Astrape: A System for Mapping Severe Abiotic Forest Disturbances Using High Spatial Resolution Satellite Imagery and Unsupervised Classification

Wegmueller, Sarah A.; Townsend, Philip A.

doi:10.3390/rs13091634

Cited by 4 publications

(4 citation statements)

References 55 publications

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“…Sentinel-2, World-View, and other satellite systems are extensively used to monitor forest health around the world [51][52][53][54], and CubeSat satellites such as the Planet Labs constellation now generate near-daily three meter spatial resolution imagery. Planet Labs data are used to capture forest flowering events [55] and disturbance events from fires to tornados [56,57], but sub-meter spatial resolution imagery is still needed to confidently monitor individual trees, and persistent cloud cover can be an issue. A revealing example of this is in East Kaua'i, where there are multi-year gaps in the availability of cloud-free World-View satellite imagery.…”

Section: Confidence Ratings and Laboratory Sample Resultsmentioning

confidence: 99%

Spatial Patterns of ‘Ōhi‘a Mortality Associated with Rapid ‘Ōhi‘a Death and Ungulate Presence

Perroy

Sullivan

Benitez

et al. 2021

Forests

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Effective forest management, particularly during forest disturbance events, requires timely and accurate monitoring information at appropriate spatial scales. In Hawai‘i, widespread ‘ōhi‘a (Metrosideros polymorpha Gaud.) mortality associated with introduced fungal pathogens affects forest stands across the archipelago, further impacting native ecosystems already under threat from invasive species. Here, we share results from an integrated monitoring program based on high resolution (<5 cm) aerial imagery, field sampling, and confirmatory laboratory testing to detect and monitor ‘ōhi‘a mortality at the individual tree level across four representative sites on Hawai‘i island. We developed a custom imaging system for helicopter operations to map thousands of hectares (ha) per flight, a more useful scale than the ten to hundreds of ha typically covered using small, unoccupied aerial systems. Based on collected imagery, we developed a rating system of canopy condition to identify ‘ōhi‘a trees suspected of infection by the fungal pathogens responsible for rapid ‘ōhi‘a death (ROD); we used this system to quickly generate and share suspect tree candidate locations with partner agencies to rapidly detect new mortality outbreaks and prioritize field sampling efforts. In three of the four sites, 98% of laboratory samples collected from suspect trees assigned a high confidence rating (n = 50) and 89% of those assigned a medium confidence rating (n = 117) returned positive detections for the fungal pathogens responsible for ROD. The fourth site, which has a history of unexplained ‘ōhi‘a mortality, exhibited much lower positive detection rates: only 6% of sampled trees assigned a high confidence rating (n = 16) and 0% of the sampled suspect trees assigned a medium confidence rating (n = 20) were found to be positive for the pathogen. The disparity in positive detection rates across study sites illustrates challenges to definitively determine the cause of ‘ōhi‘a mortality from aerial imagery alone. Spatial patterns of ROD-associated ‘ōhi‘a mortality were strongly affected by ungulate presence or absence as measured by the density of suspected ROD trees in fenced (i.e., ungulate-free) and unfenced (i.e., ungulate present) areas. Suspected ROD tree densities in neighboring areas containing ungulates were two to 69 times greater than those found in ungulate-free zones. In one study site, a fence line breach occurred during the study period, and feral ungulates entered an area that was previously ungulate-free. Following the breach, suspect ROD tree densities in this area rose from 0.02 to 2.78 suspect trees/ha, highlighting the need for ungulate control to protect ‘ōhi‘a stands from Ceratocystis-induced mortality and repeat monitoring to detect forest changes and resource threats.

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Section: Confidence Ratings and Laboratory Sample Resultsmentioning

confidence: 99%

Spatial Patterns of ‘Ōhi‘a Mortality Associated with Rapid ‘Ōhi‘a Death and Ungulate Presence

Perroy

Sullivan

Benitez

et al. 2021

Forests

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“…NB, also known as Jenks Natural Breaks Method, is widely used in GIS software [75] and remote sensing studies [76][77][78]. We used NB to obtain an initial separation of rasters into two classes, after which we analyzed the threshold values (break values) and noticed a small degree of variation between 0.56-0.64 (with five out of seven values close to 0.6).…”

Section: Methodsmentioning

confidence: 99%

Monitoring Forest Cover Dynamics Using Orthophotos and Satellite Imagery

et al. 2023

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The assessment of changes in forest coverage is crucial for managing protected forest areas, particularly in the face of climate change. This study monitored forest cover dynamics in a 6535 ha mountain area located in north-west Romania as part of the Apuseni Natural Park from 2003 to 2019. Two approaches were used: vectorization from orthophotos and Google Earth images (in 2003, 2005, 2009, 2012, 2014, 2016, 2017, and 2019) and satellite imagery (Landsat 5 TM, 7 ETM, and 8 OLI) pre-processed to Surface Reflectance (SR) format from the same years. We employed four standard classifiers: Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM), and three combined methods: Linear Spectral Unmixing (LSU) with Natural Breaks (NB), Otsu Method (OM) and SVM, to extract and classify forest areas. Our study had two objectives: 1) to accurately assess changes in forest cover over a 17-year period and 2) to determine the most efficient methods for extracting and classifying forest areas. We validated the results using performance metrics that quantify both thematic and spatial accuracy. Our results indicate a 9% loss of forest cover in the study area, representing 577 ha with an average decrease ratio of 33.9 ha/year−1. Of all the methods used, SVM produced the best results (with an average score of 88% for Overall Quality (OQ)), followed by RF (with a mean value of 86% for OQ).

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“…Degradation of forests due to biotic (insects, fungi, viruses, weeds, animals) and abiotic (floods, fires, storms, droughts, polluted atmospheres etc.) damages are becoming more frequent and intense today (Wegmueller and Townsend 2021). Also, human damage (anthropogenic impact) is numerous and varied.…”

Section: Automatic Forest Degradation Monitoring By Remote Sensing Me...mentioning

confidence: 99%

Automatic Forest Degradation Monitoring by Remote Sensing Methods and Copernicus Data

Gašparović

Klobučar²,

Gašparović³

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Nowadays, forests are the most widely spread land cover and therefore play a significant role in ecology and create processes’ dynamics. Forests are threatened by various harmful effects due to biotic (insects, fungi, viruses, weeds, animals) and abiotic (floods, fires, storms, droughts, polluted atmospheres etc.) damages. Also, human damage (anthropogenic impact) is numerous and varied. They are caused by direct human action on the forest and indirect activities and processes (damage due to grazing, consequent devastation and erosion of habitats etc.). Forest devastation and illegal logging are one of the immediate negative human activities that have a detrimental impact on the forest. The research refers to the state forests of two management units, Javornik and Ćorkovača-Karlice in the border area of the Republic of Croatia. Part of the forests (about 3,000 hectares) of these two management units are located in a mine suspected area along the state border with Bosnia and Herzegovina. This research aims to develop an automatic algorithm for forest degradation monitoring by remote sensing methods and Copernicus data. The developed algorithm was based on the Sentinel-2 (S2) optical satellite imagery and Google Earth Engine. The proposed automatic forest degradation monitoring algorithm was based on the ΔNDVI change detection approach. Accuracy assessment was done by independent data in higher, 3-m resolution based PlanetScope imagery. Preliminary results show very similar forest degradation values per all tested forest compartment/subcompartment for automatically generated S2 10-m imagery forest degradation map and 3-m forest maps obtained manually from PlanetScope imagery.

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Astrape: A System for Mapping Severe Abiotic Forest Disturbances Using High Spatial Resolution Satellite Imagery and Unsupervised Classification

Cited by 4 publications

References 55 publications

Spatial Patterns of ‘Ōhi‘a Mortality Associated with Rapid ‘Ōhi‘a Death and Ungulate Presence

Spatial Patterns of ‘Ōhi‘a Mortality Associated with Rapid ‘Ōhi‘a Death and Ungulate Presence

Monitoring Forest Cover Dynamics Using Orthophotos and Satellite Imagery

Automatic Forest Degradation Monitoring by Remote Sensing Methods and Copernicus Data

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