the Cape York Peninsula archaeological record. A short period of fieldwork allowed initial assessment of their presence, size, and shape in the Kwokkunum region, Albatross Bay. Shell mounds found in this area vary in size with some examples amongst the largest found in the Cape York Peninsula. Comparison of terrestrial and airborne LiDAR data suggests that shell mounds in areas like Kwokkunum may be identified remotely where mound slopes exceed 5–10°. However, vegetation provides significant challenges for shell mound recording and vegetation on the mounds impacts on their form and preservation. Some of the challenges the largest mounds pose for investigation are reviewed.

Forest plot volume estimation using National Forest Inventory, Forest Type Map and Airborne LiDAR data

2012 ◽  
Vol 8 (2) ◽  
pp. 89-98 ◽  
Author(s):  
Taejin Park ◽  
Woo-Kyun Lee ◽  
Jong-Yeol Lee ◽  
Woo-Hyuk Byun ◽  
Doo-Ahn Kwak ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Forest Type ◽  
National Forest Inventory ◽  
Volume Estimation ◽  
Airborne Lidar ◽  
Forest Plot ◽  
Lidar Data ◽  
National Forest ◽  
Airborne Lidar Data

Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images

2012 ◽  
Vol 7 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Yan Huang ◽  
Bailang Yu ◽  
Jianhua Zhou ◽  
Chunlin Hu ◽  
Wenqi Tan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Remote Sensing Images ◽  
Urban Green ◽  
Automatic Estimation ◽  
Airborne Lidar Data ◽  
Urban Green Volume
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