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1. Improving ICESat ‐2‐based boreal forest height estimation by a multivariate sample quality control approach

   https://doi.org/10.1111/2041-210X.14112  

   Zhang, Tianqi ; Liu, Desheng ( April 2023 , Methods in Ecology and Evolution)

   Boreal forest heights are associated with global carbon stocks and energy budgets. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA's Ice, Cloud and Land Elevation Satellite (ICESat‐2) enables canopy vertical structure measurement at a global scale. However, with a photon‐counting laser system, ICESat‐2 contains high uncertainties in the estimated canopy heights, requiring appropriate quality control before being applied to canopy height modelling.

   We adopted a multivariate quality control approach (i.e. the Cook's distance) to improve the quality of ICESat‐2 samples. The controlled ICESat‐2 data were then input as the response variable for predicting boreal forest heights based on spatially continuous satellite data and machine learning (ML) regression models. The examined ML regressors include artificial neural networks (ANN), gradient boosting machine (GBM), random forest (RF) and support vector regression (SVR).

   The proposed quality control effectively removes low‐quality ICESat‐2 samples and enhances the correlations between ICESat‐2 and airborne laser scanning (ALS) observations. Moreover, the controlled ICESat‐2 samples help achieve a trade‐off between sample quality and quantity for all ML regressors, generating close canopy heights to ALS‐derived counterparts. Overall, RF and GBM make better canopy height predictions than ANN and SVR. Of all explanatory variables, the normalized difference vegetation index calculated based on the first red edge band of Sentinel‐2 (NDVIredEdge1) is considered the most important.

   The proposed quality control on ICESat‐2 sample selection and canopy height model (CHM) development workflow will greatly benefit forest structure investigations in the Arctic community.

    

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2. Using Hyperspectral Imagery to Detect an Invasive Fungal Pathogen and Symptom Severity in Pinus strobiformis Seedlings of Different Genotypes

   https://doi.org/10.3390/rs12244041  

   Haagsma, Marja ; Page, Gerald F. ; Johnson, Jeremy S. ; Still, Christopher ; Waring, Kristen M. ; Sniezko, Richard A. ; Selker, John S. ( December 2020 , Remote Sensing)

   null (Ed.)

   Finding trees that are resistant to pathogens is key in preparing for current and future disease threats such as the invasive white pine blister rust. In this study, we analyzed the potential of using hyperspectral imaging to find and diagnose the degree of infection of the non-native white pine blister rust in southwestern white pine seedlings from different seed-source families. A support vector machine was able to automatically detect infection with a classification accuracy of 87% (κ = 0.75) over 16 image collection dates. Hyperspectral imaging only missed 4% of infected seedlings that were impacted in terms of vigor according to expert’s assessments. Classification accuracy per family was highly correlated with mortality rate within a family. Moreover, classifying seedlings into a ‘growth vigor’ grouping used to identify the degree of impact of the disease was possible with 79.7% (κ = 0.69) accuracy. We ranked hyperspectral features for their importance in both classification tasks using the following features: 84 vegetation indices, simple ratios, normalized difference indices, and first derivatives. The most informative features were identified using a ‘new search algorithm’ that combines both the p-value of a 2-sample t-test and the Bhattacharyya distance. We ranked the normalized photochemical reflectance index (PRIn) first for infection detection. This index also had the highest classification accuracy (83.6%). Indices such as PRIn use only a small subset of the reflectance bands. This could be used for future developments of less expensive and more data-parsimonious multispectral cameras. 

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3. Field-Validated Burn-Severity Mapping in North Patagonian Forests

   https://doi.org/10.3390/rs12020214  

   Franco, María Guadalupe ; Mundo, Ignacio A. ; Veblen, Thomas T. ( January 2020 , Remote Sensing)

   Burn severity, which can be reliably estimated by validated spectral indices, is a key element for understanding ecosystem dynamics and informing management strategies. However, in North Patagonian forests, where wildfires are a major disturbance agent, studies aimed at the field validation of spectral indices of burn severity are scarce. The aim of this work was to develop a field validated methodology for burn-severity mapping by studying two large fires that burned in the summer of 2013–2014 in forests of Araucaria araucana and other tree species. We explored the relation between widely used spectral indices and a field burn-severity index, and we evaluated index performance by examining index sensitivity in discriminating burn-severity classes in different vegetation types. For those indices that proved to be suitable, we adjusted the class thresholds and constructed confusion matrices to assess their accuracy. Burn severity maps of the studied fires were generated using the two most accurate methods and were compared to evaluate their level of agreement. Our results confirm that reliable burn severity estimates can be derived from spectral indices for these forests. Two severity indices, the delta normalized burn ratio (dNBR) and delta normalized difference vegetation index (dNDVI), were highly related to the fire-induced changes observed in the field, but the strength of these associations varied across the five different vegetation types defined by tree heights and tree and tall shrub species regeneration strategies. The thresholds proposed in this study for these indices generated classifications with global accuracies of 82% and Kappa indices of 70%. Both the dNBR and dNDVI classification approaches were more accurate in detecting high severity, but to a lesser degree for detecting low severity burns. Moderate severity was poorly classified, with producer and user errors reaching 50%. These constraints, along with detected differences in separability, need to be considered when interpreting burn severity maps generated using these methods. 

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4. Biophysical drivers for predicting the distribution and abundance of invasive yellow sweet clover in the Northern Great Plains

   Saraf, Sakshi ( March 2023 , Landscape ecology)

   Jianguo (Ed.)

   Yellow sweetclover (Melilotus officinalis; YSC) is an invasive biennial legume that bloomed across the Northern Great Plains in 2018–2019 in response to above-average precipitation. YSC can increase nitrogen (N) levels and potentially cause substantial changes in the composition of native plant species communities. There is little knowledge of the spatiotemporal variability and conditions causing substantial widespread blooms of YSC across western South Dakota (SD). We aimed to develop a generalized prediction model to predict the relative abundance of YSC in suitable habitats across rangelands of western South Dakota for 2019. Our research questions are: (1) What is the spatial extent of YSC across western South Dakota? (2) Which model can accurately predict the habitat and percent cover of YSC? and (3) What significant biophysical drivers affect its presence across western South Dakota? We trained machine learning models with in situ data (2016–2021), Sentinel 2A-derived surface reflectance and indices (10 m, 20 m) and site-specific variables of climate, topography, and edaphic factors to optimize model performance. We identified moisture proxies (Shortwave Infrared reflectance and variability in Tasseled Cap Wetness) as the important predictors to explain the YSC presence. Land Surface Water Index and variability in summer temperature were the top predictors in explaining the YSC abundance. We demonstrated how machine learning algorithms could help generate valuable information on the spatial distribution of this invasive plant. We delineated major YSC hotspots in Butte, Pennington, and Corson Counties of South Dakota. The floodplains of major rivers, including White and Bad Rivers, and areas around Badlands National Park also showed a higher occurrence probability and cover percentage. These prediction maps could aid land managers in devising management strategies for the regions that are prone to YSC outbreaks. The management workflow can also serve as a prototype for mapping other invasive plant species in similar regions. 

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5. Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors

   https://doi.org/10.3390/drones4030056  

   Atkins, Jeff W. ; Stovall, Atticus E. ; Yang, Xi ( September 2020 , Drones)

   null (Ed.)

   Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity. 

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