Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review

Bark beetle outbreaks can result in a devastating impact on forest ecosystem processes, biodiversity, forest structure and function, and economies. Accurate and timely detection of bark beetle infestations is crucial to mitigate further damage, develop proactive forest management activities, and minimize economic losses. Incorporating remote sensing (RS) data with machine learning (ML) (or deep learning (DL)) can provide a great alternative to the current approaches that rely on aerial surveys and field surveys, which are impractical over vast geographical regions. This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three key perspectives: bark beetle & host interactions, RS, and ML/DL. We parse recent literature according to bark beetle species & attack phases, host trees, study regions, imagery platforms & sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices (SVIs), ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks & architectures. This review focuses on challenging early detection, discussing current challenges and potential solutions. Our literature survey suggests that the performance of current ML methods is limited (less than 80%) and depends on various factors, including imagery sensors & resolutions, acquisition dates, and employed features & algorithms/networks. A more promising result from DL networks and then the random forest (RF) algorithm highlighted the potential to detect subtle changes in visible, thermal, and short-wave infrared (SWIR) spectral regions.Comment: Under review, 33 pages, 5 figures, 8 Table

Responding to Large-Scale Forest Damage in an Alpine Environment with Remote Sensing, Machine Learning, and Web-GIS

This paper reports a semi-automated workflow for detection and quantification of forest damage from windthrow in an Alpine region, in particular from the Vaia storm in October 2018. A web-GIS platform allows to select the damaged area by drawing polygons; several vegetation indices (VIs) are automatically calculated using remote sensing data (Sentinel-2A) and tested to identify the more suitable ones for quantifying forest damage using cross-validation with ground-truth data. Results show that the mean value of NDVI and NDMI decreased in the damaged areas, and have a strong negative correlation with severity. RGI has an opposite behavior in contrast with NDVI and NDMI, as it highlights the red component of the land surface. In all cases, variance of the VI increases after the event between 0.03 and 0.15. Understorey not damaged from the windthrow, if consisting of 40% or more of the total cover in the area, undermines significantly the sensibility of the VIs to detecting and predicting severity. Using aggregational statistics (average and standard deviation) of VIs over polygons as input to a machine learning algorithm, i.e., Random Forest, results in severity prediction with regression reaching a root mean square error (RMSE) of 9.96, on a severity scale of 0–100, using an ensemble of area averages and standard deviations of NDVI, NDMI, and RGI indices. The results show that combining more than one VI can significantly improve the estimation of severity, and web-GIS tools can support decisions with selected VIs. The reported results prove that Sentinel-2 imagery can be deployed and analysed via web-tools to estimate forest damage severity and that VIs can be used via machine learning for predicting severity of damage, with careful evaluation of the effect of understorey in each situation

Mediterranean forest resilience to drought and climate change

Enhancing resilience to climate change is a key management goal for Mediterranean ecosystems. Typically, these management plans are based on ecological knowledge of species’ tolerances derived from local studies limited in time and space. Remote sensing provides opportunities to study resilience over larger scales, but the tools needed to quantify the resilience of forests to drought and evaluate the effectiveness of management plans remain limited. This thesis examines how freely available satellite data can be used to quantify changes in forest canopies in response to climate variability. Using a combination of time-series and break-point analyses of satellite imagery I resolve limitations in forest resilience estimation and show that, for Spanish woodlands, the relative water availability during and following drought events are important in driving the canopy greenness loss and recovery. I show that despite increasing aridity, and examples of localised die-back events, Spanish forests are mostly becoming denser, with only 12% of locations analysed declining in greenness over the 18-year study period. This work demonstrates the importance of large-scale remote sensing analyses for obtaining an objective perspective on drought impacts. The thesis then explores the potential of remote sensing to map tree species in a region of regenerating woodlands near Madrid, providing the information needed for a nuanced understanding of resilience. I found that tree classification using high-resolution airborne hyperspectral imagery was highly accurate, while species maps produced using Sentinel 2 imagery (multispectral data with 10-m spatial resolution) were less successful at identifying species, with average agreement of 64% with the airborne derived map. Following on from this work, I used areas with high classification agreement between the airborne and spaceborne species information to study the effect of species composition on forest responses to droughts. I identify contrasting responses of the canopy greenness and wood production to drought. Specifically, wood production was found to be more sensitive to changes in water availability than canopy greenness. For the oak species, wood production was mirrored by changes in canopy greenness, but black pines reduced their wood production during droughts without substantial reduction in canopy greenness. I investigate the differences between the species and the mixing effects further by studying foliar compositions during a dry summer in Spain. There were strong differences between pines and oaks in the stable isotope ratios of carbon, probably driven by underlying differences in water-use efficiency, and differences in the stable isotope ratios of nitrogen, probably driven by underlying differences in species’ investments in the photosynthetic apparatus. I conclude by highlighting the implications of my research for studying the relationships between diversity and ecosystem functioning from space.PhD scholarship from Cambridge International Trus

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

Mapping grass nutrient phosphorus (P) and sodium (NA) across different grass communities using Sentinel-2 data

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirement for the degree of Master of Science (Environmental Sciences) at the School of Geography, Archaeology & Environmental Studies March 2017Accurate estimates and mapping of grass quality is important for effective rangeland management. The purpose of this research was to map different grass species as well as nutrient Phosphorus (P) and Sodium (Na) concentration across grass communities using Sentinel-2 imagery in Telperion game reserve. The main objectives of the study were to: map the most common grass communities at the Telperion game reserve using Sentinel-2 imagery using artificial neural network (ANN) classifier and to evaluate the use of Sentinel-2 (MSI) in quantifying grass phosphorus and sodium concentration across different grass communities. Grass phosphorus and sodium concentrations were estimated using Random Forest (RF) regression algorithm, normalized difference vegetation index (NDVI) and the simple ratios (SR) which were calculated from all two possible band combination of Sentinel-2 data. Results obtained demonstrated woody vegetation as the dominant vegetation and Aristida congesta as the most common grass species. The overall classification accuracy = 81%; kappa =0.78 and error rate=0.18 was achieved using the ANN classifier. Regression model for leaf phosphorus concentration prediction both NDVI and SR data sets yielded similar results (R2 =0.363; RMSE=0.017%) and (R2 =0.36 2; RMSE=0.0174%). Regression model for leaf sodium using NDVI and SR data sets yielded dissimilar results (R2 =0.23; RMSE=16.74 mg/kg) and (R2 =0.15; RMSE =34.08 mg/kg). The overall outcomes of this study demonstrate the capability of Sentinel 2 imagery in mapping vegetation quality (phosphorus and sodium) and quantity. The study recommends the mapping of grass communities and both phosphorus and sodium concentrations across different seasons to fully understand the distribution of different species across the game reserve as well as variations in foliar concentration of the elements. Such information will guide the reserve managers on resource use and conservation strategies to implement within the reserve. Furthermore, the information will enable conservation managers to understand wildlife distribution and feeding patterns. This will allow integration of effective conservation strategies into decisions on stocking capacity.MT 201

Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems

P. 137-144The development of improved spatial and spectral resolution sensors provides new opportunities to assess burn severity more accurately. This study evaluates the ability of remote sensing indices derived from three remote sensing sensors (i.e., Landsat 8 OLI/TIRS, Sentinel-2 MSI and Deimos-1 SLIM-6-22) to assess burn severity (site, vegetation and soil burn severity). As a case study, we used a megafire (9,939 ha) that occurred in a Mediterranean ecosystem in northwestern Spain. Remote sensing indices included seven reflective, two thermal and four mixed indices, which were derived from each satellite and were validated with field burn severity metrics obtained from CBI index. Correlation patterns of field burn severity and remote sensing indices were relatively consistent across the different sensors. Additionally, regardless of the sensor, indices that incorporated SWIR bands (i.e., NBR-based indices), exceed those using red and NIR bands, and thermal and mixed indices. High resolution Sentinel-2 imagery only slightly improved the performance of indices based on NBR compared to Landsat 8. The dNDVI index from Landsat 8 and Sentinel-2 images showed relatively similar correlation values to NBR-based indices for site and soil burn severity, but showed limitations using Deimos-1. In general, mono-temporal and relativized indices better correlated with vegetation burn severity in heterogeneous systems than differenced indices. This study showed good potential for Landsat 8 OLI/TIRS and Sentinel-2 MSI for burn severity assessment in fire-prone heterogeneous ecosystems, although we highlight the need for further evaluation of Deimos-1 SLIM-6-22 in different fire scenarios, especially using bi-temporal indices.S