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Special Issue "Applications of Remote Sensing to Forestry"

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A special issue of Forests (ISSN 1999-4907).

Deadline for manuscript submissions: closed (31 March 2014)

Special Issue Editors

Guest Editor
Prof. Dr. Randolph H. Wynne (Website)

Department of Forest Resources and Environmental Conservation, Virginia Tech, Cheatham Hall, RM 319, 310 West Campus Dr, Blacksburg, VA 24061, USA
Phone: 540-231-7811
Interests: applications of remote sensing to forestry; natural resource management; ecological modeling; and earth system science
Co-Guest Editor
Dr. Valerie Thomas

Department of Forest Resources and Environmental Conservation, Virginia Tech, Cheatham Hall, RM 307A, 310 West Campus Dr, Blacksburg, VA 24061, USA
Interests: remote sensing of forest structure and function; hyperspectral and lidar remote sensing; forest physiology; forest ecosystem science

Special Issue Information

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 1000 CHF (Swiss Francs).

Published Papers (9 papers)

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Research

Open AccessArticle Local-Scale Mapping of Biomass in Tropical Lowland Pine Savannas Using ALOS PALSAR
Forests 2014, 5(9), 2377-2399; doi:10.3390/f5092377
Received: 3 April 2014 / Revised: 8 September 2014 / Accepted: 22 September 2014 / Published: 25 September 2014
Cited by 1 | PDF Full-text (3847 KB) | HTML Full-text | XML Full-text
Abstract
Fine-scale biomass maps offer forest managers the prospect of more detailed and locally accurate information for measuring, reporting and verification activities in contexts, such as sustainable forest management, carbon stock assessments and ecological studies of forest growth and change. In this study, [...] Read more.
Fine-scale biomass maps offer forest managers the prospect of more detailed and locally accurate information for measuring, reporting and verification activities in contexts, such as sustainable forest management, carbon stock assessments and ecological studies of forest growth and change. In this study, we apply a locally validated method for estimating aboveground woody biomass (AGWB) from Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) data to produce an AGWB map for the lowland pine savannas of Belize at a spatial resolution of 100 m. Over 90% of these woodlands are predicted to have an AGWB below 60 tha−1, with the average woody biomass of these savannas estimated at 23.5 tha−1. By overlaying these spatial estimates upon previous thematic mapping of national land cover, we derive representative average biomass values of ~32 tha−1 and ~18 tha−1 for the previously qualitative classes of “denser” and “less dense” tree savannas. The predicted average biomass, from the mapping for savannas woodlands occurring within two of Belize’s larger protected areas, agree closely with previous biomass estimates for these areas based on ground surveys and forest inventories (error ≤20%). However, biomass estimates derived for these protected areas from two biomass maps produced at coarser resolutions (500 m and 1000 m) from global datasets overestimated biomass (errors ≥275% in each dataset). The finer scale biomass mapping of both protected and unprotected areas provides evidence to suggest that protection has a positive effect upon woody biomass, with the mean AGWB higher in areas protected and managed for biodiversity (protected and passively managed (PRPM), 29.5 tha−1) compared to unprotected areas (UPR, 23.29 tha−1). These findings suggest that where sufficient ground data exists to build a reliable local relationship to radar backscatter, the more detailed biomass mapping that can be produced from ALOS and similar satellite data at resolutions of ~100 m provides more accurate and spatially detailed information that is more appropriate for supporting the management of forested areas of ~10,000 ha than biomass maps that can be produced from lower resolution, but freely available global data sets. Full article
(This article belongs to the Special Issue Applications of Remote Sensing to Forestry)
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Open AccessArticle Large Area Mapping of Boreal Growing Stock Volume on an Annual and Multi-Temporal Level Using PALSAR L-Band Backscatter Mosaics
Forests 2014, 5(8), 1999-2015; doi:10.3390/f5081999
Received: 26 March 2014 / Revised: 23 July 2014 / Accepted: 8 August 2014 / Published: 20 August 2014
Cited by 1 | PDF Full-text (26309 KB) | HTML Full-text | XML Full-text
Abstract
The forests of the Russian Taiga can be described as an enormous biomass and carbon reservoir. Therefore, they are of utmost importance for the global carbon cycle. Large-area forest inventories in these mostly remote regions are associated with logistical problems and high [...] Read more.
The forests of the Russian Taiga can be described as an enormous biomass and carbon reservoir. Therefore, they are of utmost importance for the global carbon cycle. Large-area forest inventories in these mostly remote regions are associated with logistical problems and high financial efforts. Remotely-sensed data from satellite platforms may have the capability to provide such huge amounts of information. This study presents an application-oriented approach to derive aboveground growing stock volume (GSV) maps using the annual large-area L-band backscatter mosaics provided by the Japan Aerospace Exploration Agency (JAXA). Furthermore, a multi-temporal map has been created to improve GSV estimation accuracy. Based on information from Russian forest inventory data, the maps were generated using the machine learning algorithm, RandomForest. The results showed the high potential of this method for an operational, large-scale and high-resolution biomass estimation over boreal forests. An RMSE from 55.2 to 63.3 m3/ha could be obtained for the annual maps. Using the multi-temporal approach, the error could be slightly reduced to 54.4 m3/ha. Full article
(This article belongs to the Special Issue Applications of Remote Sensing to Forestry)
Open AccessArticle Optical Medium Spatial Resolution Satellite Constellation Data for Monitoring Woodland in the UK
Forests 2014, 5(7), 1798-1814; doi:10.3390/f5071798
Received: 7 March 2014 / Revised: 23 June 2014 / Accepted: 2 July 2014 / Published: 23 July 2014
PDF Full-text (1890 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this study was to test the potential of a constellation of remote sensing satellites, the Disaster Monitoring Constellation (DMC), for retrieving a temporal record of forest leaf area index (LAI) in the United Kingdom (U.K.). Ground-based LAI measurements were [...] Read more.
The aim of this study was to test the potential of a constellation of remote sensing satellites, the Disaster Monitoring Constellation (DMC), for retrieving a temporal record of forest leaf area index (LAI) in the United Kingdom (U.K.). Ground-based LAI measurements were made over a 12-month period in broadleaf woodland at Risley Moss Nature Reserve, Lancashire, U.K. The ground-based LAI varied between zero in January to a maximum of 4.5 in July. Nine DMC images, combining data from UK-DMC and NigeriaSat-1, were acquired, and all images were cross-calibrated and atmospherically corrected. The spectral reflectance of the test site was extracted, and a range of vegetation indices were then computed and correlated with the ground measurements of LAI. The soil adjusted vegetation index (SAVI) had the strongest correlation, and this was used to derive independent estimates of LAI using the “leave-one-out” method. The root mean square error of the LAI estimates was 0.47, which was close to that calculated for the ground-measured LAI. This study shows, for the first time, that data from a constellation of high temporal, medium spatial resolution optical satellite sensors may be used to map seasonal variation in woodland canopy leaf area index (LAI) in cloud-prone areas, like the U.K. Full article
(This article belongs to the Special Issue Applications of Remote Sensing to Forestry)
Open AccessCommunication Outlook for the Next Generation’s Precision Forestry in Finland
Forests 2014, 5(7), 1682-1694; doi:10.3390/f5071682
Received: 9 April 2014 / Revised: 30 May 2014 / Accepted: 2 July 2014 / Published: 15 July 2014
Cited by 14 | PDF Full-text (416 KB) | HTML Full-text | XML Full-text
Abstract
During the past decade in forest mapping and monitoring applications, the ability to acquire spatially accurate, 3D remote-sensing information by means of laser scanning, digital stereo imagery and radar imagery has been a major turning point. These 3D data sets that use [...] Read more.
During the past decade in forest mapping and monitoring applications, the ability to acquire spatially accurate, 3D remote-sensing information by means of laser scanning, digital stereo imagery and radar imagery has been a major turning point. These 3D data sets that use single- or multi-temporal point clouds enable a wide range of applications when combined with other geoinformation and logging machine-measured data. New technologies enable precision forestry, which can be defined as a method to accurately determine characteristics of forests and treatments at stand, sub-stand or individual tree level. In precision forestry, even individual tree-level assessments can be used for simulation and optimization models of the forest management decision support system. At the moment, the forest industry in Finland is looking forward to next generation’s forest inventory techniques to improve the current wood procurement practices. Our vision is that in the future, the data solution for detailed forest management and wood procurement will be to use multi-source and -sensor information. In this communication, we review our recent findings and describe our future vision in precision forestry research in Finland. Full article
(This article belongs to the Special Issue Applications of Remote Sensing to Forestry)
Open AccessArticle Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm
Forests 2014, 5(7), 1635-1652; doi:10.3390/f5071635
Received: 1 April 2014 / Revised: 15 June 2014 / Accepted: 2 July 2014 / Published: 11 July 2014
Cited by 3 | PDF Full-text (1786 KB) | HTML Full-text | XML Full-text
Abstract
Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm have been used successfully. We applied both methods for the mapping of organic carbon (Corg) [...] Read more.
Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm have been used successfully. We applied both methods for the mapping of organic carbon (Corg) in riparian forests due to their considerably high carbon storage capacity. Despite the importance of floodplains for carbon sequestration, a sufficient scientific foundation for creating large-scale maps showing the spatial Corg distribution is still missing. We estimated organic carbon in a test site in the Danube Floodplain based on RapidEye remote sensing data and additional geodata. Accordingly, carbon distribution maps of vegetation, soil, and total Corg stocks were derived. Results were compared and statistically evaluated with terrestrial survey data for outcomes with pure remote sensing data and for the combination with additional geodata using bias and the Root Mean Square Error (RMSE). Results show that SOM and kNN approaches enable us to reproduce spatial patterns of riparian forest Corg stocks. While vegetation Corg has very high RMSEs, outcomes for soil and total Corg stocks are less biased with a lower RMSE, especially when remote sensing and additional geodata are conjointly applied. SOMs show similar percentages of RMSE to kNN estimations. Full article
(This article belongs to the Special Issue Applications of Remote Sensing to Forestry)
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Open AccessArticle Mapping Forest Biomass Using Remote Sensing and National Forest Inventory in China
Forests 2014, 5(6), 1267-1283; doi:10.3390/f5061267
Received: 21 January 2014 / Revised: 19 April 2014 / Accepted: 29 May 2014 / Published: 11 June 2014
Cited by 8 | PDF Full-text (2068 KB) | HTML Full-text | XML Full-text
Abstract
Quantifying the spatial pattern of large-scale forest biomass can provide a general picture of the carbon stocks within a region and is of great scientific and political importance. The combination of the advantages of remote sensing data and field survey data can [...] Read more.
Quantifying the spatial pattern of large-scale forest biomass can provide a general picture of the carbon stocks within a region and is of great scientific and political importance. The combination of the advantages of remote sensing data and field survey data can reduce uncertainty as well as demonstrate the spatial distribution of forest biomass. In this study, the seventh national forest inventory statistics (for the period 2004–2008) and the spatially explicit MODIS Land Cover Type product (MCD12C1) were used together to quantitatively estimate the spatially-explicit distribution of forest biomass in China (with a resolution of 0.05°, ~5600 m). Our study demonstrated that the calibrated forest cover proportion maps allow proportionate downscaling of regional forest biomass statistics to forest cover pixels to produce a relatively fine-resolution biomass map. The total stock of forest biomass in China was 11.9 Pg with an average of 76.3 Mg ha−1 during the study period; the high values were located in mountain ranges in northeast, southwest and southeast China and were strongly correlated with forest age and forest density. Full article
(This article belongs to the Special Issue Applications of Remote Sensing to Forestry)
Open AccessArticle Forest Cover Database Updates Using Multi-Seasonal RapidEye Data—Storm Event Assessment in the Bavarian Forest National Park
Forests 2014, 5(6), 1284-1303; doi:10.3390/f5061284
Received: 26 February 2014 / Revised: 19 May 2014 / Accepted: 29 May 2014 / Published: 11 June 2014
Cited by 2 | PDF Full-text (3525 KB) | HTML Full-text | XML Full-text
Abstract
This study is a part of a research program that investigates the potential of RapidEye (RE) satellite data for timely updates of forest cover databases to reflect both regular management activities and sudden changes due to bark beetle and storms. Applied here [...] Read more.
This study is a part of a research program that investigates the potential of RapidEye (RE) satellite data for timely updates of forest cover databases to reflect both regular management activities and sudden changes due to bark beetle and storms. Applied here in the Bavarian Forest National Park (BFNP) in southeastern Germany, this approach detected even small changes between two data takes, thus, facilitating documentation of regular management activities. In the case of a sudden event, forest cover databases also serve as a baseline for damage assessment. A storm event, which occurred on 13 July, 2011, provided the opportunity to assess the effectiveness of multi-seasonal RE data for rapid damage assessment. Images of sufficient quality (<20% cloud cover) acquired one day before the storm event were used as a baseline. Persistent cloud cover meant that the first “after event” image of sufficient quality was acquired six weeks later, on 22 August, 2011. Aerial images (AI) for the official damage assessment done by the BFNP administration were acquired on that same day. The RE analysis for damage assessment was completed two weeks after the post-event data take with an overall accuracy of 96% and a kappa coefficient of 0.86. In contrast, the official aerial image survey from the BFNP was first released in late November, eleven weeks later. Comparison of the results from the two analyses showed a difference in the detected amount of forest cover loss of only 3%. The estimated cost of the RE approach was four times less than that of the standard digital AI procedure employed by the BFNP. Full article
(This article belongs to the Special Issue Applications of Remote Sensing to Forestry)
Open AccessArticle Evaluation and Comparison of QuickBird and ADS40-SH52 Multispectral Imagery for Mapping Iberian Wild Pear Trees (Pyrus bourgaeana, Decne) in a Mediterranean Mixed Forest
Forests 2014, 5(6), 1304-1330; doi:10.3390/f5061304
Received: 9 January 2014 / Revised: 15 April 2014 / Accepted: 29 May 2014 / Published: 11 June 2014
Cited by 3 | PDF Full-text (2182 KB) | HTML Full-text | XML Full-text
Abstract
The availability of images with very high spatial and spectral resolution from airborne sensors or those aboard satellites is opening new possibilities for the analysis of fine-scale vegetation, such as the identification and classification of individual tree species. To evaluate the potential [...] Read more.
The availability of images with very high spatial and spectral resolution from airborne sensors or those aboard satellites is opening new possibilities for the analysis of fine-scale vegetation, such as the identification and classification of individual tree species. To evaluate the potential of these images, a study was carried out to compare the spatial, spectral and temporal resolution between QuickBird and ADS40-SH52 imagery, in order to discriminate and identify, within the mixed Mediterranean forest, individuals of the Iberian wild pear (Pyrus bourgaeana). This is a typical species of the Mediterranean forest, but its biology and ecology are still poorly known. The images were subjected to different correction processes and data were homogenized. Vegetation classes and individual trees were identified on the images, which were classified from two types of supervised classification (Maximum Likelihood and Support Vector Machines) on a pixel-by-pixel basis. The classification values were satisfactory. The classifiers were compared, and Support Vector Machines was the algorithm that provided the best results in terms of overall accuracy. The QuickBird image showed higher overall accuracy (86.16%) when the Support Vector Machines algorithm was applied. In addition, individuals of Iberian wild pear were discriminated with probability of over 55%, when the Maximum Likelihood algorithm was applied. From the perspective of improving the sampling effort, these results are a starting point for facilitating research on the abundance, distribution and spatial structure of P. bourgaeana at different scales, in order to quantify the conservation status of this species. Full article
(This article belongs to the Special Issue Applications of Remote Sensing to Forestry)
Open AccessArticle High NDVI and Potential Canopy Photosynthesis of South American Subtropical Forests despite Seasonal Changes in Leaf Area Index and Air Temperature
Forests 2014, 5(2), 287-308; doi:10.3390/f5020287
Received: 14 December 2013 / Revised: 16 February 2014 / Accepted: 17 February 2014 / Published: 20 February 2014
Cited by 4 | PDF Full-text (1210 KB) | HTML Full-text | XML Full-text
Abstract
The canopy photosynthesis and carbon balance of the subtropical forests are not well studied compared to temperate and tropical forest ecosystems. The main objective of this study was to assess the seasonal dynamics of Normalized Difference Vegetation Index (NDVI) and potential canopy [...] Read more.
The canopy photosynthesis and carbon balance of the subtropical forests are not well studied compared to temperate and tropical forest ecosystems. The main objective of this study was to assess the seasonal dynamics of Normalized Difference Vegetation Index (NDVI) and potential canopy photosynthesis in relation to seasonal changes in leaf area index (LAI), chlorophyll concentration, and air temperatures of NE Argentina subtropical forests throughout the year. We included in the analysis several tree plantations (Pinus, Eucalyptus and Araucaria species) that are known to have high productivity. Field studies in native forests and tree plantations were conducted; stem growth rates, LAI and leaf chlorophyll concentration were measured. MODIS satellite-derived LAI (1 km SIN Grid) and NDVI (250m SIN Grid) from February 2000 to 2012 were used as a proxy of seasonal dynamics of potential photosynthetic activity at the stand level. The remote sensing LAI of the subtropical forests decreased every year from 6 to 5 during the cold season, similar to field LAI measurements, when temperatures were 10 °C lower than during the summer. The yearly maximum NDVI values were observed during a few months in autumn and spring (March through May and November, respectively) because high and low air temperatures may have a small detrimental effect on photosynthetic activity during both the warm and the cold seasons. Leaf chlorophyll concentration was higher during the cold season than the warm season which may have a compensatory effect on the seasonal variation of the NDVI values. The NDVI of the subtropical forest stands remained high and fairly constant throughout the year (the intra-annual coefficient of variation was 1.9%), and were comparable to the values of high-yield tree plantations. These results suggest that the humid subtropical forests in NE Argentina potentially could maintain high canopy photosynthetic activity throughout the year and thus this ecosystem may be a large carbon sink. Full article
(This article belongs to the Special Issue Applications of Remote Sensing to Forestry)

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