The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis

Analysis of Dieback in a Coastal Pinewood in Campania, Southern Italy, through High-Resolution Remote Sensing

For some years, the stone pine (Pinus pinea L.) forests of the Domitian coast in Campania, Southern Italy, have been at risk of conservation due to biological adversities. Among these, the pine tortoise scale Toumeyella parvicornis (Cockerell) has assumed a primary role since its spread in Campania began. Observation of pine forests using remote sensing techniques was useful for acquiring information on the health state of the vegetation. In this way, it was possible to monitor the functioning of the forest ecosystem and identify the existence of critical states. To study the variation in spectral behavior and identify conditions of plant stress due to the action of pests, the analysis of the multispectral data of the Copernicus Sentinel-2 satellite, acquired over seven years between 2016 and 2022, was conducted on the Domitian pine forest. This method was used to plot the values of individual pixels over time by processing spectral indices using Geographic Information System (GIS) tools. The use of vegetation indices has made it possible to highlight the degradation suffered by the vegetation due to infestation by T. parvicornis. The results showed the utility of monitoring the state of the vegetation through high-resolution remote sensing to protect and preserve the pine forest ecosystem peculiar to the Domitian coast.

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

Forests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future.

Modelling hydrological strength and alteration in moribund deltaic India

The Ganga-Brahmaputra moribund deltaic floodplain region hosted many socio-ecologically precious freshwater wetland ecosystems experiencing hydrological alteration. The present study aimed to model hydrological strength (HS) to show the spatial difference and account for the degree and direction of hydrological alteration of Indian moribund deltaic wetland in three phases e.g. (1) phase I (1988-1997), (2) phase II (1998-2007) and phase III (2008-2017). Three key hydrological parameters, such as Water Presence Frequency (WPF), water depth, and hydro-period were considered for hydrological strength modelling using two ensemble Machine Learning (ML) techniques (Random Forest (RF) and XGBoost). Image algebra was employed for phasal change detection. Hydrological strength models show that around 75% of the wetland area was lost in-between phases I to III and the loss was found more intensive in moderate and weak HS zones. Existing wetland shows a clear spatial difference of HS between wetland core and periphery and river linked and delinked or not linked wetlands. Regarding the suitability of the ML models, both are acceptable, however, the XGBoost outperformed in reference to applied 15 statistical validation techniques and field evidence. HS models based on change detection clarified that more than 22% and 55% of the weak HS zone in phases II and III respectively were turned into non-wetland. The degree of alteration revealed that about 40% of wetland areas experienced a negative alteration during phases I to II, and this proportion increased to 63% in between phases II to III. Since the study figured out the spatial nature of HS, degree and direction of alteration at a spatial scale, these findings would be instrumental for adopting rational planning towards wetland conservation and restoration.

Strategic roadmap to assess forest vulnerability under air pollution and climate change

Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly, most studies investigating the effects of air pollutants on forests have generally neglected the impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend (1) the establishment of much denser array of monitoring sites, particularly in the South Hemisphere; (2) further integration of ground and satellite monitoring; (3) generation of flux-based standards and critical levels taking into account the sensitivity of dominant forest tree species; (4) long-term monitoring of N, S, P cycles and base cations deposition together at global scale; (5) intensification of experimental studies, addressing the combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~73,000 tree species on Earth; (6) more experimental focus on phenomics and genomics; (7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and (8) development of models integrating air pollution and climate change data from long-term monitoring programs.

UAV-Based Forest Health Monitoring: A Systematic Review

In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.

Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review

Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data. The purpose of this review is to summarize recent contributions and to identify knowledge gaps in UAV remote sensing for FIPD monitoring. A systematic review was performed using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) protocol. We reviewed the full text of 49 studies published between 2015 and 2021. The parameters examined were the taxonomic characteristics, the type of UAV and sensor, data collection and pre-processing, processing and analytical methods, and software used. We found that the number of papers on this topic has increased in recent years, with most being studies located in China and Europe. The main FIPDs studied were pine wilt disease (PWD) and bark beetles (BB) using UAV multirotor architectures. Among the sensor types, multispectral and red–green–blue (RGB) bands were preferred for the monitoring tasks. Regarding the analytical methods, random forest (RF) and deep learning (DL) classifiers were the most frequently applied in UAV imagery processing. This paper discusses the advantages and limitations associated with the use of UAVs and the processing methods for FIPDs, and research gaps and challenges are presented.

Robust Satellite-Based Identification and Monitoring of Forests Having Undergone Climate-Change-Related Stress

Climate-induced drought events are responsible for forest decline and mortality in different areas of the world. Forest response to drought stress periods may be different, in time and space, depending on vegetation type and local factors. Stress analysis may be carried out by using field methods, but the use of remote sensing may be needed to highlight the effects of climate-change-induced phenomena at a larger spatial and temporal scale. In this context, satellite-based analyses are presented in this work to evaluate the drought effects during the 2000s and the possible climatological forcing over oak forests in Southern Italy. To this aim, two approaches based on the well-known Normalized Difference Vegetation Index (NDVI) were used: one based on NDVI values, averaged over selected decaying and non-decaying forests; another based on the Robust Satellite Techniques (RST). The analysis of the first approach mainly gave us overall information about 1984–2011 rising NDVI trends, despite a general decrease around the 2000s. The second, more refined approach was able to highlight a different drought stress impact over decaying and non-decaying forests. The combined use of the RST-based approach, Landsat satellite data, and Google Earth Engine (GEE) platform allowed us to identify in space domain and monitor over time significant oak forest changes and climate-driven effects (e.g., in 2001) from the local to the Basilicata region scale. By this way, the decaying status of the Gorgoglione forest was highlighted two years before the first visual field evidence (e.g., dryness of apical branches, bark detachment, root rot disease). The RST exportability to different satellite sensors and vegetation types, the availability of suitable satellite data, and the potential of GEE suggest the possibility of long-term monitoring of forest health, from the local to the global scale, to provide useful information to different end-user classes.

What Is the Effect of Quantitative Inversion of Photosynthetic Pigment Content in Populus euphratica Oliv. Individual Tree Canopy Based on Multispectral UAV Images?

It is highly necessary to apply unmanned aerial vehicle (UAV) remote sensing technology to forest health assessment. To prove the feasibility of quantitative inversion of photosynthetic pigment content (PPC) in Populus euphratica Oliv. individual tree canopy (PeITC) by using multispectral UAV images, in this study, Parrot Sequoia+ multispectral UAV system was manipulated to collect the images of Populus euphratica (Populus euphratica Oliv.) sample plots in Daliyabuyi Oasis from 2019 to 2020, and the canopy PPCs of five Populus euphratica sample trees per plot were determined in six plots. The Populus euphratica crown regions were extracted by grey wolf optimizer-OTSU (GWO-OTSU) multithreshold segmentation algorithm from the normalized difference vegetation index (NDVI) images of Populus euphratica sample plots obtained after preprocessing, and the PeITCs were segmented by multiresolution segmentation algorithm. The mean values of 27 spectral indices in the PeITCs were calculated in each plot, and the optimal model was constructed for quantitative estimation of the PPCs in the PeITCs, then the inversion results were compared and verified based on GF-6 and ZY1-02D satellite imageries respectively. The results were as follows. (1) The average value of canopy chlorophyll content (Chl) was 2.007 mg/g, the mean value of canopy carotenoid content (Car) was 0.703 mg/g. The coefficient of variation (C.V) of both were basically the same and they were both of strong variability. The measured PPCs of the PeITCs in Daliyabuyi Oasis was generally low. The average contents of chlorophyll and carotenoid in PeITC in June were more than twice those in August, while the mean ratio between them was significantly lower in June than in August. The measured PPCs had no obvious spatial distribution law. However, that could prove the rationality of sample selection in this study. (2) NDVI had the best effect of highlighting vegetation among all quadrats in the study area. Based on the GWO-OTSU multithreshold segmentation method, the canopy area of Populus euphratica could be quickly and effectively extracted from the quadrat NDVI map. The best segmentation effect of PeITCs was obtained based on a multiresolution segmentation method when the segmentation scale was 120, the shape index was 0.7, and the compactness index was 0.5. Compared with manual vectorization method of visual interpretation, the root mean square error (RMSE) and Pearson correlation coefficient (R) values of the mean NDVI values in PeITCs obtained by these two methods were 0.038 and 0.951. (3) Only 12 of the 27 spectral indices were significantly correlated with Chl and Car at the significance level of 0.02. Characteristics of the calibration set and validation set were basically consistent with those of the entire set. The classification and regression tree-decision tree (CART-DT) model performed best in the estimation of the PPCs in the PeITCs, in which, when estimating the Car, the calibration coefficient of determination (R2C) was 0.843, the calibration root mean square error (RMSEC) was 0.084, the calibration residual prediction deviation (RPDC) was 2.525, the validation coefficient of determination (R2V) was 0.670, the validation root mean square error (RMSEV) was 0.251, the validation residual prediction deviation (RPDV) was 1.741. (4) Qualitative comparison of spectral reflectance and NDVI values between GF-6 multispectral imagery and Parrot Sequoia+ multispectral image on the 172 PeITCs can show the reliability of Parrot Sequoia+ multispectral image. The comparison results of five PeITCs relative health degree judged by field vision judgment, measured SPAD value, predicted value of Chl (Chlpre), the red edge value calculated by ZY1-02D (ZY1-02Dred edge) and the Carotenoid Reflection Index 2 (CRI2) value calculated by ZY1-02D (ZY1-02DCRI2) can further prove the scientificity of inversion results to a certain extent. These results indicate that multispectral UAV images can be applied for quantitative inversion of PPC in PeITC, which could provide an indicator for the construction of a Populus euphratica individual tree health evaluation indicator system based on UAV remote sensing technology in the next step.

Remote Sensing Assessment of the Expansion of Ips typographus Attacks in the Chuprene Reserve, Western Balkan Range

The Chuprene Reserve was created in 1973 to preserve the natural coniferous forests in the Western Balkan Range in Bulgaria. The first infestations by European spruce bark beetle (Ips typographus) were registered in Norway spruce (Picea abies) stands in the mid-1980s. The aim of this study is to assess the damages caused by I. typographus in the Chuprene Reserve using remote sensing techniques – unmanned aerial vehicle (UAV) images, airborne images, and satellite images of European Space Imaging (EUSI), combined with terrestrial verification. High-resolution images in four bands of the electromagnetic spectrum and in a standard RGB channel were taken in 2017 via a multispectral camera ‘Parrot Sequoia’, integrated with a specialized professional UAV system eBee ‘Flying Wing’. The health status of Norway spruce stands in the reserve was assessed with the normalized difference vegetation index, based on the digital mixing of imagery captured in the red and near infrared range. The dynamic of bark beetle attacks was studied in GIS on the basis of maps generated from photographic surveys, airborne images taken in 2011 and 2015, and satellite images from 2020. In the UAV-captured area (314.0 ha), the size of Norway spruce stands attacked by I. typographus increased from 7.6 ha (2.4%) in 2011 to 44.9 ha (14.3%) in 2020. The satellite images showed that on the entire territory of the Chuprene Reserve (1451.9 ha), I. typographus killed spruce trees on 137.4 ha, which is 9.6% of the total area.