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Preface: ForestSAT 2014 Special Issue
Remote Sensing of Environment 173 (2016) 211–213
Contents lists available at ScienceDirect
Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse
Preface: ForestSAT 2014 Special Issue Dirk Pflugmacher a,⁎, Ronald E. McRoberts b, Felix Morsdorf c a b c
Department of Geography, Humboldt University of Berlin, Berlin, Germany Northern Research Station, U.S. Forest Service, Saint Paul, MN, USA Department of Geography, University of Zurich, Zurich, Switzerland
a r t i c l e
i n f o
Article history: Received 16 November 2015 Accepted 17 November 2015 Available online 24 November 2015
1. Introduction This special issue contains selected papers from the 2014 ForestSAT conference: a bridge between forest sciences, remote sensing and geospatial applications, held on 4–7 November, 2014, in Riva del Garda, Italy. The 2014 event was the sixth conference of a series of ForestSAT conferences. The first conference was held in Edinburgh, Scotland, in 2002, the second conference was held in Borås, Sweden, in 2005, the third in Montpelier, France, in 2007, the fourth in Lugo, Spain, in 2010, and the fifth conference was held in Corvallis, USA, in 2012. Over the
⁎ Corresponding author.
http://dx.doi.org/10.1016/j.rse.2015.11.025 0034-4257/© 2015 Elsevier Inc. All rights reserved.
years, ForestSAT has become one of the most popular, international remote sensing conferences bringing together scientists, practitioners, and service providers to discuss the latest developments in spatial analysis technologies (SAT) for characterizing and monitoring forested ecosystems. The 2014 ForestSAT saw a record-breaking 360 participants from 40 countries in attendance. The next ForestSAT conference will be hosted by the Universidad Mayor on 14–18 November 2016 in Santiago, Chile (http://forestsat2016.com). One of the highlights of the conference was Professor Marvin Bauer's award for outstanding service as Editor-in-Chief of Remote Sensing of Environment. The award was presented by Dr. Elaine van Ommen Kloeke representing Elsevier Publishing Company, the journal's Associate Editors and members of the journal's Editorial Board. Professor Bauer has been Editor-in-Chief for 35 years, beginning in 1980. Through his efforts, Remote Sensing of Environment has become the top remote sensing journal and among the top very few environmental journals. The journal is now the premier interdisciplinary forum at the interface of the remote sensing, earth, environmental and forest sciences.
212
D. Pflugmacher et al. / Remote Sensing of Environment 173 (2016) 211–213
Celebrating 35 years of Editorship of Professor Marvin Bauer for Remote Sensing of Environment during the ForestSAT 2014 conference. From left to right, front: Gherardo Chirici, Scott Goetz, Josep Peñuelas, Stephen Stehman. Back: Ron McRoberts, Erik Næsset, Elaine van Ommen Kloeke, Marvin Bauer, Warren Cohen, Thomas Hilker, Dirk Pflugmacher.
Papers published in this special issue represent a broad range of forest-related topics, forest ecosystems (i.e., boreal forest, temperate forest, and tropical forest), and geographic regions (i.e., Europe, Eurasia, Africa, North America, and Southeast Asia). Yet, comparing this special issue with past special issues (McRoberts, Donoghue, & Olsson, 2007; McRoberts, Donoghue, & Deshayes, 2009; McRoberts, Cohen, & Pflugmacher, 2014), several trends emerge. First, lidar continues to play an important role for characterizing, mapping and inventorying forest attributes. Seven of eleven papers published in this special issue used lidar. Second, multi-sensor approaches that link fine-scale structure information from very fine resolution imagery or lidar with longterm satellite observations such as those from Landsat have become an attractive means to study forest dynamics. Third, more and more studies focus on monitoring forest change, e.g., assessing land cover change or ecological consequences of disturbance. In the following, we preview the 11 special issue papers grouped into two main categories: 1) Forest attribute prediction, and 2) Assessment of forest ecosystem disturbance dynamics and their ecological impacts. 2. Forest attribute prediction The largest category of six papers focuses on the prediction and estimation of forest structure and composition attributes using various types of active sensors. Sumnall, Hill, and Hinsley (2015) and Hovi, Korhonen, Vauhkonen, and Korpela (2015) studied the potential of full waveform airborne laser scanning (ALS), a relatively recent technology. Sumnall et al. (2015) compared statistical models for predicting forest structure, composition, and deadwood variables for a study area in the United Kingdom based on discrete return and full waveform ALS data. The authors found that discrete return data performed better at modelling structure variables, whereas full waveform data performed better at modelling composition and deadwood variables. However, the timing of data acquisition (i.e., leaf-on versus leaf-off) had a greater influence on model prediction accuracy than the lidar data type. Hovi et al. (2015) studied the performance of ALS full-waveform features for the classification of the main tree species in Finland. The authors found that waveform features, particularly the total backscattered
energy of single returning wave form sequences, outperformed predictors based on discrete-return intensity. The best results were obtained using a stratification of tree heights and data acquired in early summer. Melin, Matala, Mehtätalo, Pusenius, and Packalén (2015) used discrete-return ALS data to examine forest structural characteristics of moose habitat preferences in Finland with particular emphasis on the winter season and the calving season. The results, explained primarily by food preferences, were that moose preferred more mature forests with higher and denser canopies in the summer and autumn and forests with lower canopies in winter. Vauhkonen, Holopainen, Kankare, Vastaranta, and Viitala (2015) explored an approach to extract geometrical information from sparse, discrete-return ALS point clouds that are not dense enough for individual tree crown detection approaches but often available from operational large area surveys. The authors used a specialized set of filters on computationally derived geometry employing topological connectivity. The approach was evaluated by predicting forest attributes such as canopy volume, basal area, stem volume, and biomass using the total volume under the derived geometric space. The resulting coefficient of determination ranged from 0.62 to 0.93, when 245 sample plots were used for parameterization. Thiel and Schmullius (2015) investigated the potential of ALOS PALSAR backscatter and InSAR coherence for mapping forest growing stock volume in Central Siberia. InSAR coherence acquired in frozen conditions offered the greatest potential for predicting growing stock, while PALSAR backscatter acquired in unfrozen conditions showed a weaker relationship and saturated at smaller growing stock volumes. The two predictors combined yielded a corrected, relative root-meansquare error of less than 30% when compared with forest inventory data. Breidenbach, McRoberts, and Astrup (2015) used model-based inference to estimate forest attributes for small domains such as forest stands with few population units and few, if any, sample observations. The analyses focused on the effects of various characterizations of the distribution of model prediction residuals on the empirical coverage of confidence intervals for stand-level estimates. The primary finding was that for small domains such as stands, accurate empirical coverage
D. Pflugmacher et al. / Remote Sensing of Environment 173 (2016) 211–213
of confidence intervals requires that the residual variance, including both heteroscedasticity and spatial correlation, must be accommodated in the variance estimators. 3. Assessment of forest disturbance dynamics and their ecological impact The five papers in this category focus on forest ecosystem change related to climate, wildfire, and logging. Neigh et al. (2015) combined annual Landsat forest disturbance history from 1985 to 2011 with single date IKONOS stereo imagery to estimate the change in young forest canopy height and above ground live dry biomass accumulation for selected sites in the USA. The authors first developed canopy height maps from IKONOS stereo pairs and national elevation data, and subsequently predicted biomass from the canopy height maps by applying a statistical model constructed from the relationship between aboveground biomass from forest inventory data and canopy height metrics from airborne laser scanner data. Estimates of accumulated aboveground dry biomass since clearing were consistent with regional site index curves and ranged from 1.31 to 12.47 t/ha/year with a mean of 6.31 t/ha/year. Vogeler, Zhiqiang, and Cohen (2015) mapped important post-fire wildlife habitat components including deadwood (snags) and woody shrub classes using lidar and Landsat disturbance history metrics at a study site in Oregon, USA. Classification accuracies ranged from 69–85%, suggesting that the approach has potential for habitat mapping applications. The Landsat-based models produced slightly greater accuracies than lidar-based models, whereas the best results were achieved from combining the two datasets. Ioki et al. (2015) used ALS data to assess the similarity of tree community composition after human disturbances in a tropical forest of Borneo, Indonesia. Based on an ordination analysis of tree species communities, the authors found a strong correlation between ALS metrics, such as laser echo penetration rate and maximum canopy height, and tree communities. Since tree community composition is strongly influenced by human disturbances in this region, this study suggests a potential means to map and assess disturbance impacts on biodiversity of tropical forests. Mildrexler, Yang, Cohen, and Bell (2015) developed a forest vulnerability index to assess temperature- and drought-induced tree mortality using MODIS-based land surface temperature, MODIS-based evapotranspiration and gridded precipitation data. The authors evaluated the index in the Pacific Northwest region of the USA where they found that climate-induced physiological stress between the years 2003 to 2012 had increased in the months August and September, and across a variety of ecoregions and forest type groups. Sannier, McRoberts, and Fichet (2015) assessed the feasibility of replacing national wall-to-wall forest and forest change maps with global datasets, for the purpose of estimating deforested area for national greenhouse gas reporting in Gabon. The authors compared a national forest change product developed for the years 2000 and 2010 with the University of Maryland Global Forest Change map product (UMD GFC) using a model-assisted regression (MAR) estimator in combination with reference data obtained from a probability sample. The authors found that the UMD GFC dataset provided a reliable means of producing
213
area statistics at national level combined with appropriate sample reference data, thus offering an alternative to nationally produced datasets. However, the classification errors associated with the Global dataset had non-negligible effects on both the estimate and the precision, which supports previous findings that map data should not be used alone to produce area estimates. Acknowledgements The guest editors thank the organizers for an excellent conference and the authors for their contributions and patience. The numerous reviewers for this special issue deserve special thanks for their diligence and insightful critiques that greatly improved the quality of each paper. Special thanks are extended to Professor Marvin Bauer, Editor-in-Chief Emeritus of Remote Sensing Environment, for allowing us to publish this special issue and to Betty Schiefelbein, Managing Editor, for her guidance, patience, and efficiency in guiding this issue to completion. Dirk Pflugmacher, Ronald McRoberts and Felix Morsdorf Co-Guest Editors ForestSAT 2014 Special Issue References Breidenbach, J., McRoberts, R. E., & Astrup, R. (2015). Empirical coverage of model-based variance estimators for remote sensing assisted estimation of stand-level timber volume. Remote Sensing of Environment, 173, 274–281. Hovi, A., Korhonen, L., Vauhkonen, J., & Korpela, I. (2015). LiDAR waveform features for tree species classification and their sensitivity to tree- and acquisition related parameters. Remote Sensing of Environment, 173, 224–237. Ioki, K., Tsuyuki, S., Hirata, Y., Phua, M. H., Wong, W. V. C., Ling, Z. Y., ... Takao, G. (2015). Evaluation of the similarity in tree community composition in a tropical rainforest using airborne LiDAR data. McRoberts, R. E., Donoghue, D. N. M., & Olsson, H. (2007). Special issue — ForestSAT — Preface. Remote Sensing of Environment, 110, 411. McRoberts, R. E., Donoghue, D. N. M., & Deshayes, M. (2009). 2007 ForestSAT preface. International Journal of Remote Sensing, 30, 4911–4914. McRoberts, R. E., Cohen, W. B., & Pflugmacher, D. (2014). Preface, 2012 ForestSAT special issue. Remote Sensing of Environment, 151, 1–2. Melin, M. M., Matala, J., Mehtätalo, L., Pusenius, J., & Packalén, P. (2015). Ecological dimensions of airborne laser scanning — Analyzing the role of forest structure in moose habitat use within a year. Remote Sensing of Environment, 173, 238–247. Mildrexler, D. J., Yang, Z., Cohen, W. B., & Bell, D. M. (2015). A forest vulnerability index based on drought and high temperatures. Remote Sensing of Environment, 173, 314–325. Neigh, C. S., Masek, J. G., Bourget, P., Rishmawi, K., Zhao, F., Huang, C., ... Nelson, R. F. (2015). Regional rates of young US forest growth estimated from annual Landsat disturbance history and IKONOS stereo imagery. Remote Sensing of Environment, 173, 282–293. Sannier, C., McRoberts, R. E., & Fichet, L. -V. (2015). Suitability of Global Forest Change data to report forest cover estimates at national level in Gabon. Remote Sensing of Environment, 173, 326–338. Sumnall, M. J., Hill, R. A., & Hinsley, S. A. (2015). Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables. Remote Sensing of Environment, 173, 214–223. Thiel, J., & Schmullius, C. (2015). The potential of ALOS PALSAR backscatter and InSAR coherence for forest growing stock volume estimation in Central Siberia. Remote Sensing of Environment, 173, 258–273. Vauhkonen, J., Holopainen, M., Kankare, V., Vastaranta, M., & Viitala, R. (2015). Geometrically explicit description of forest canopy based on 3D triangulations of airborne laser scanning data. Remote Sensing of Environment, 173, 248–257. Vogeler, J. C., Zhiqiang, Y., & Cohen, W. B. (2015). Mapping post-fire habitat characteristics through the fusion of remote sensing tools. Remote Sensing of Environment, 173, 294–303.
Contents lists available at ScienceDirect
Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse
Preface: ForestSAT 2014 Special Issue Dirk Pflugmacher a,⁎, Ronald E. McRoberts b, Felix Morsdorf c a b c
Department of Geography, Humboldt University of Berlin, Berlin, Germany Northern Research Station, U.S. Forest Service, Saint Paul, MN, USA Department of Geography, University of Zurich, Zurich, Switzerland
a r t i c l e
i n f o
Article history: Received 16 November 2015 Accepted 17 November 2015 Available online 24 November 2015
1. Introduction This special issue contains selected papers from the 2014 ForestSAT conference: a bridge between forest sciences, remote sensing and geospatial applications, held on 4–7 November, 2014, in Riva del Garda, Italy. The 2014 event was the sixth conference of a series of ForestSAT conferences. The first conference was held in Edinburgh, Scotland, in 2002, the second conference was held in Borås, Sweden, in 2005, the third in Montpelier, France, in 2007, the fourth in Lugo, Spain, in 2010, and the fifth conference was held in Corvallis, USA, in 2012. Over the
⁎ Corresponding author.
http://dx.doi.org/10.1016/j.rse.2015.11.025 0034-4257/© 2015 Elsevier Inc. All rights reserved.
years, ForestSAT has become one of the most popular, international remote sensing conferences bringing together scientists, practitioners, and service providers to discuss the latest developments in spatial analysis technologies (SAT) for characterizing and monitoring forested ecosystems. The 2014 ForestSAT saw a record-breaking 360 participants from 40 countries in attendance. The next ForestSAT conference will be hosted by the Universidad Mayor on 14–18 November 2016 in Santiago, Chile (http://forestsat2016.com). One of the highlights of the conference was Professor Marvin Bauer's award for outstanding service as Editor-in-Chief of Remote Sensing of Environment. The award was presented by Dr. Elaine van Ommen Kloeke representing Elsevier Publishing Company, the journal's Associate Editors and members of the journal's Editorial Board. Professor Bauer has been Editor-in-Chief for 35 years, beginning in 1980. Through his efforts, Remote Sensing of Environment has become the top remote sensing journal and among the top very few environmental journals. The journal is now the premier interdisciplinary forum at the interface of the remote sensing, earth, environmental and forest sciences.
212
D. Pflugmacher et al. / Remote Sensing of Environment 173 (2016) 211–213
Celebrating 35 years of Editorship of Professor Marvin Bauer for Remote Sensing of Environment during the ForestSAT 2014 conference. From left to right, front: Gherardo Chirici, Scott Goetz, Josep Peñuelas, Stephen Stehman. Back: Ron McRoberts, Erik Næsset, Elaine van Ommen Kloeke, Marvin Bauer, Warren Cohen, Thomas Hilker, Dirk Pflugmacher.
Papers published in this special issue represent a broad range of forest-related topics, forest ecosystems (i.e., boreal forest, temperate forest, and tropical forest), and geographic regions (i.e., Europe, Eurasia, Africa, North America, and Southeast Asia). Yet, comparing this special issue with past special issues (McRoberts, Donoghue, & Olsson, 2007; McRoberts, Donoghue, & Deshayes, 2009; McRoberts, Cohen, & Pflugmacher, 2014), several trends emerge. First, lidar continues to play an important role for characterizing, mapping and inventorying forest attributes. Seven of eleven papers published in this special issue used lidar. Second, multi-sensor approaches that link fine-scale structure information from very fine resolution imagery or lidar with longterm satellite observations such as those from Landsat have become an attractive means to study forest dynamics. Third, more and more studies focus on monitoring forest change, e.g., assessing land cover change or ecological consequences of disturbance. In the following, we preview the 11 special issue papers grouped into two main categories: 1) Forest attribute prediction, and 2) Assessment of forest ecosystem disturbance dynamics and their ecological impacts. 2. Forest attribute prediction The largest category of six papers focuses on the prediction and estimation of forest structure and composition attributes using various types of active sensors. Sumnall, Hill, and Hinsley (2015) and Hovi, Korhonen, Vauhkonen, and Korpela (2015) studied the potential of full waveform airborne laser scanning (ALS), a relatively recent technology. Sumnall et al. (2015) compared statistical models for predicting forest structure, composition, and deadwood variables for a study area in the United Kingdom based on discrete return and full waveform ALS data. The authors found that discrete return data performed better at modelling structure variables, whereas full waveform data performed better at modelling composition and deadwood variables. However, the timing of data acquisition (i.e., leaf-on versus leaf-off) had a greater influence on model prediction accuracy than the lidar data type. Hovi et al. (2015) studied the performance of ALS full-waveform features for the classification of the main tree species in Finland. The authors found that waveform features, particularly the total backscattered
energy of single returning wave form sequences, outperformed predictors based on discrete-return intensity. The best results were obtained using a stratification of tree heights and data acquired in early summer. Melin, Matala, Mehtätalo, Pusenius, and Packalén (2015) used discrete-return ALS data to examine forest structural characteristics of moose habitat preferences in Finland with particular emphasis on the winter season and the calving season. The results, explained primarily by food preferences, were that moose preferred more mature forests with higher and denser canopies in the summer and autumn and forests with lower canopies in winter. Vauhkonen, Holopainen, Kankare, Vastaranta, and Viitala (2015) explored an approach to extract geometrical information from sparse, discrete-return ALS point clouds that are not dense enough for individual tree crown detection approaches but often available from operational large area surveys. The authors used a specialized set of filters on computationally derived geometry employing topological connectivity. The approach was evaluated by predicting forest attributes such as canopy volume, basal area, stem volume, and biomass using the total volume under the derived geometric space. The resulting coefficient of determination ranged from 0.62 to 0.93, when 245 sample plots were used for parameterization. Thiel and Schmullius (2015) investigated the potential of ALOS PALSAR backscatter and InSAR coherence for mapping forest growing stock volume in Central Siberia. InSAR coherence acquired in frozen conditions offered the greatest potential for predicting growing stock, while PALSAR backscatter acquired in unfrozen conditions showed a weaker relationship and saturated at smaller growing stock volumes. The two predictors combined yielded a corrected, relative root-meansquare error of less than 30% when compared with forest inventory data. Breidenbach, McRoberts, and Astrup (2015) used model-based inference to estimate forest attributes for small domains such as forest stands with few population units and few, if any, sample observations. The analyses focused on the effects of various characterizations of the distribution of model prediction residuals on the empirical coverage of confidence intervals for stand-level estimates. The primary finding was that for small domains such as stands, accurate empirical coverage
D. Pflugmacher et al. / Remote Sensing of Environment 173 (2016) 211–213
of confidence intervals requires that the residual variance, including both heteroscedasticity and spatial correlation, must be accommodated in the variance estimators. 3. Assessment of forest disturbance dynamics and their ecological impact The five papers in this category focus on forest ecosystem change related to climate, wildfire, and logging. Neigh et al. (2015) combined annual Landsat forest disturbance history from 1985 to 2011 with single date IKONOS stereo imagery to estimate the change in young forest canopy height and above ground live dry biomass accumulation for selected sites in the USA. The authors first developed canopy height maps from IKONOS stereo pairs and national elevation data, and subsequently predicted biomass from the canopy height maps by applying a statistical model constructed from the relationship between aboveground biomass from forest inventory data and canopy height metrics from airborne laser scanner data. Estimates of accumulated aboveground dry biomass since clearing were consistent with regional site index curves and ranged from 1.31 to 12.47 t/ha/year with a mean of 6.31 t/ha/year. Vogeler, Zhiqiang, and Cohen (2015) mapped important post-fire wildlife habitat components including deadwood (snags) and woody shrub classes using lidar and Landsat disturbance history metrics at a study site in Oregon, USA. Classification accuracies ranged from 69–85%, suggesting that the approach has potential for habitat mapping applications. The Landsat-based models produced slightly greater accuracies than lidar-based models, whereas the best results were achieved from combining the two datasets. Ioki et al. (2015) used ALS data to assess the similarity of tree community composition after human disturbances in a tropical forest of Borneo, Indonesia. Based on an ordination analysis of tree species communities, the authors found a strong correlation between ALS metrics, such as laser echo penetration rate and maximum canopy height, and tree communities. Since tree community composition is strongly influenced by human disturbances in this region, this study suggests a potential means to map and assess disturbance impacts on biodiversity of tropical forests. Mildrexler, Yang, Cohen, and Bell (2015) developed a forest vulnerability index to assess temperature- and drought-induced tree mortality using MODIS-based land surface temperature, MODIS-based evapotranspiration and gridded precipitation data. The authors evaluated the index in the Pacific Northwest region of the USA where they found that climate-induced physiological stress between the years 2003 to 2012 had increased in the months August and September, and across a variety of ecoregions and forest type groups. Sannier, McRoberts, and Fichet (2015) assessed the feasibility of replacing national wall-to-wall forest and forest change maps with global datasets, for the purpose of estimating deforested area for national greenhouse gas reporting in Gabon. The authors compared a national forest change product developed for the years 2000 and 2010 with the University of Maryland Global Forest Change map product (UMD GFC) using a model-assisted regression (MAR) estimator in combination with reference data obtained from a probability sample. The authors found that the UMD GFC dataset provided a reliable means of producing
213
area statistics at national level combined with appropriate sample reference data, thus offering an alternative to nationally produced datasets. However, the classification errors associated with the Global dataset had non-negligible effects on both the estimate and the precision, which supports previous findings that map data should not be used alone to produce area estimates. Acknowledgements The guest editors thank the organizers for an excellent conference and the authors for their contributions and patience. The numerous reviewers for this special issue deserve special thanks for their diligence and insightful critiques that greatly improved the quality of each paper. Special thanks are extended to Professor Marvin Bauer, Editor-in-Chief Emeritus of Remote Sensing Environment, for allowing us to publish this special issue and to Betty Schiefelbein, Managing Editor, for her guidance, patience, and efficiency in guiding this issue to completion. Dirk Pflugmacher, Ronald McRoberts and Felix Morsdorf Co-Guest Editors ForestSAT 2014 Special Issue References Breidenbach, J., McRoberts, R. E., & Astrup, R. (2015). Empirical coverage of model-based variance estimators for remote sensing assisted estimation of stand-level timber volume. Remote Sensing of Environment, 173, 274–281. Hovi, A., Korhonen, L., Vauhkonen, J., & Korpela, I. (2015). LiDAR waveform features for tree species classification and their sensitivity to tree- and acquisition related parameters. Remote Sensing of Environment, 173, 224–237. Ioki, K., Tsuyuki, S., Hirata, Y., Phua, M. H., Wong, W. V. C., Ling, Z. Y., ... Takao, G. (2015). Evaluation of the similarity in tree community composition in a tropical rainforest using airborne LiDAR data. McRoberts, R. E., Donoghue, D. N. M., & Olsson, H. (2007). Special issue — ForestSAT — Preface. Remote Sensing of Environment, 110, 411. McRoberts, R. E., Donoghue, D. N. M., & Deshayes, M. (2009). 2007 ForestSAT preface. International Journal of Remote Sensing, 30, 4911–4914. McRoberts, R. E., Cohen, W. B., & Pflugmacher, D. (2014). Preface, 2012 ForestSAT special issue. Remote Sensing of Environment, 151, 1–2. Melin, M. M., Matala, J., Mehtätalo, L., Pusenius, J., & Packalén, P. (2015). Ecological dimensions of airborne laser scanning — Analyzing the role of forest structure in moose habitat use within a year. Remote Sensing of Environment, 173, 238–247. Mildrexler, D. J., Yang, Z., Cohen, W. B., & Bell, D. M. (2015). A forest vulnerability index based on drought and high temperatures. Remote Sensing of Environment, 173, 314–325. Neigh, C. S., Masek, J. G., Bourget, P., Rishmawi, K., Zhao, F., Huang, C., ... Nelson, R. F. (2015). Regional rates of young US forest growth estimated from annual Landsat disturbance history and IKONOS stereo imagery. Remote Sensing of Environment, 173, 282–293. Sannier, C., McRoberts, R. E., & Fichet, L. -V. (2015). Suitability of Global Forest Change data to report forest cover estimates at national level in Gabon. Remote Sensing of Environment, 173, 326–338. Sumnall, M. J., Hill, R. A., & Hinsley, S. A. (2015). Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables. Remote Sensing of Environment, 173, 214–223. Thiel, J., & Schmullius, C. (2015). The potential of ALOS PALSAR backscatter and InSAR coherence for forest growing stock volume estimation in Central Siberia. Remote Sensing of Environment, 173, 258–273. Vauhkonen, J., Holopainen, M., Kankare, V., Vastaranta, M., & Viitala, R. (2015). Geometrically explicit description of forest canopy based on 3D triangulations of airborne laser scanning data. Remote Sensing of Environment, 173, 248–257. Vogeler, J. C., Zhiqiang, Y., & Cohen, W. B. (2015). Mapping post-fire habitat characteristics through the fusion of remote sensing tools. Remote Sensing of Environment, 173, 294–303.