Integrating Remote Sensing and Machine Learning to Assess Forest Health and Susceptibility to Pest-induced Damage

Spruce budworm (Choristoneura fumiferana; SBW) outbreaks are cyclically occurring phenomena in the northeastern USA and neighboring Canadian provinces. These outbreaks are often of landscape level causing impaired growth and mortality of the host species namely spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Acknowledging the recent SBW outbreak in Canadian provinces like Quebec and New Brunswick neighboring the state of Maine, our study devised comprehensive techniques to assess the susceptibility of Maine forests to SBW attack. This study aims to harness the power of remote sensing data and machine learning algorithms to model and map the susceptibility of forest in terms of host species availability and abundance (basal area per hectare; BAPH, and leaf area index; LAI), their maturity and the defense mechanism prevalent. In terms of host species abundance mapping our study explores the integration of satellite remote sensing data to model BAPH and LAI of two economically vital SBW host species, red spruce (Picea rubens Sarg.) and balsam fir, in Maine USA. Combining Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral, and site variables, we used Random Forest (RF) and Multi-Layer Perceptron (MLP) algorithms for modeling LAI and BAPH. The results demonstrated the superiority of RF over MLP, achieving smaller normalized root mean square error (nRMSE) by 0.01 and 0.06 for LAI and BAPH, respectively. Notably, Sentinel-2 variables, especially the red-edge spectral vegetation indices, played a significant role in both LAI and BAPH estimation, with the minor inclusion of site variables, particularly elevation. In addition, using various satellite remote sensing data such as Sentinel-1 C-band SAR, PALSAR L-band SAR and Sentinel-2 multispectral, along with site variables, the study developed large-scale SBW stand impact types and susceptibility maps for the entire state of Maine. The susceptibility of the forest was assessed based on the availability of SBW host species and their maturity. Integrating machine-learning algorithms, RF and MLP, the best model, utilizing site (elevation and aspect) and Sentinel-2 data achieved an overall accuracy of 83.4% to predict SBW host species. Furthermore, combining the host species data with age data from Land Change Monitoring, Assessment, and Projection (LCMAP) products we could produce the SBW susceptibility map based on stand impact types with an overall accuracy of 88.3%. Moreover, the work builds upon the assessment of susceptibility of SBW host species taking into account the concentration of several canopy traits using remote sensing and site data. The study focused on various foliar traits affecting insect herbivory, including nutritive such as nitrogen (N), phosphorous (P), potassium (K), and copper (Cu), non-nutritive such as iron (Fe) and calcium (Ca), and defensive parameters such as equivalent water thickness (EWT) and leaf mass per area (LMA). Using Sentinel-2 and site data, we developed trait estimation models using machine-learning algorithms like Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM). The accuracy of the developed model was evaluated based on the normalized root mean square error (nRMSE). Based on the model performances, we selected XGB algorithm to estimate Ca, EWT, Fe, and K whereas Cu, LMA, N, and P were estimated using RF algorithm. Regarding the variables used, almost all the best performing models included Sentinel-2 red-edge indices and depth to water table (DWT) as the most important variables. Ultimately, the study proposed a novel framework connecting the concentrations of foliar traits in SBW host foliage to tree susceptibility to the pest, enabling the assessment of host susceptibility on a landscape level. To sum up, this study highlights the advantages and effectiveness of integrating satellite remote sensing data for enhanced pest management, providing valuable insights into tree attributes and susceptibility to spruce budworm outbreaks in Northeast USA. The findings offer essential tools for forest stakeholders to improve management strategies and mitigate potential forthcoming SBW outbreaks in the region

Quantifying mangrove leaf area index from Sentinel-2 imagery using hybrid models and active learning

Environmental Biolog

Reviewing the potential of Sentinel-2 in assessing the drought

This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated

Hyperspectral leaf area index and chlorophyll retrieval over forest and row-structured vineyard canopies

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation

Hyperspectral leaf area index and chlorophyll retrieval over forest and row-structured vineyard canopies

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation

Towards scalable estimation of plant functional diversity from Sentinel-2: in-situ validation in a heterogeneous (semi-)natural landscape

Environmental BiologyConservation Biolog

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

A new generation of sensors and monitoring tools to support climate-smart forestry practices

Climate-smart forestry (CSF) is an emerging branch of sustainable adaptive forest management aimed at enhancing the potential of forests to adapt to and mitigate climate change. It relies on much higher data requirements than traditional forestry. These data requirements can be met by new devices that support continuous, in situ monitoring of forest conditions in real time. We propose a comprehensive network of sensors, i.e., a wireless sensor network (WSN), that can be part of a worldwide network of interconnected uniquely addressable objects, an Internet of Things (IoT), which can make data available in near real time to multiple stakeholders, including scientists, foresters, and forest managers, and may partially motivate citizens to participate in big data collection. The use of in situ sources of monitoring data as ground-truthed training data for remotely sensed data can boost forest monitoring by increasing the spatial and temporal scales of the monitoring, leading to a better understanding of forest processes and potential threats. Here, some of the key developments and applications of these sensors are outlined, together with guidelines for data management. Examples are given of their deployment to detect early warning signals (EWS) of ecosystem regime shifts in terms of forest productivity, health, and biodiversity. Analysis of the strategic use of these tools highlights the opportunities for engaging citizens and forest managers in this new generation of forest monitoring.Peer reviewe