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The Kyoto and Carbon Initiative—Environmental Applications by ALOS-2 PALSAR-2

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 July 2018) | Viewed by 100457

Special Issue Editors

Kyoto and Carbon Initiative Science Coordinator, solo Earth Observation (soloEO), Tokyo 104-0054, Japan
Interests: operational EO applications for sustainable development and environmental conservation; SAR acquisition strategy development supporting space agencies assure the vast public investments in space benefit the global environment; public open data policies
School of Science and Engineering, Division of Architectural, Civil and Environmental Engineering, Tokyo Denki University, Hatoyama Campus, Ishizaka, Hatoyama, Hiki, Saitama 350-0394, Japan
Interests: SAR; InSAR; PolSAR; calibration; forestry; surface deformation; ionosphere
Special Issues, Collections and Topics in MDPI journals
JAXA Earth Observation Research Center, Tsukuba 305-8505, Japan
Interests: calibration and validation of high-resolution optical and SAR and algorithm development especially for land applications; the Advanced Land Observing Satellite mission series (ALOS-2, ALOS-3, ALOS-4)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Kyoto and Carbon (K&C) Initiative is an international collaborative project led by the Japan Aerospace Exploration Agency (JAXA). It was established in 2001 to stimulate the development of L-band Synthetic Aperture Radar (SAR) applications related to the “4 Cs”: international environmental Conventions (such as UNFCCC/REDD+ and the Ramsar Wetlands Convention), environmental Conservation, Carbon cycle science and Climate change. The current phase of the K&C involves 32 research groups from 19 countries.

This special issue of Remote Sensing is dedicated to demonstrate new applications of L-band SAR—alone or in combination with other radar and/or optical satellite data sources—related to the “4 C” themes outlined above. We invite both K&C Science Team members and other researchers using ALOS-2 PALSAR-2 to submit.

Dr. Ake Rosenqvist
Prof. Masanobu Shimada
Dr. Takeo Tadono
Guest Editors

Manuscript Submission Information

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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

  • ALOS-2 PALSAR-2
  • Sensor synergy
  • Forest
  • Wetlands
  • Environment
  • Carbon
  • Climate

Published Papers (11 papers)

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Research

22 pages, 8259 KiB  
Article
A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR, and Landsat Sensor Data
by Peter Scarth, John Armston, Richard Lucas and Peter Bunting
Remote Sens. 2019, 11(2), 147; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11020147 - 14 Jan 2019
Cited by 33 | Viewed by 10875
Abstract
Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference [...] Read more.
Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference map of Australian forest and woodland structure (height and cover), with this generated by integrating Landsat Thematic Mapper (TM) and Enhanced TM, Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and Ice, Cloud, and land Elevation (ICESat),and Geoscience Laser Altimeter System (GLAS) data. ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) were used to segment and classify the Australian landscape. Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95% percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. Within each class, and for selected areas, the variability in ICESat profiles was found to be similar with differences between segments of the same class attributed largely to clearance or disturbance events. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Validation against airborne LiDAR for a range of forest structural types indicated a high degree of correspondence in estimated height measures. On this basis, a map of vegetation height was generated at a national level and was combined with estimates of cover to produce a revised structural classification based on the scheme of the Australian National Vegetation Information System (NVIS). The benefits of integrating the three datasets for segmenting and classifying the landscape and retrieving biophysical attributes was highlighted with this leading the way for future mapping using ALOS-2 PALSAR-2, Landsat/Sentinel-2, Global Ecosystem Dynamics Investigation (GEDI), and ICESat-2 LiDAR data. The ability to map across large areas provides considerable benefits for quantifying carbon dynamics and informing on biodiversity metrics. Full article
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32 pages, 14803 KiB  
Article
Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon
by Henrique Luis Godinho Cassol, João Manuel de Brito Carreiras, Elisabete Caria Moraes, Luiz Eduardo Oliveira e Cruz de Aragão, Camila Valéria de Jesus Silva, Shaun Quegan and Yosio Edemir Shimabukuro
Remote Sens. 2019, 11(1), 59; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11010059 - 29 Dec 2018
Cited by 19 | Viewed by 5401
Abstract
Secondary forests (SF) are important carbon sinks, removing CO2 from the atmosphere through the photosynthesis process and storing photosynthates in their aboveground live biomass (AGB). This process occurring at large-scales partially counteracts C emissions from land-use change, playing, hence, an important role [...] Read more.
Secondary forests (SF) are important carbon sinks, removing CO2 from the atmosphere through the photosynthesis process and storing photosynthates in their aboveground live biomass (AGB). This process occurring at large-scales partially counteracts C emissions from land-use change, playing, hence, an important role in the global carbon cycle. The absorption rates of carbon in these forests depend on forest physiology, controlled by environmental and climatic conditions, as well as on the past land use, which is rarely considered for retrieving AGB from remotely sensed data. In this context, the main goal of this study is to evaluate the potential of polarimetric (quad-pol) ALOS-2 PALSAR-2 data for estimating AGB in a SF area. Land-use was assessed through Landsat time-series to extract the SF age, period of active land-use (PALU), and frequency of clear cuts (FC) to randomly select the SF plots. A chronosequence of 42 SF plots ranging 3–28 years (20 ha) near the Tapajós National Forest in Pará state was surveyed to quantifying AGB growth. The quad-pol data was explored by testing two regression methods, including non-linear (NL) and multiple linear regression models (MLR). We also evaluated the influence of the past land-use in the retrieving AGB through correlation analysis. The results showed that the biophysical variables were positively correlated with the volumetric scattering, meaning that SF areas presented greater volumetric scattering contribution with increasing forest age. Mean diameter, mean tree height, basal area, species density, and AGB were significant and had the highest Pearson coefficients with the Cloude decomposition (λ3), which in turn, refers to the volumetric contribution backscattering from cross-polarization (HV) (ρ = 0.57–0.66, p-value < 0.001). On the other hand, the historical use (PALU and FC) showed the highest correlation with angular decompositions, being the Touzi target phase angle the highest correlation (Φs) (ρ = 0.37 and ρ = 0.38, respectively). The combination of multiple prediction variables with MLR improved the AGB estimation by 70% comparing to the NL model (R2 adj. = 0.51; RMSE = 38.7 Mg ha−1) bias = 2.1 ± 37.9 Mg ha−1 by incorporate the angular decompositions, related to historical use, and the contribution volumetric scattering, related to forest structure, in the model. The MLR uses six variables, whose selected polarimetric attributes were strongly related with different structural parameters such as the mean forest diameter, basal area, and the mean forest tree height, and not with the AGB as was expected. The uncertainty was estimated to be 18.6% considered all methodological steps of the MLR model. This approach helped us to better understand the relationship between parameters derived from SAR data and the forest structure and its relation to the growth of the secondary forest after deforestation events. Full article
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19 pages, 18505 KiB  
Article
The Global Mangrove Watch—A New 2010 Global Baseline of Mangrove Extent
by Pete Bunting, Ake Rosenqvist, Richard M. Lucas, Lisa-Maria Rebelo, Lammert Hilarides, Nathan Thomas, Andy Hardy, Takuya Itoh, Masanobu Shimada and C. Max Finlayson
Remote Sens. 2018, 10(10), 1669; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10101669 - 22 Oct 2018
Cited by 413 | Viewed by 28506
Abstract
This study presents a new global baseline of mangrove extent for 2010 and has been released as the first output of the Global Mangrove Watch (GMW) initiative. This is the first study to apply a globally consistent and automated method for mapping mangroves, [...] Read more.
This study presents a new global baseline of mangrove extent for 2010 and has been released as the first output of the Global Mangrove Watch (GMW) initiative. This is the first study to apply a globally consistent and automated method for mapping mangroves, identifying a global extent of 137,600 km 2 . The overall accuracy for mangrove extent was 94.0% with a 99% likelihood that the true value is between 93.6–94.5%, using 53,878 accuracy points across 20 sites distributed globally. Using the geographic regions of the Ramsar Convention on Wetlands, Asia has the highest proportion of mangroves with 38.7% of the global total, while Latin America and the Caribbean have 20.3%, Africa has 20.0%, Oceania has 11.9%, North America has 8.4% and the European Overseas Territories have 0.7%. The methodology developed is primarily based on the classification of ALOS PALSAR and Landsat sensor data, where a habitat mask was first generated, within which the classification of mangrove was undertaken using the Extremely Randomized Trees classifier. This new globally consistent baseline will also form the basis of a mangrove monitoring system using JAXA JERS-1 SAR, ALOS PALSAR and ALOS-2 PALSAR-2 radar data to assess mangrove change from 1996 to the present. However, when using the product, users should note that a minimum mapping unit of 1 ha is recommended and that the error increases in regions of disturbance and where narrow strips or smaller fragmented areas of mangroves are present. Artefacts due to cloud cover and the Landsat-7 SLC-off error are also present in some areas, particularly regions of West Africa due to the lack of Landsat-5 data and persistence cloud cover. In the future, consideration will be given to the production of a new global baseline based on 10 m Sentinel-2 composites. Full article
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20 pages, 5987 KiB  
Article
Estimation of Above-Ground Biomass over Boreal Forests in Siberia Using Updated In Situ, ALOS-2 PALSAR-2, and RADARSAT-2 Data
by Martyna A. Stelmaszczuk-Górska, Mikhail Urbazaev, Christiane Schmullius and Christian Thiel
Remote Sens. 2018, 10(10), 1550; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10101550 - 26 Sep 2018
Cited by 26 | Viewed by 4825
Abstract
The estimation of above-ground biomass (AGB) in boreal forests is of special concern as it constitutes the highest carbon pool in the northern hemisphere. Particularly, monitoring of the forests in the Russian Federation is important as some regions have not been inventoried for [...] Read more.
The estimation of above-ground biomass (AGB) in boreal forests is of special concern as it constitutes the highest carbon pool in the northern hemisphere. Particularly, monitoring of the forests in the Russian Federation is important as some regions have not been inventoried for many years. This study explores the combination of multi-frequency, multi-polarization, and multi-temporal radar data as one key approach to provide an accurate estimate of forest biomass. The data from L-band Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-Band Synthetic Aperture Radar 2 (PALSAR-2), together with C-band RADARSAT-2 data, were applied for AGB estimation. Backscatter coefficients from L- and C-band radar were used independently and in combination with a non-parametric model to retrieve AGB data for a boreal forest in Siberia (Krasnoyarskiy Kray). AGB estimation was performed using the random forests machine learning algorithm. The results demonstrated that high estimation accuracies can be achieved at a spatial resolution of 0.25 ha. When the L-band data alone were used for the retrieval, a corrected root-mean-square error (RMSEcor) of 29.4 t ha−1 was calculated. A marginal decrease in RMSEcor was observed when only the filtered L-band backscatter data, without ratio and texture, were used (29.1 t ha−1). The inclusion of the C-band data reduced the over and underestimation; the bias was reduced from 5.5 t ha−1 to 4.7 t ha−1; and a RMSEcor of 30.2 t ha−1 was calculated. Full article
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20 pages, 1930 KiB  
Article
Mapping Mangrove Extent and Change: A Globally Applicable Approach
by Nathan Thomas, Peter Bunting, Richard Lucas, Andy Hardy, Ake Rosenqvist and Temilola Fatoyinbo
Remote Sens. 2018, 10(9), 1466; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091466 - 14 Sep 2018
Cited by 74 | Viewed by 11873
Abstract
This study demonstrates a globally applicable method for monitoring mangrove forest extent at high spatial resolution. A 2010 mangrove baseline was classified for 16 study areas using a combination of ALOS PALSAR and Landsat composite imagery within a random forests classifier. A novel [...] Read more.
This study demonstrates a globally applicable method for monitoring mangrove forest extent at high spatial resolution. A 2010 mangrove baseline was classified for 16 study areas using a combination of ALOS PALSAR and Landsat composite imagery within a random forests classifier. A novel map-to-image change method was used to detect annual and decadal changes in extent using ALOS PALSAR/JERS-1 imagery. The map-to-image method presented makes fewer assumptions of the data than existing methods, is less sensitive to variation between scenes due to environmental factors (e.g., tide or soil moisture) and is able to automatically identify a change threshold. Change maps were derived from the 2010 baseline to 1996 using JERS-1 SAR and to 2007, 2008 and 2009 using ALOS PALSAR. This study demonstrated results for 16 known hotspots of mangrove change distributed globally, with a total mangrove area of 2,529,760 ha. The method was demonstrated to have accuracies consistently in excess of 90% (overall accuracy: 92.2–93.3%, kappa: 0.86) for mapping baseline extent. The accuracies of the change maps were more variable and were dependent upon the time period between images and number of change features. Total change from 1996 to 2010 was 204,850 ha (127,990 ha gain, 76,860 ha loss), with the highest gains observed in French Guiana (15,570 ha) and the highest losses observed in East Kalimantan, Indonesia (23,003 ha). Changes in mangrove extent were the consequence of both natural and anthropogenic drivers, yielding net increases or decreases in extent dependent upon the study site. These updated maps are of importance to the mangrove research community, particularly as the continual updating of the baseline with currently available and anticipated spaceborne sensors. It is recommended that mangrove baselines are updated on at least a 5-year interval to suit the requirements of policy makers. Full article
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18 pages, 10647 KiB  
Article
Zero Deforestation Agreement Assessment at Farm Level in Colombia Using ALOS PALSAR
by Carlos Pedraza, Nicola Clerici, Cristian Fabián Forero, América Melo, Diego Navarrete, Diego Lizcano, Andrés Felipe Zuluaga, Juliana Delgado and Gustavo Galindo
Remote Sens. 2018, 10(9), 1464; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091464 - 13 Sep 2018
Cited by 1 | Viewed by 6314
Abstract
Due to the fast deforestation rates in the tropics, multiple international efforts have been launched to reduce deforestation and develop consistent methodologies to assess forest extension and change. Since 2010 Colombia implemented the Mainstream Sustainable Cattle Ranching project with the participation of small [...] Read more.
Due to the fast deforestation rates in the tropics, multiple international efforts have been launched to reduce deforestation and develop consistent methodologies to assess forest extension and change. Since 2010 Colombia implemented the Mainstream Sustainable Cattle Ranching project with the participation of small farmers in a payment for environmental services (PES) scheme where zero deforestation agreements are signed. To assess the fulfillment of such agreements at farm level, ALOS-1 and ALOS-2 PALSAR fine beam dual imagery for years 2010 and 2016 was processed with ad-hoc routines to estimate stable forest, deforestation, and stable nonforest extension for 2615 participant farms in five heterogeneous regions of Colombia. Landsat VNIR imagery was integrated in the processing chain to reduce classification uncertainties due to radar limitations. Farms associated with Meta Foothills regions showed zero deforestation during the period analyzed (2010–2016), while other regions showed low deforestation rates with the exception of the Cesar River Valley (75 ha). Results, suggests that topography and dry weather conditions have an effect on radar-based mapping accuracy, i.e., deforestation and forest classes showed lower user accuracy values on mountainous and dry regions revealing overestimations in these environments. Nevertheless, overall ALOS Phased Array L-band SAR (PALSAR) data provided overall accurate, relevant, and consistent information for forest change analysis for local zero deforestation agreements assessment. Improvements to preprocessing routines and integration of high dense radar time series should be further investigated to reduce classification errors from complex topography conditions. Full article
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28 pages, 6613 KiB  
Article
Estimation of Methane Emissions from Rice Paddies in the Mekong Delta Based on Land Surface Dynamics Characterization with Remote Sensing
by Hironori Arai, Wataru Takeuchi, Kei Oyoshi, Lam Dao Nguyen and Kazuyuki Inubushi
Remote Sens. 2018, 10(9), 1438; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091438 - 09 Sep 2018
Cited by 19 | Viewed by 7390
Abstract
In paddy soils in the Mekong Delta, soil archaea emit substantial amounts of methane. Reproducing ground flux data using only satellite-observable explanatory variables is a highly transparent method for evaluating regional emissions. We hypothesized that PALSAR-2 (Phased Array type L-band Synthetic Aperture RADAR) [...] Read more.
In paddy soils in the Mekong Delta, soil archaea emit substantial amounts of methane. Reproducing ground flux data using only satellite-observable explanatory variables is a highly transparent method for evaluating regional emissions. We hypothesized that PALSAR-2 (Phased Array type L-band Synthetic Aperture RADAR) can distinguish inundated soil from noninundated soil even if the soil is covered by rice plants. Then, we verified the reproducibility of the ground flux data with satellite-observable variables (soil inundation and cropping calendar) and with hierarchical Bayesian models. Furthermore, inundated/noninundated soils were classified with PALSAR-2. The model parameters were successfully converged using the Hamiltonian–Monte Carlo method. The cross-validation of PALSAR-2 land surface water coverage (LSWC) with several inundation indices of MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-2 (Advanced Microwave Scanning Radiometer-2) data showed that (1) high PALSAR-2-LSWC values were detected even when MODIS and AMSR-2 inundation index values (MODIS-NDWI and AMSR-2-NDFI) were low and (2) low values of PALSAR-2-LSWC tended to be less frequently detected as the MODIS-NDWI and AMSR-2-NDFI increased. These findings indicate the potential of PALSAR-2 to detect inundated soils covered by rice plants even when MODIS and AMSR-2 cannot, and show the similarity between PALSAR-2-LSWC and the other two indices for nonvegetated areas. Full article
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29 pages, 12220 KiB  
Article
Assessing L-Band GNSS-Reflectometry and Imaging Radar for Detecting Sub-Canopy Inundation Dynamics in a Tropical Wetlands Complex
by Katherine Jensen, Kyle McDonald, Erika Podest, Nereida Rodriguez-Alvarez, Viviana Horna and Nicholas Steiner
Remote Sens. 2018, 10(9), 1431; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091431 - 07 Sep 2018
Cited by 52 | Viewed by 6852
Abstract
Despite the growing number of remote-sensing products from satellite sensors, mapping of the combined spatial distribution and temporal variability of inundation in tropical wetlands remains challenging. An emerging innovative approach is offered by Global Navigation Satellite System reflectometry (GNSS-R), a concept that takes [...] Read more.
Despite the growing number of remote-sensing products from satellite sensors, mapping of the combined spatial distribution and temporal variability of inundation in tropical wetlands remains challenging. An emerging innovative approach is offered by Global Navigation Satellite System reflectometry (GNSS-R), a concept that takes advantage of GNSS-transmitting satellites and independent radar receivers to provide bistatic radar observations of Earth’s surface with large-scale coverage. The objective of this paper is to assess the capability of spaceborne GNSS reflections to characterize surface inundation dynamics in a complex wetlands environment in the Peruvian Amazon with respect to current state-of-the-art methods. This study examines contemporaneous ALOS2 PALSAR-2 L-band imaging radar, CYGNSS GNSS reflections, and ground measurements to assess associated advantages and challenges to mapping inundation dynamics, particularly in regions under dense tropical forest canopies. Three derivatives of CYGNSS Delay-Doppler maps (1) peak signal-to-noise ratio (SNR), (2) leading edge slope, and (3) trailing edge slope, demonstrated statistically significant logarithmic relationships with estimated flooded area percentages determined from SAR, with SNR exhibiting the strongest association. Aggregated Delay-Doppler maps SNR time series data examined for inundated regions undetected by imaging radar suggests GNSS-R exhibits a potentially greater sensitivity to inundation state beneath dense forest canopies relative to SAR. Results demonstrate the capability for mapping extent and dynamic wetlands ecosystems in complex tropical landscapes, alone or in combination with other remote-sensing techniques such as those based on imaging radar, contributing to enhanced mapping of these regions. However, several aspects of GNSS-R observations such as noise level, spatial resolution, and signal coherence need to be further examined. Full article
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15 pages, 3955 KiB  
Article
Assessment of Forest above Ground Biomass Estimation Using Multi-Temporal C-band Sentinel-1 and Polarimetric L-band PALSAR-2 Data
by Xiaodong Huang, Beth Ziniti, Nathan Torbick and Mark J. Ducey
Remote Sens. 2018, 10(9), 1424; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091424 - 07 Sep 2018
Cited by 61 | Viewed by 6466
Abstract
Synthetic Aperture Radar (SAR), as an active sensor transmitting long wavelengths, has the advantages of working day and night and without rain or cloud disturbance. It is further able to sense the geometric structure of forests more than passive optical sensors, making it [...] Read more.
Synthetic Aperture Radar (SAR), as an active sensor transmitting long wavelengths, has the advantages of working day and night and without rain or cloud disturbance. It is further able to sense the geometric structure of forests more than passive optical sensors, making it a valuable tool for mapping forest Above Ground Biomass (AGB). This paper studies the ability of the single- and multi-temporal C-band Sentinel-1 and polarimetric L-band PALSAR-2 data to estimate live AGB based on ground truth data collected in New England, USA in 2017. Comparisons of results using the Simple Water Cloud Model (SWCM) on both VH and VV polarizations show that C-band reaches saturation much faster than the L-band due to its limited forest canopy penetration. The exhaustive search multiple linear regression model over the many polarimetric parameters from PALSAR-2 data shows that the combination of polarimetric parameters could slightly improve the AGB estimation, with an adjusted R2 as high as 0.43 and RMSE of around 70 Mg/ha when decomposed Pv component and Alpha angle are used. Additionally, the single- and multi-temporal C-band Sentinel-1 data are compared, which demonstrates that the multi-temporal Sentinel-1 significantly improves the AGB estimation, but still has a much lower adjusted R2 due to the limitations of the short wavelength. Finally, a site-level comparison between paired control and treatment sites shows that the L-band aligns better with the ground truth than the C-band, showing the high potential of the models to be applied to relative biomass change detection. Full article
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23 pages, 7029 KiB  
Article
Multifrequency and Full-Polarimetric SAR Assessment for Estimating Above Ground Biomass and Leaf Area Index in the Amazon Várzea Wetlands
by Luciana O. Pereira, Luiz F. A. Furtado, Evlyn M. L. M. Novo, Sidnei J. S. Sant’Anna, Veraldo Liesenberg and Thiago S. F. Silva
Remote Sens. 2018, 10(9), 1355; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091355 - 25 Aug 2018
Cited by 23 | Viewed by 5887
Abstract
The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) [...] Read more.
The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha−1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide. Full article
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19 pages, 5915 KiB  
Article
Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico
by Mikhail Urbazaev, Felix Cremer, Mirco Migliavacca, Markus Reichstein, Christiane Schmullius and Christian Thiel
Remote Sens. 2018, 10(8), 1277; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10081277 - 14 Aug 2018
Cited by 14 | Viewed by 5126
Abstract
Information on the spatial distribution of forest structure parameters (e.g., aboveground biomass, vegetation height) are crucial for assessing terrestrial carbon stocks and emissions. In this study, we sought to assess the potential and merit of multi-temporal dual-polarised L-band observations for vegetation height estimation [...] Read more.
Information on the spatial distribution of forest structure parameters (e.g., aboveground biomass, vegetation height) are crucial for assessing terrestrial carbon stocks and emissions. In this study, we sought to assess the potential and merit of multi-temporal dual-polarised L-band observations for vegetation height estimation in tropical deciduous and evergreen forests of Mexico. We estimated vegetation height using dual-polarised L-band observations and a machine learning approach. We used airborne LiDAR-based vegetation height for model training and for result validation. We split LiDAR-based vegetation height into training and test data using two different approaches, i.e., considering and ignoring spatial autocorrelation between training and test data. Our results indicate that ignoring spatial autocorrelation leads to an overoptimistic model’s predictive performance. Accordingly, a spatial splitting of the reference data should be preferred in order to provide realistic retrieval accuracies. Moreover, the model’s predictive performance increases with an increasing number of spatial predictors and training samples, but saturates at a specific level (i.e., at 12 dual-polarised L-band backscatter measurements and at around 20% of all training samples). In consideration of spatial autocorrelation between training and test data, we determined an optimal number of L-band observations and training samples as a trade-off between retrieval accuracy and data collection effort. In summary, our study demonstrates the merit of multi-temporal ScanSAR L-band observations for estimation of vegetation height at a larger scale and provides a workflow for robust predictions of this parameter. Full article
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