Special Issue "Advances in Remote Sensing of Forestry"
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A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (31 October 2012)
Special Issue Editor
Guest Editor
Prof. Dr. David L. Skole
Department of Forestry, Michigan State University, 1405 S. Harrison Road, East Lansing, MI 48823, USA
Website: http://www.goes.msu.edu/content.cfm?ID=14
E-Mail: skole@msu.edu
Interests: global carbon cycle; biophysical remote sensing; land cover change; deforestation; tropical forests; REDD+; carbon remote sensing
Special Issue Information
Dear Colleagues,
In recent years there has been substantial progress by the research community developing ways to detect land cover change in tropical forests with remote sensing. What was initially a focus on measuring the conversion of tropical forests to non-forest, recent advances have made it possible to increase the variety of disturbances that can be detected for closed tropical forests to include logging and understory fires. Thus, there are now methods available to remotely detect a full range of disturbance intensities, from outright clearing to low levels of degradation, over large areas. Yet in spite of this progress two important next steps are needed. The first is to expand the measurement and monitoring capabilities to open forest systems, such as savanna woodlands, and to develop the means to measure trees outside of forests in agricultural landscapes. The second is to apply the technical means to the deployment of measurement, reporting and verification systems (MRV) to support carbon and climate change policy
Papers in the special issue will move on from the starting point of basic closed tropical forests monitoring and focus on the use of a variety of sensors and spatial resolutions to monitor the full suite of landscapes necessary to support the emerging REDD+ programs. This will include applications from a range of sensors and scales, including optical, microwave, and LiDAR. The purpose of this monitoring approach is to measure continuous fields of land cover and assign biomass and carbon attributes to these data sets. It will require monitoring deforestation, degradation, reforestation, agroforestry and trees outside of forests at the landscape level. Select papers will describe various technical approaches to forest carbon tracking, as well as information systems that can be developed to support a range of carbon monitoring needs.
Prof. David Skole
Guest Editor
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. Remote Sensing 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 800 CHF (Swiss Francs).
Keywords
- tropical forests
- forest disturbance
- remote sensing
- REDD+
- Lidar
- reforestation
- agroforests
- savannas
- open forests
Published Papers (5 papers)
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Received: 3 November 2012; in revised form: 24 December 2012 / Accepted: 15 January 2013 / Published: 25 January 2013
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Abstract: This paper presents a new method for constructing quickly and automatically precision tree models from point clouds of the trunk and branches obtained by terrestrial laser scanning. The input of the method is a point cloud of a single tree scanned from multiple positions. The surface of the visible parts of the tree is robustly reconstructed by making a flexible cylinder model of the tree. The thorough quantitative model records also the topological branching structure. In this paper, every major step of the whole model reconstruction process, from the input to the finished model, is presented in detail. The model is constructed by a local approach in which the point cloud is covered with small sets corresponding to connected surface patches in the tree surface. The neighbor-relations and geometrical properties of these cover sets are used to reconstruct the details of the tree and, step by step, the whole tree. The point cloud and the sets are segmented into branches, after which the branches are modeled as collections of cylinders. From the model, the branching structure and size properties, such as volume and branch size distributions, for the whole tree or some of its parts, can be approximated. The approach is validated using both measured and modeled terrestrial laser scanner data from real trees and detailed 3D models. The results show that the method allows an easy extraction of various tree attributes from terrestrial or mobile laser scanning point clouds.
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Received: 12 December 2012; in revised form: 29 January 2013 / Accepted: 30 January 2013 / Published: 4 February 2013
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Abstract: Assessment of forest degradation has been emphasized as an important issue for emission calculations, but remote sensing based detecting of forest degradation is still in an early phase of development. The use of optical imagery for degradation assessment in the tropics is limited due to frequent cloud cover. Recent studies based on radar data often focus on classification approaches of 2D backscatter. In this study, we describe a method to detect areas affected by forest degradation from digital surface models derived from COSMO-SkyMed X-band Spotlight InSAR-Stereo Data. Two test sites with recent logging activities were chosen in Cameroon and in the Republic of Congo. Using the full resolution COSMO-SkyMed digital surface model and a 90-m resolution Shuttle Radar Topography Mission model or a mean filtered digital surface model we calculate difference models to detect canopy disturbances. The extracted disturbance gaps are aggregated to potential degradation areas and then evaluated with respect to reference areas extracted from RapidEye and Quickbird optical imagery. Results show overall accuracies above 75% for assessing degradation areas with the presented methods.
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Received: 20 December 2012; in revised form: 17 February 2013 / Accepted: 18 February 2013 / Published: 26 February 2013
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Abstract: Estimation of forest aboveground biomass (AGB) has become one of the main challenges of remote sensing science for global observation of carbon storage and changes in the past few decades. We examine the impact of plot size at different spatial resolutions, incidence angles, and polarizations on the forest biomass estimation using L-band polarimetric Synthetic Aperture Radar data acquired by NASA’s Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne system. Field inventory data from 32 1.0 ha plots (AGB < 200 Mg ha−1) in approximately even-aged forests in a temperate to boreal transitional region in the state of Maine were divided into subplots at four different spatial scales (0.0625 ha, 0.25 ha, 0.5 ha, and 1.0 ha) to quantify aboveground biomass variations. The results showed a large variability in aboveground biomass at smaller plot size (0.0625 ha). The variability decreased substantially at larger plot sizes (>0.5 ha), suggesting a stability of field-estimated biomass at scales of about 1.0 ha. UAVSAR backscatter was linked to the field estimates of aboveground biomass to develop parametric equations based on polarized returns to accurately map biomass over the entire radar image. Radar backscatter values at all three polarizations (HH, VV, HV) were positively correlated with field aboveground biomass at all four spatial scales, with the highest correlation at the 1.0 ha scale. Among polarizations, the cross-polarized HV had the highest sensitivity to field estimated aboveground biomass (R2 = 0.68). Algorithms were developed that combined three radar backscatter polarizations (HH, HV, and VV) to estimate aboveground biomass at the four spatial scales. The predicted aboveground biomass from these algorithms resulted in decreasing estimation error as the pixel size increased, with the best results at the 1 ha scale with an R2 of 0.67 (p < 0.0001), and an overall RMSE of 44 Mg·ha−1. For AGB < 150 Mg·ha−1, the error reduced to 23 Mg·ha−1 (±15%), suggesting an improved AGB prediction below the L-band sensitivity range to biomass. Results also showed larger bias in aboveground biomass estimation from radar at smaller scales that improved at larger spatial scales of 1.0 ha with underestimation of −3.62 Mg·ha−1 over the entire biomass range.
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Received: 20 December 2012; in revised form: 20 February 2013 / Accepted: 4 March 2013 / Published: 7 March 2013
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Abstract: The mapping of changes in the distribution of insect-caused forest damage remains an important forest monitoring application and challenge. Efficient and accurate methods are required for mapping and monitoring changes in insect defoliation to inform forest management and reporting activities. In this research, we develop and evaluate a LiDAR-driven (Light Detection And Ranging) approach for mapping defoliation caused by the Common pine sawfly (Diprion pini L.). Our method requires plot-level training data and airborne scanning LiDAR data. The approach is predicated on a forest canopy mask created by detecting forest canopy cover using LiDAR. The LiDAR returns that are reflected from the canopy (that is, returns > half of maximum plot tree height) are used in the prediction of the defoliation. Predictions of defoliation are made at plot-level, which enables a direct integration of the method to operational forest management planning while also providing additional value-added from inventory-focused LiDAR datasets. In addition to the method development, we evaluated the prediction accuracy and investigated the required pulse density for operational LiDAR-based mapping of defoliation. Our method proved to be suitable for the mapping of defoliated stands, resulting in an overall mapping accuracy of 84.3% and a Cohen’s kappa coefficient of 0.68.
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Received: 1 December 2012; in revised form: 1 April 2013 / Accepted: 9 April 2013 / Published: 15 April 2013
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Abstract: Insular Southeast Asia is a hotspot of humid tropical forest cover loss. A sample-based monitoring approach quantifying forest cover loss from Landsat imagery was implemented to estimate gross forest cover loss for two eras, 1990–2000 and 2000–2005. For each time interval, a probability sample of 18.5 km × 18.5 km blocks was selected, and pairs of Landsat images acquired per sample block were interpreted to quantify forest cover area and gross forest cover loss. Stratified random sampling was implemented for 2000–2005 with MODIS-derived forest cover loss used to define the strata. A probability proportional to x (πpx) design was implemented for 1990–2000 with AVHRR-derived forest cover loss used as the x variable to increase the likelihood of including forest loss area in the sample. The estimated annual gross forest cover loss for Malaysia was 0.43 Mha/yr (SE = 0.04) during 1990–2000 and 0.64 Mha/yr (SE = 0.055) during 2000–2005. Our use of the πpx sampling design represents a first practical trial of this design for sampling satellite imagery. Although the design performed adequately in this study, a thorough comparative investigation of the πpx design relative to other sampling strategies is needed before general design recommendations can be put forth.
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Last update: 18 May 2012
