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Forests
Special Issues
Mapping Forest Health Using Moderate Resolution Satellites
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Mapping Forest Health Using Moderate Resolution Satellites

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (1 April 2019)

Special Issue Editors

Dr. Zhiqiang Yang

E-Mail Website
Guest Editor
Oregon State University, Department of Forest Ecosystem & Society, Corvallis, OR 97331, USA
Interests: ecological modeling with remote sensing for landscape and regional ecological information; forest succession; ecological informatics and application development for ecological researches; linking time series of spectral data with different successional trajectories; spatial modeling of ecosystem production through BiomBGC modeling; developing methods to quantify uncertainties associated with remote sensing analysis
Dr. Zhe Zhu

E-Mail Website
Guest Editor
Department of Geosciences, Texas Tech University, Lubbock, TX, USA
Interests: remote sensing of forest, clouds, urban, and land cover/land use; time series analysis; change detection; climate change

Special Issue Information

Dear Colleagues,

The health and vitality of global forests are being affected by both abiotic and biotic stressors, e.g., insect and disease, invasive species, drought and other factors, such as climate change. These stressors affect, not only ecosystem functions, but also the economic loss of forest lands. For this Special Issue, we invite papers to address the mapping and quantifying forest health using remote sensing approach. The series of Landsat satellites provide the longest records for mapping forest health conditions, and their moderate resolution is ideal for quantifying forest health conditions. For this special issue, we welcome studies focusing on using Landsat, as well as other moderate resolution satellites, to map forest health from regional to global scales. Development and application using big data and cloud computing approach for time series analysis of forest health are especially welcome.

Dr. Zhiqiang Yang
Dr. Zhe Zhu
Guest Editors

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. Forests is an international peer-reviewed open access monthly 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 2600 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

  • mapping
  • forest health
  • Landsat
  • moderate resolution
  • cloud computing

Published Papers (3 papers)

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Research

14 pages, 7702 KiB  
Article
Assessing the Trade-Offs of SPOT7 Imagery for Monitoring Natural Forest Canopy Intactness
by Astika Bhugeloo, Kabir Peerbhay, Syd Ramdhani and Sershen
Forests 2018, 9(12), 781; https://doi.org/10.3390/f9120781 - 18 Dec 2018
Cited by 2 | Viewed by 3070
Abstract
Natural and human-induced disturbances influence the biodiversity and functionality of forest ecosystems. Regular, repeated assessments of canopy intactness are essential to map site-specific forest disturbance and recovery patterns, an essential requirement for forest monitoring and management. However, accessibility to images required for this [...] Read more.
Natural and human-induced disturbances influence the biodiversity and functionality of forest ecosystems. Regular, repeated assessments of canopy intactness are essential to map site-specific forest disturbance and recovery patterns, an essential requirement for forest monitoring and management. However, accessibility to images required for this practice, uncertainty around the levels of accuracy achieved with images of different resolution, and the affordability of the practice challenges its application in many developing regions. This study aimed to compare the accuracy of forest gap detection (in subtropical forests) achieved with lower-resolution (SPOT7 5 m) and higher-resolution (SPOT7 1.5 m) pan-sharpened imagery. Additionally, the Normalised Difference Vegetation Index (NDVI) and Synthetic Aperture Radar (SAR) were compared in terms of their ability to increase the accuracy of this detection when used in conjunction with both high and low resolution imagery. Results indicate that the SPOT7 1.5 m imagery produced an overall accuracy of 77.78% and a ϰ coefficient of 0.66 compared with the 69.44% accuracy and the 0.59 ϰ coefficient achieved with the SPOT7 5 m imagery. Computing image texture analysis within the Random Forest classifier (RF) framework increased classification accuracies to 75.00% for the SPOT 5 m and 86.11% for the SPOT7 1.5 m imagery, validating the usefulness of texture analysis. Variable importance was used to identify wavebands and texture-derived variables that were the most effective in discriminating canopy gaps from intact canopy. In this regard, near infrared, NDVI, SAR, contrast, mean, entropy and second moment were the most important. Collectively the results indicate that the approach adopted in this study, i.e., the use of SPOT7 1.5 m imagery in conjunction with image texture analysis and variable importance, can be used to accurately discriminate between canopy gaps and intact canopy, making it a cost-effective spatial approach for monitoring and managing natural forests. Full article
(This article belongs to the Special Issue Mapping Forest Health Using Moderate Resolution Satellites)
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14 pages, 8095 KiB  
Article
Mapping Progression and Severity of a Southern Colorado Spruce Beetle Outbreak Using Calibrated Image Composites
by Brian David Woodward, Paul Harrison Evangelista and Anthony Grant Vorster
Forests 2018, 9(6), 336; https://doi.org/10.3390/f9060336 - 07 Jun 2018
Cited by 5 | Viewed by 3473
Abstract
An ongoing spruce beetle (Dendroctonus rufipennis Kirby.) epidemic in southern Colorado has resulted in the death of thousands of acres of forests primarily dominated by Engelmann spruce (Picea engelmannii Parry.). To evaluate the ecological and economic impacts of this massive mortality [...] Read more.
An ongoing spruce beetle (Dendroctonus rufipennis Kirby.) epidemic in southern Colorado has resulted in the death of thousands of acres of forests primarily dominated by Engelmann spruce (Picea engelmannii Parry.). To evaluate the ecological and economic impacts of this massive mortality event, researchers and land managers need to efficiently track its progression, spread, and severity across large spatial extents. In this study, mortality severity (0–100% dead) was successfully mapped at the Landsat pixel scale (30 × 30 m) across a large (5000 km2), persistently cloud-covered study area using multi-sensor (Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI)) harmonized tasseled cap image composites as spectral predictors of gray stage spruce beetle mortality. Our maps display the distribution and severity of this landscape-scale mortality event in 2011 (R2 = 0.48, root mean squared error (RMSE) = 7.7) and 2015 (R2 = 0.55, RMSE = 11.6). Potential applications of this study include efficient landscape-scale forest health monitoring, targeted forest and timber management, and assessment of ecological impacts of bark beetle outbreaks. Full article
(This article belongs to the Special Issue Mapping Forest Health Using Moderate Resolution Satellites)
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16 pages, 3981 KiB  
Article
Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series
by Paulo J. Murillo-Sandoval, Thomas Hilker, Meg A. Krawchuk and Jamon Van Den Hoek
Forests 2018, 9(5), 269; https://doi.org/10.3390/f9050269 - 15 May 2018
Cited by 26 | Viewed by 5883
Abstract
The Colombian Andes foothills have seen an expansion of forest disturbance since the 1950s. While understanding the drivers of disturbance is important for quantifying the implications of land use change on regional biodiversity, methods for attributing disturbance to specific drivers of change at [...] Read more.
The Colombian Andes foothills have seen an expansion of forest disturbance since the 1950s. While understanding the drivers of disturbance is important for quantifying the implications of land use change on regional biodiversity, methods for attributing disturbance to specific drivers of change at a high temporal and spatial resolution are still lacking in the Andes region, in part due to persistent cloud cover. Using 20 years of Landsat images (1996–2015) covering Picachos National Park in the Colombian Andes, we detected sub-annual forest cover disturbances using the Breaks For Additive Season and Trend (BFAST) Monitor algorithm; characterized different types of disturbance using spectral, spatial, and topographic indicators; and attributed causes of forest disturbance such as conversion to pasture, conversion to agriculture, and non-stand replacing disturbance (i.e., thinning) using a Random Forest (RF) classifier. Conversion to pasture has been the main driver of forest disturbance in Picachos, responsible for 11,395 ± 72 ha (17%) of forest cover loss, followed by non-stand replacing disturbance and conversion to agriculture. Disturbance detection had 96% overall agreement with validation data, although we had a high omission error of 21% primarily associated with forest to agriculture conversion. Other change drivers had a much more reliable attribution with forest to pasture conversion or non-stand-replacing disturbance, showing only 1–5% commission and 2–14% omission errors. Our results provide spatially-explicit information on sub-annual disturbances and associated drivers of change that are necessary for evaluating and improving domestic conservation efforts and establishing systematic ecological observations, which is currently absent from Colombia. While effective at revealing forest change dynamics in a geographically remote and socio-politically complex region like Picachos, our approach is highly automated and it can be easily extended to the rest of Andes-Amazon transition belt where low availability of remote sensing data and high cloud cover impede efforts at consistent monitoring of forest cover change dynamics and drivers. Full article
(This article belongs to the Special Issue Mapping Forest Health Using Moderate Resolution Satellites)
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