Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Forest Health Monitoring
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Forest Health Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (10 August 2019) | Viewed by 28036

Special Issue Editors

Prof. Dr. Moses Azong Cho

E-Mail Website
Guest Editor
1. Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa
2. Department of Plant and Soil Science, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria 0002, South Africa
Interests: remote sensing; precision agriculture; biodiversity conservations; data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Renaud Mathieu

E-Mail Website
Guest Editor
International Rice Research Institute Africa, Naivasha Road, Nairobi 72951, Kenya
Interests: synthetic aperture radar; LiDAR; forest structure; vegetation assessment and monitoring

Special Issue Information

Dear Colleagues,

Forest biomes and plantations provide important goods and services to the biosphere, industry, and are a source of livelihoods to millions of people. Forest degradation, defined generally as the decreasing capacity of a forest to provide goods and services, has become a widespread phenomenon. The causes of forest degradation can be attributed to factors that affect forest health, a measure of a forest’s capacity to provide good and services. Forest health attributes such as structure, composition, function and vigour (e.g. phenology or nutrient status) are constantly affected by biotic and abiotic agents and processes. Biotic processes include infestation by insects, plant parasites, fungi, weeds or invasive species and impact of large grazing/browsing animals. Abiotic processes are generally associated to forest disturbance or damage caused by weather (fire, wind, snow and hail) and climate (e.g. drought and global warming) events, changing soil condition (nutrient deficiency, waterlogging), chemical pollution (e.g. pesticides and atmospheric pollution) and changing land use.

Air/spaceborne remote sensing of forests provide a cost effective means of monitoring forest health. We would like to invite both applied and theoretical research contributions on the use of passive and active sensors including multispectral, hyperspectral, thermal, Radio Detection and Ranging (RADAR) and Light Detection and Ranging (LiDAR) in forest health monitoring. A multi-sensor/multiscale approach is particularly encouraged.

Topics of interest include:

  • Forest structural attributes including tree height, diameter-at-breast height and aboveground biomass
  • Forest species composition and diversity
  • Invasive species
  • Insect defoliation
  • Forest phenology and impact of climatic change on phenology
  • Forest biochemistry
  • Fungi infestation
  • Tree dieback
  • Impact of forest fire on health and damage by meteorological events, snow, wind, drought etc.
  • Deforestation, forest cover change and fragmentation at multiple spatial and temporal scales

Prof. Moses Azong Cho
Dr. Renaud Mathieu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Forest health
  • Hyperspectral remote sensing
  • Multispectral remote sensing
  • Synthetic aperture RADAR
  • LiDAR
  • Multi-sensor approach

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

26 pages, 9339 KiB  
Article
Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA
by John Hogland, Nathaniel Anderson, David L. R. Affleck and Joseph St. Peter
Remote Sens. 2019, 11(15), 1803; https://doi.org/10.3390/rs11151803 - 1 Aug 2019
Cited by 13 | Viewed by 4303
Abstract
This study improved on previous efforts to map longleaf pine (Pinus palustris) over large areas in the southeastern United States of America by developing new methods that integrate forest inventory data, aerial photography and Landsat 8 imagery to model forest characteristics. [...] Read more.
This study improved on previous efforts to map longleaf pine (Pinus palustris) over large areas in the southeastern United States of America by developing new methods that integrate forest inventory data, aerial photography and Landsat 8 imagery to model forest characteristics. Spatial, statistical and machine learning algorithms were used to relate United States Forest Service Forest Inventory and Analysis (FIA) field plot data to relatively normalized Landsat 8 imagery based texture. Modeling algorithms employed include softmax neural networks and multiple hurdle models that combine softmax neural network predictions with linear regression models to estimate key forest characteristics across 2.3 million ha in Georgia, USA. Forest metrics include forest type, basal area and stand density. Results show strong relationships between Landsat 8 imagery based texture and field data (map accuracy > 0.80; square root basal area per ha residual standard errors < 1; natural log transformed trees per ha < 1.081). Model estimates depicting spatially explicit, fine resolution raster surfaces of forest characteristics for multiple coniferous and deciduous species across the study area were created and made available to the public in an online raster database. These products can be integrated with existing tabular, vector and raster databases already being used to guide longleaf pine conservation and restoration in the region. Full article
(This article belongs to the Special Issue Forest Health Monitoring)
►▼ Show Figures

Graphical abstract

24 pages, 7117 KiB  
Article
Impact of Soil Reflectance Variation Correction on Woody Cover Estimation in Kruger National Park Using MODIS Data
by Sa’ad Ibrahim, Heiko Balzter, Kevin Tansey, Renaud Mathieu and Narumasa Tsutsumida
Remote Sens. 2019, 11(8), 898; https://doi.org/10.3390/rs11080898 - 12 Apr 2019
Cited by 21 | Viewed by 4699
Abstract
Time-series of imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) has previously been used to estimate woody and herbaceous vegetation cover in savannas. However, this is challenging due to the mixture of woody and herbaceous plant functional types with specific contributions to [...] Read more.
Time-series of imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) has previously been used to estimate woody and herbaceous vegetation cover in savannas. However, this is challenging due to the mixture of woody and herbaceous plant functional types with specific contributions to the phenological signal and variations in soil background reflectance signatures observed from satellite. These factors cause variations in the accuracy and precision of woody cover estimates from different modelling approaches and datasets. Here, woody cover is estimated over Kruger National Park (KNP) from the MODIS 16-day composite time-series data using dry season NDVI/SAVI images and applying NDVIsoil determination methods. The woody cover estimates when NDVIsoil was ignored had R2 = 0.40, p < 0.01, slope = 1.01, RMSE (root mean square error) = 15.26% and R2 = 0.32, p < 0.03, slope = 0.79, RMSE = 16.39% for NDVIpixel and SAVIpixel, respectively, when compared to field plot data of plant functional type fractional cover. The woody cover estimated from the soil determination methods had a slope closer to 1 for both NDVI and SAVI but also a slightly higher RMSE. For a soil-invariant method, RMSE = 19.04% and RMSE = 17.34% were observed for NDVI and SAVI respectively, while for a soil-variant method, RMSE = 18.28% and RMSE = 19.17% were found for NDVI and SAVI. The woody cover estimated from all models had a high correlation and significant relationship with LiDAR/SAR based estimates and a woody cover map produced by Bucini. Woody cover maps are required for vegetation succession monitoring, grazing impact assessment, climate change mitigation and adaptation research and dynamic vegetation model validation. Full article
(This article belongs to the Special Issue Forest Health Monitoring)
►▼ Show Figures

Graphical abstract

23 pages, 8137 KiB  
Article
Long-Term Monitoring of Cork and Holm Oak Stands Productivity in Portugal with Landsat Imagery
by Valentine Aubard, Joana Amaral Paulo and João M. N. Silva
Remote Sens. 2019, 11(5), 525; https://doi.org/10.3390/rs11050525 - 4 Mar 2019
Cited by 30 | Viewed by 5972
Abstract
Oak stands are declining in many regions of southern Europe. The goal of this paper is to assess this process and develop an effective monitoring tool for research and management. Long-term trends of the Normalized Difference Vegetation Index (NDVI) were derived and mapped [...] Read more.
Oak stands are declining in many regions of southern Europe. The goal of this paper is to assess this process and develop an effective monitoring tool for research and management. Long-term trends of the Normalized Difference Vegetation Index (NDVI) were derived and mapped at 30-m spatial resolution for all areas with a stable land cover of cork oak (Quercus suber L.) and holm oak (Quercus ilex L.) forests and agroforestry systems in mainland Portugal. NDVI, a good proxy for forest health and productivity monitoring, was obtained for the 1984–2017 period using Landsat-5 TM and Landsat-7 ETM+ imagery. TM values were adjusted to those of ETM+, after a comparison of site-specific and literature linear equations. The spatiotemporal trend analysis was performed using only July and August NDVI values, in order to minimize the spectral contribution of understory vegetation and its phenological variability, and thus, focus on the tree layer. Signs and significance of trends were obtained for six representative oak stands and the whole country with the Mann Kendall and Contextual Mann-Kendall test, respectively, and their slope was assessed with the Theil-Sen estimator. Long-term forest inventories of six study sites and NDVI time series derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) allowed validating the methodology and results with independent data. NDVI has a good relationship with cork production at the forest stand level. Pettitt tests reveal significant change-points within the trends in the period 1996–2005, when changes in drought patterns occurred. Twelve percent of the area of oak stands in Portugal presents significant decreasing trends, most of them located in mountainous regions with shallow soils. Cork oak agroforestry is the most declining oak forest type, compared to cork oak and holm oak forests. The Google Earth Engine platform proved to be a powerful tool to deal with long-term time series and for the monitoring of forests health and productivity. Full article
(This article belongs to the Special Issue Forest Health Monitoring)
►▼ Show Figures

Graphical abstract

22 pages, 3853 KiB  
Article
Sensitivity of Landsat-8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus, L.) Infestation
by Haidi Abdullah, Roshanak Darvishzadeh, Andrew K. Skidmore and Marco Heurich
Remote Sens. 2019, 11(4), 398; https://doi.org/10.3390/rs11040398 - 15 Feb 2019
Cited by 46 | Viewed by 6688
Abstract
In this study, the early stage of European spruce bark beetle (Ips typographus, L.) infestation (so-called green attack) is investigated using Landsat-8 optical and thermal data. We conducted an extensive field survey in June and the beginning of July 2016, to [...] Read more.
In this study, the early stage of European spruce bark beetle (Ips typographus, L.) infestation (so-called green attack) is investigated using Landsat-8 optical and thermal data. We conducted an extensive field survey in June and the beginning of July 2016, to collect field data measurements from several infested and healthy trees in the Bavarian Forest National Park (BFNP), Germany. In total, 157 trees were selected, and leaf traits (i.e. stomatal conductance, chlorophyll fluorescence, and water content) were measured. Three Landsat-8 images from May, July, and August 2016 were studied, representing an early stage, advanced stage, and post-infestation, respectively. Spectral vegetation indices (SVIs) sensitive to the measured traits were calculated from the optical domain (VIS, NIR, and SWIR), and canopy surface temperature (CST) was calculated from the thermal infrared band using the mono-window algorithm. The leaf traits were used to examine the impact of bark beetle infestation on the infested trees and to explore the link between these traits and remote sensing data (CST and SVIs). The differences between healthy and infested samples regarding measured leaf traits were assessed using Student’s t test. The relative importance of the CST and SVIs for estimating measured leaf traits was evaluated based on the variable importance in projection (VIP) obtained from the partial least squares regression (PLSR) analysis. A temporal comparison was then made for SVIs with a VIP > 1, including CST, using statistical significance tests. The clustering method using a principal components analysis (PCA) was used to examine visually how well the two groups of sample plots (healthy and infested) are separated in 2-D space based on principal component scores. Finally, linear regression (LR) was used to generate the leaf traits maps using the SVI that have highest VIP score and then used to produce a stress map for the study area. The results revealed that all measured leaf traits were significantly different (p < 0.05) between healthy versus infested samples. Moreover, the study showed that CST was superior to the SVIs in detecting subtle canopy changes due to bark beetle infestation for the three months considered in this study. The results showed that CST is an essential variable for estimating measured leaf traits with VIP > 1, improving the results of clustering when used with other SVIs. Likewise, the stress map produced by CST and leaf traits well presented the infestation areas at the green attacked stage. The new insight offered by this study is that the stress induced by the early stage of bark beetle infestation is more pronounced by Landsat-8 thermal bands than the SVIs calculated from its optical bands. The potential of CST in detecting the green attack stage would have positive implications for forest practice. Full article
(This article belongs to the Special Issue Forest Health Monitoring)
►▼ Show Figures

Graphical abstract

16 pages, 2384 KiB  
Article
Using Sentinel-2 Multispectral Images to Map the Occurrence of the Cossid Moth (Coryphodema tristis) in Eucalyptus Nitens Plantations of Mpumalanga, South Africa
by Samuel Takudzwa Kumbula, Paramu Mafongoya, Kabir Yunus Peerbhay, Romano Trent Lottering and Riyad Ismail
Remote Sens. 2019, 11(3), 278; https://doi.org/10.3390/rs11030278 - 31 Jan 2019
Cited by 20 | Viewed by 5660
Abstract
Coryphodema tristis is a wood-boring insect, indigenous to South Africa, that has recently been identified as an emerging pest feeding on Eucalyptus nitens, resulting in extensive damage and economic loss. Eucalyptus plantations contributes over 9% to the total exported manufactured goods of [...] Read more.
Coryphodema tristis is a wood-boring insect, indigenous to South Africa, that has recently been identified as an emerging pest feeding on Eucalyptus nitens, resulting in extensive damage and economic loss. Eucalyptus plantations contributes over 9% to the total exported manufactured goods of South Africa which contributes significantly to the gross domestic product. Currently, the distribution extent of the Coryphodema tristis is unknown and estimated to infest Eucalyptus nitens compartments from less than 1% to nearly 80%, which is certainly a concern for the forestry sector related to the quantity and quality of yield produced. Therefore, the study sought to model the probability of occurrence of Coryphodema tristis on Eucalyptus nitens plantations in Mpumalanga, South Africa, using data from the Sentinel-2 multispectral instrument (MSI). Traditional field surveys were carried out through mass trapping in all compartments (n = 878) of Eucalyptus nitens plantations. Only 371 Eucalyptus nitens compartments were positively identified as infested and were used to generate the Coryphodema tristis presence data. Presence data and spectral features from the area were analysed using the Maxent algorithm. Model performance was evaluated using the receiver operating characteristics (ROC) curve showing the area under the curve (AUC) and True Skill Statistic (TSS) while the performance of predictors was analysed with the jack-knife. Validation of results were conducted using the test data. Using only the occurrence data and Sentinel-2 bands and derived vegetation indices, the Maxent model provided successful results, exhibiting an area under the curve (AUC) of 0.890. The Photosynthetic vigour ratio, Band 5 (Red edge 1), Band 4 (Red), Green NDVI hyper, Band 3 (Green) and Band 12 (SWIR 2) were identified as the most influential predictor variables. Results of this study suggest that remotely sensed derived vegetation indices from cost-effective platforms could play a crucial role in supporting forest pest management strategies and infestation control. Full article
(This article belongs to the Special Issue Forest Health Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop