Phased Array L-band Synthetic Aperture Radar Data Research Articles

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

BackgroundAboveground biomass (AGB) is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.MethodsHere, we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China. We used Landsat time-series observations, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data, and National Forest Inventory (NFI) plot measurements, to generate the forest AGB maps at three time points (1992, 2002 and 2010) showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong, China.ResultsThe proposed model was capable of mapping forest AGB using spectral, textural, topographical variables and the radar backscatter coefficients in an effective and reliable manner. The root mean square error of the plot-level AGB validation was between 15.62 and 53.78 t∙ha− 1, the mean absolute error ranged from 6.54 to 32.32 t∙ha− 1, the bias ranged from − 2.14 to 1.07 t∙ha− 1, and the relative improvement over the random forest algorithm was between 3.8% and 17.7%. The largest coefficient of determination (0.81) and the smallest mean absolute error (6.54 t∙ha− 1) were observed in the 1992 AGB map. The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010. By adding elevation as a covariable, the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals, because co-kriging resulted in better interpolation results in the valleys and plains of the study area.ConclusionsValidation of the three AGB maps with an independent dataset indicated that the random forest/co-kriging performed best for AGB prediction, followed by random forest coupled with ordinary kriging (random forest/ordinary kriging), and the random forest model. The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography. The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.

Quantifying spatial-temporal changes of tea plantations in complex landscapes through integrative analyses of optical and microwave imagery

High demand for tea has driven the expansion of tea plantations in the tropical and subtropical regions over the past few decades. Tea plant cultivation promotes economic development and creates job opportunities, but tea plantation expansion has significant impacts on biodiversity, carbon and water cycles, and ecosystem services. Mapping the spatial distribution and extent of tea plantations in a timely fashion is crucial for land use management and policy making. In this study, we mapped tea plantation expansion in Menghai County, Yunnan Province, China. We analyzed the structure and features of major land cover types in this tropical and subtropical region using (1) the HH and HV gamma-naught imagery from the Advanced Land Observation Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) and (2) time series Landsat TM/ETM+/OLI imagery. Tea plantation maps for 2010 and 2015 were generated using the pixel-based support vector machine (SVM) approach at 30 m resolution, which had high user/producer accuracies of 83.58%/91.67% and 87.50%/90.83%, respectively. The resultant maps show that tea plantation area increased by 33.56% (∼9335 ha), from ∼27,817 ha in 2010 to ∼37,152 ha in 2015. The additional tea plantation area was mainly converted from forest (32.50%) and cropland (67.50%). The results showed that the combination of PALSAR and optical data performed better in tea plantation mapping than using optical data only. This study provides a promising new approach to identify and map tea plantations in complex tropical landscapes at high spatial resolution.

Identification of Altered Minerals Based on Synthetic Aperture Radar (SAR) For Mineral Exploration in a Tropical Area

Geological investigation by remote sensing using surface physical properties in tropical regions is challenging. To minimize the effects of atmosphere and vegetation in the obtained optical images, we used the synthetic aperture radar (SAR) images. The phased array L-band synthetic aperture radar (PALSAR) onboard the advanced land observing satellite (ALOS) was selected due to ability of the L-band to penetrate clouds and canopy vegetation. The polarimetric decomposition method based on Cloude–Pottier classification was used as the basis for backscattering analyses. The Ciseuti area in West Java, Indonesia was selected as the study site due to the existence of mining activities, including gold and galena mines. The identification is focused on the spatial distribution of prospected minerals regardless of cloud and vegetation canopy. The classified prospect zones could be extended using the Cloude– Pottier polarimetric decomposition into moderate random entropy and alpha double bounce scattering at argillic alterations, moderate random entropy and alpha surface scattering at intermediate argillic alterations, and highly moderate random entropy and alpha volume diffusion at advanced argillic alterations. The entropy and alpha extracted from ALOS PALSAR data based on Cloude–Pottier decomposition were very useful for identifying alteration zones.

Aboveground biomass estimation of mangrove species using ALOS-2 PALSAR imagery in Hai Phong City, Vietnam

This study examined the potential of using the HH and HV backscatter from the Advanced Land Observing Satellite 2 (ALOS-2) with enhanced phased array L-band synthetic aperture radar (PALSAR) in high sensitive mode to estimate the above-ground biomass (AGB) of the two mangrove species of Hai Phong city, Vietnam. A positive correlation was observed between the mean backscattering coefficients of the dominant mangrove species at dual polarizations HH and HV and various biophysical parameters. In contrast, low correlations were observed between those coefficients and the tree densities for the two mangrove species. The AGB of the mangrove species were estimated at between 2.8 and 161.5 Mg ha−1 with an average of about 39 Mg ha−1 for Sonneratia caseolaris and between 27.6 and 209.2 Mg ha−1 with an average of ∼100 Mg ha−1 for Kandelia obovata. The main indicators used for the selection of the best potential models in estimating the AGB of different species were R2 and the root-mean-square error (RMSE). The results showed a satisfactory correlation between model estimation and field-based measurements with R2=0.51, RMSE=35.5 Mg ha−1 for S. caseolaris and R2=0.64, RMSE=41.3 Mg ha−1 for K. obovata. This research has illustrated the potential use of ALOS-2 PALSAR data in estimating the AGB of mangrove species in the tropics.

Retrieval of Water Depth of Coastal Wetlands in the Yellow River Delta From ALOS PALSAR Backscattering Coefficients and Interferometry

Coastal wetland ecosystems are among the most productive yet highly threatened systems in the world, and population growth and increasing economic development have resulted in extremely rapid degradation and loss of coastal wetlands. Synthetic aperture radar (SAR) has proved to be of great potential in wetland applications, such as characterizing wetland types and mapping wetland inundation extent, based on the fact that the SAR backscatter signal from the wetland depends mainly on the wetland vegetation and hydrodynamic. However, the absence of ground observations has limited calibration and validation of water depth estimated from satellite SAR data. This letter aims to explore the potential for retrieving the water depth in freshwater marshes using L-band SAR backscattering coefficients. We present an innovative approach for combining backscattering coefficients and interferometric data to estimate water depth from L-band SAR data. The technique was applied to freshwater marshes in the Yellow River Delta using an Advanced Land Observing Satellite Phased Array L-band SAR data set acquired between 2007 and 2010. It is shown that the combination of L-band backscattering coefficients and interferometric data could provide information on the hydrodynamic of the Yellow River Delta with high spatial resolution.

Calibration of Compact Polarimetric SAR Images Using Distributed Targets and One Corner Reflector

Compact polarimetric (CP) synthetic aperture radar (SAR) systems are attracting increasing interest as they possess the ability to construct quad-polarimetric information with advantages of simpler system, lower data rate, and wider swath, compared to a fully polarimetric (FP) system. In order to get undistorted polarimetric information, calibration of CP measurements has to be performed. Freeman and Chen both proposed methods for CP SAR calibration using merely calibrators, which require at least three calibrators at a range line and would require many more calibrators when monitoring the change of the system across the swath is necessary. It has been proved that FP SAR calibration methods exploring scattering characteristics of natural distributed targets (DTs) can reduce the requirement of the number of deployed calibrators. However, it is much more complicated in the case of CP SAR calibration, as only two channels of CP measurements are available. This paper proposes a numerical CP calibration method using natural DTs and only one corner reflector (CR) for a reciprocal CP SAR system. The validity of the method for three typical CP modes is confirmed by simulations with Advanced Land Observing Satellite Phased Array L-band SAR data. Through sensitivity analysis, a dihedral at 0 $^{\circ}$ is selected as the best CR for the calibration algorithm.

Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history

Timber production is the purpose for managing plantation forests, and its spatial and quantitative information is critical for advising management strategies. Previous studies have focused on growing stock volume (GSV), which represents the current potential of timber production, yet few studies have investigated historical process-harvested timber. This resulted in a gap in a synthetical ecosystem service assessment of timber production. In this paper, we established a Management Process–based Timber production (MPT) framework to integrate the current GSV and the harvested timber derived from historical logging regimes, trying to synthetically assess timber production for a historical period. In the MPT framework, age-class and current GSV determine the times of historical thinning and the corresponding harvested timber, by using a “space-for-time” substitution. The total timber production can be estimated by the historical harvested timber in each thinning and the current GSV. To test this MPT framework, an empirical study on a larch plantation (LP) with area of 43,946ha was conducted in North China for a period from 1962 to 2010. Field-based inventory data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) and Landsat-8 OLI (Operational Land Imager) data for estimating the age-class and current GSV of LP. The random forest model with PALSAR backscatter intensity channels and OLI bands as input predictive variables yielded an accuracy of 67.9% with a Kappa coefficient of 0.59 for age-class classification. The regression model using PALSAR data produced a root mean square error (RMSE) of 36.5m3ha−1. The total timber production of LP was estimated to be 7.27×106m3, with 4.87×106m3 in current GSV and 2.40×106m3 in harvested timber through historical thinning. The historical process-harvested timber accounts to 33.0% of the total timber production, which component has been neglected in the assessments for current status of plantation forests. Synthetically considering the RMSE for predictive GSV and misclassification of age-class, the error in timber production were supposed to range from −55.2 to 56.3m3ha−1. The MPT framework can be used to assess timber production of other tree species at a larger spatial scale, providing crucial information for a better understanding of forest ecosystem service.

Estimation of CO2 Sequestration by the Forests in Japan by Discriminating Precise Tree Age Category using Remote Sensing Techniques

This study estimates CO2 sequestration by forests in Japan using Land Remote Sensing Satellite (Landsat) Operational Land Imager (OLI) and the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) remote sensing data for the in-depth retrieval of forest growth stages (tree age). Landsat imagery was used to develop a detailed forest cover map, while the PALSAR data were used to estimate the volume information. The volume was converted to tree age information for each of the three forest types in Japan. An estimation of CO2 sequestration values for each forest type and for each tree age from the forest inventory data was made. The forest cover map results in four classes, and the overall accuracy yields approximately 74%. For the volume estimation, Root Mean Square Error (RMSE) was computed with the ground reference information resulting in 105.58 m3/ha. The final result showed that total CO2 sequestration in Japan based on tree age forest subclasses yields 85.0 Mt∙CO2 (coniferous), 4.76 Mt∙CO2 (evergreen broadleaf) and 21.61 Mt∙CO2 (deciduous broadleaf), which in total is 111.27 Mt∙CO2. Using remote sensing techniques to quantitatively estimate CO2 sequestration in Japanese forests has been shown both to have advantages and to offer further possibilities.

Correction of Transmit Crosstalk in Reconstruction of Quad-Pol Data From Compact Polarimetry Data

A compact polarimetric ( CP) synthetic aperture radar (SAR) has an advantage of reconstructing quad-polarimetric (quad-pol) information from measured dual-polarized scattering vectors. In order to fully explore CP data, calibration of CP measurements is important. However, among different system errors, the crosstalk on transmit is not possible to be corrected in measured scattering vectors even when it is already estimated and known. Except scattering vectors, reconstructed quad-pol data are widely used. Three reconstruction algorithms have been published in the literature: one by Souyris et al. , a refined algorithm by Nord, and another refined one by Collins et al. However, the removal of transmit crosstalk is still left unresolved. In this letter, we proposed a calibration algorithm to correct the transmit crosstalk in reconstructions of quad-pol data from CP data. We conducted simulations with fully polarimetric Advanced Land Observing Satellite Phased Array L-band SAR data. Simulation results confirmed that the algorithm performed effectively and stably under certain conditions.

Mapping forest growth and degradation stage in the Brigalow Belt Bioregion of Australia through integration of ALOS PALSAR and Landsat-derived foliage projective cover data

Differentiation of forest growth stages through classification of single date or time-series of Landsat sensor data is limited because of insensitivity to their three-dimensional structure. This study therefore evaluated the benefits of integrating the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) L-band HH and HV polarisation response from the woody components of vegetation with Landsat-derived foliage projective cover (FPC). Focus was on 12 regional ecosystems (REs) distributed across the Brigalow Belt Bioregion (BRB) of Queensland, Australia, where different stages of growth dominated by brigalow (Acacia harpophylla) were widespread. From remnant areas of brigalow-dominated forests mapped previously for each RE by the Queensland Herbarium through field visits and interpretations of aerial imagery, frequency distributions of all three channels were extracted and compared to those of image segments generated using FPC and PALSAR data. For woody vegetation (with an FPC threshold of ≥9%) outside of the remnant areas, mature (non-remnant) forests were associated with segments where the HH and HV backscatter thresholds were within one standard deviation of the mean extracted for remnant forest. Early-stage regrowth was differentiated using an L-band HH threshold of <−14dB, common for all REs because of similarities in structure at this stage. The early-stage included forests regrowing over several decades and often occurred in areas recovering from recent clearing events. Objects falling between the early and mature stages were considered to be intermediate regrowth and/or degraded forest. All areas with an FPC <9% were mapped as non-forest.Within the BRB, the Queensland Herbarium established that forests with brigalow as a dominant or subdominant component originally occupied over 7.3 million ha but were reduced to 586,364ha by 2009, with 460,499ha (78.5%) having brigalow as the dominant component. Using the Landsat FPC and ALOS PALSAR data, an additional 722,686ha of brigalow-dominated regrowth forest were identified giving a total forested area (brigalow-dominated remnant and secondary forest) of 1,183,185ha or 17.2% of the area of the 12 REs. Within this area, the greater proportion of regrowth (368,473ha or 31.1%) was mapped as early stage primarily because of recovery following recent clearance events. 230,551 (19.5%) ha and 123,662ha (10.5%) were mapped as intermediate and mature (non-remnant) stages respectively and the remainder (38.9%) was remnant forest. Users’ and producers’ accuracies were, respectively, 81% and 69% for early regrowth and 71% and 89% for mature and intermediate stage forests combined. The mapping, which used Queensland Herbarium’s RE data to delineate brigalow extent, provided a structural, rather than age-based classification of growth stage, as is typically retrieved using time-series comparison of optical imagery. The regional estimates of growth/degradation stage generated for the BRB provide a basis for optimising the use and recovery of these threatened brigalow ecosystems with benefits for biodiversity and carbon sequestration.

A Land Cover Variation Model of Water Level for the Floodplain of Tonle Sap, Cambodia, Derived From ALOS PALSAR and MODIS Data

The floodplain around Tonle Sap, Cambodia is strongly influenced by seasonal variations in water level. In the wet season, lacustrine landforms and vegetated areas are partly inundated due to increases in the water level. Conversely, they are gradually emerged when the flooding recedes during the dry season. Because floods in Tonle Sap are an annual event, a land cover variation model that takes into account water level is necessary to predict areal changes in each land cover class at the floodplain. To establish this model, we used the Phased Array L-band Synthetic Aperture Radar (PALSAR) backscattering coefficients, normalized difference vegetation index (NDVI) values, and tasseled cap (TC) transformations of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2007 to 2010 to estimate the areal variation of six land cover classes during the annual flood pulse. The radar backscattering coefficients correlated well with NDVI values during the dry season, but the relationship vanished during the wet season. According to our model, a backscattering coefficient change from -8.4 dB to -20.6 dB for lowland shrubs in the flood developing stage corresponded to an areal percentage change of un-flooded lowland shrubs from 16.3% to 0.5% of the total study area. Once the water level increased to the peak of flooding, 46.2% of the lowland shrub area was immersed. Our model also predicted that approximately 41.8% of the total study area was replaced with a water surface at the peak of flooding. When we compared the two results obtained using our model at 6 m above mean sea level (amsl) and using a digital terrain model (DTM) and the land use map, we observed a large difference between the two models in the areal percentage of the corresponding land cover of un-flooded lowland shrubs (-10.3%). Our land cover variation model can be used to predict areal changes in land cover classes during flood development and recession stages, and can also provide insight into flood dynamics, thereby enabling flood management in this region.

Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat-7 ETM+ data

Integration of multisensor data provides the opportunity to explore benefits emanating from different data sources. A fusion between fraction images derived from spectral mixture analysis of Landsat-7 ETM+ and phased array L-band synthetic aperture radar (PALSAR) is introduced. The aim of this fusion is to improve the estimation accuracy of above-ground biomass (AGB) in lowland mixed dipterocarp forest. Spectral mixture analysis was applied to decompose a mixture of spectral components of Landsat-7 ETM+ into vegetation, soil, and shade fractions. These fraction images were integrated with PALSAR data using the discrete wavelet transform (DWT) and Brovey transform. As a comparison, spectral reflectance of Landsat-7 ETM+ was fused directly with PALSAR data. Backscatter of horizontal–horizontal and horizontal–vertical polarizations was also used to estimate AGB. Forest inventory was carried out in 77 randomly distributed plots, the data being used for either model development or validation. A local allometric equation was applied to calculate AGB per plot. Regression models were developed by integrating field measurements of 50 sample plots with remotely sensed data, e.g. fraction images, reflectance of Landsat-7 ETM+, and PALSAR data. The models developed were validated using 27 independent sample plots. The results showed that not all fused images significantly improved the accuracy of AGB estimation. The model based on Brovey transform using the reflectance of Landsat-7ETM+ and PALSAR produced an R2 of only 0.03–0.10. By contrast, fusion between PALSAR data and fraction images using Brovey transform improved the accuracy of R2 to 0.33–0.46. Further improvement in the accuracy of estimating AGB was observed when DWT was applied to integrate PALSAR with the reflectance of Landsat-7ETM+ (R2 = 0.69–0.72) and PALSAR with fraction images (R2 = 0.70–0.75).

QUANTATAIVE ANALYSIS OF RELATIONSHIP BETWEEN ALOS PALSAR BACKSCATTER AND FOREST STAND VOLUME

To estimate forest stand volume, topographic effects should be considered in Synthetic Aperture Radar (SAR) data processing, because most forests are located on mountainous areas in Korea. This paper shows how the effects on the ALOS Phased-Array L-Band SAR (PALSAR) backscatter in Kwangneung Experiment Forest can be reduced by gamma-nought γ^0.Regardless of coniferous and deciduous forests, the gamma-nought γ^0 values are always lower compared to the sigma-nought σ^0 values at HH and HV polarization for 58 standwise tree volume of the fore-slope (here, the west-facing slope) and are always higher in case of far-slope (here, east-facing slope).Comparison of the fore-slope with the far-slope, relating to the stand volume of the γ^0 and σ^0 values, demonstrates that the σ^0 values are significantly remarkable, while the γ^0 values are not. This means that the topographic normalized backscattering coefficient γ^0 can be used to estimate tree volume, independent of fore-slope and far-slope.Though the aforementioned two backscattering coefficients of γ^0 and σ^0 indicate L-band HH and HV saturations with tree volume observed at around 310 m^3ha^(-1), they increase with increasing trend with forest stand volume.These results show that stand volume retrieval of mountain forests can be improved by using topographic normalized backscattering coefficient of ALOS PALSAR data.

An object-oriented classification method for mapping mangroves in Guinea, West Africa, using multipolarized ALOS PALSAR L-band data

The principal objective of this study was to determine the accuracy of an object-based image analysis (OBIA) approach in classifying mangroves from spaceborne synthetic aperture radar (SAR) data, specifically Advanced Land Observation Satellite (ALOS), phased array L-band synthetic aperture radar (PALSAR), and single-polarized (HH) and dual-polarized (HH + HV) L-bands. The accuracy of the object parameters was examined to determine the optimal colour and shape ratios for the hierarchical classification. At the first level of classification (mangroves from non-mangroves), the results indicate that it is possible to accurately separate mangrove areas from saltpan and water/shallow zones using both sets of SAR images for the Mabala and Yélitono islands of southern Guinea. The final accuracies, based on the most optimal object parameters, were 91.1% and 92.3% for the single- and dual-polarized data, respectively. At the second level of classification, separation among the three mangrove classes identified was most accurate when using the dual-polarized data, at an overall accuracy of only 63.4%. The three mangrove classes identified included tall red mangrove (Rhizophora racemosa), dwarf red mangrove (R. mangle and R. harisonii), and black mangrove (Avicennia germinans). Using the optimal combination of parameters, the extent to which a filter could be used to improve the accuracy was examined. At this level, it was determined that the dual-polarized data, filtered with a 3 × 3 Lee speckle filter and a segmentation scale of 5, resulted in an overall accuracy of 64.9%. Consequently, it is recommended that for persistently cloud-covered regions, such as Guinea, ALOS PALSAR data using an OBIA could be useful as a quick method for mapping and monitoring mangroves.

An Approach to Mapping Forest Growth Stages in Queensland, Australia through Integration of ALOS PALSAR and Landsat Sensor Data

Whilst extensive clearance of forests in the eastern Australian Brigalow Belt Bioregion (BBB) has occurred since European settlement, appropriate management of those that are regenerating can facilitate restoration of biomass (carbon) and biodiversity to levels typical of relatively undisturbed or remnant formations. However, maps of forests are different stages of regeneration are needed to facilitate restoration planning, including prevention of further re-clearing. Focusing on the Tara Downs subregion of the BBB and on forests with brigalow (Acacia harpophylla) as a component, this research establishes a method for differentiating and mapping early, intermediate and remnant growth stages from Japan Aerospace Exploration Agency (JAXA) Advanced Land Observing Satellite (ALOS) Phased-Array L-band Synthetic Aperture Radar (PALSAR) Fine Beam Dual (FBD) L-band HH- and HV-polarisation backscatter and Landsat-derived Foliage Projective Cover (FPC). Using inventory data collected from 74 plots, located in the Tara Downs subregion, forests were assigned to one of three regrowth stages based on their height and cover relative to that of undisturbed stands. The image data were then segmented into objects with each assigned to a growth stage by comparing the distributions of L-band HV and HH polarisation backscatter and FPC to that of reference distributions using a z-test. Comparison with independent assessments of growth stage, based on time-series analysis of aerial photography and SPOT images, established an overall accuracy of &gt; 70%, with this increasing to 90% when intermediate regrowth was excluded and only early-stage regrowth and remnant classes were considered. The proposed method can be adapted to respond to amendments to user-definitions of growth stage and, as regional mosaics of ALOS PALSAR and Landsat FPC are available for Queensland, has application across the state.

Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa)

Guinea-Bissau is one of the poorest countries in the world with a large proportion of its population living in rural areas. While industry is limited, over 70% of the territory is covered by forests, which can potentially be used to attract investment through forest-based projects that promote reductions in carbon emissions and sustainable management. These can be leveraged by producing accurate maps of forest aboveground biomass (AGB) at national level and by developing cost-effective mapping methods that allow reliable future updating for management and engagement in international mechanisms such as the United Nations (UN) Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation (REDD) in developing countries.Using data from Japan's Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR), this study compared a semi-empirical and machine learning algorithm, with the latter based on bagging stochastic gradient boosting (BagSGB), for retrieving the AGB of woody vegetation thereby supporting estimation of national carbon stocks. AGB was estimated by using measurements of tree size collected from112 forest plots during two field campaigns (2007 and 2008) as input to published allometric equations.The BagSGB outperformed the semi-empirical algorithm, resulting in a coefficient of correlation (R) between observed and cross-validation predicted forest AGB values of 0.95 and in a root mean square error (RMSE) of 26.62Mgha−1. Furthermore, the BagSGB model produced also a measure of forest AGB prediction variability (coefficient of variation) on a pixel-by-pixel basis, with values ranging from 7 to 250% (mean=42%). An estimate of total forest AGB carbon stock of 96.93Mt C was obtained in this study for Guinea-Bissau, with a mean forest AGB value of 65.17Mgha−1. Although the mean error associated with this forest AGB map is still undesirably high, several issues were addressed. The heterogeneity of forest structural types, presence of palm trees, and dimension and type of field plots were identified as potential source of uncertainty that must be tackled in future studies. This study represents a step forward regarding the information currently available for Guinea-Bissau.

Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data

Conversion of tropical forests to oil palm plantations in Malaysia and Indonesia has resulted in large-scale environmental degradation, loss of biodiversity and significant carbon emissions. For both countries to participate in the United Nation’s REDD (Reduced Emission from Deforestation and Degradation) mechanism, assessment of forest carbon stocks, including the estimated loss in carbon from conversion to plantation, is needed. In this study, we use a combination of field and remote sensing data to quantify both the magnitude and the geographical distribution of carbon stock in forests and timber plantations, in Sabah, Malaysia, which has been the site of significant expansion of oil palm cultivation over the last two decades. Forest structure data from 129 ha of research and inventory plots were used at different spatial scales to discriminate forest biomass across degradation levels. Field data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) imagery to both discriminate oil palm plantation from forest stands, with an accuracy of 97.0% ( κ = 0.64) and predict AGB using regression analysis of HV-polarized PALSAR data ( R 2 = 0.63, p < .001). Direct estimation of AGB from simple regression models was sensitive to both environmental conditions and forest structure. Precipitation effect on the backscatter data changed the HV prediction of AGB significantly ( R 2 = 0.21, p < .001), and scattering from large leaves of mature palm trees significantly impeded the use of a single HV-based model for predicting AGB in palm oil plantations. Multi-temporal SAR data and algorithms based on forest types are suggested to improve the ability of a sensor similar to ALOS PALSAR for accurately mapping and monitoring forest biomass, now that the ALOS PALSAR sensor is no longer operational.

Evaluation of the altimetry from SRTM-3 and planimetry from high-resolution PALSAR FBD data for semi-detailed topographic mapping in the Amazon Region

The Brazilian Amazon has a deficit of 35% of coverage regarding topographic mapping at semi-detailed (1:100,000) scale. This paper presents an alternative to overcome this scenario using a combination of planialtimetric information from two orbital SAR (Synthetic Aperture Radar) missions. The altimetry was acquired from Shuttle Radar Topography Mission (SRTM), while the planimetry was provided from Fine Beam Dual (FBD) images of the Phased Array L-band Synthetic Aperture Radar (PALSAR) sensor. The research was carried out in the mountainous area of the Serra dos Carajás (Pará State), located on the Amazon region. The quality of the orbital topographic information was evaluated regarding precise planialtimetric measurements acquired from Global Positioning System (GPS) field campaigns. The evaluations were performed following two approaches: (1) the use of Root Mean Square Error (RMSE) and (2) tendency and precision hypothesis tests. The investigation has shown that the planialtimetric quality of the orbital products fulfilled the Brazilian Map Accuracy Standards requirements for 1:100,000 A Class map. Thus, the use of combination of information provided by PALSAR and SRTM-3 data can be considered a promising alternative for production and update of semi-detailed topographic mapping in similar environments of the Amazon region, where topographic information is lacking or presents low quality.

On a Novel Approach Using MLCC and CFAR for the Improvement of Ship Detection by Synthetic Aperture Radar

Multilook cross correlation (MLCC) is a useful technique in extracting the images of ships embedded in heavy sea clutter by synthetic aperture radar (SAR). In the ship detection experiment in 2006 by Phased Array L-band Synthetic Aperture Radar (PALSAR) on board the Advanced Land Observing Satellite, we applied MLCC to PALSAR data in order to extract small fishing boats. The result was that some boats were detected by thresholding MLCC coherence images under favorable conditions. However, it was also found that the threshold method was not suitable to automatically determine the threshold levels corresponding to the desired false alarm rate (FAR) values. In order to overcome this problem and to improve the accuracy of ship detection by MLCC, we propose a new and simple technique of MLCC-constant FAR (CFAR) or gamma-CFAR. In this method, CFAR is applied to interlook coherence images produced by MLCC. We tested this method using simulation and PALSAR data and then found out substantial improvement in signal-to-noise ratio and FAR in comparison with the coherence image alone. In this letter, we summarize the MLCC-CFAR algorithm and the experimental results.

Verification of Polarimetric Calibration Method Including Faraday Rotation Compensation Using PALSAR Data

The spaceborne Phased Array L-band Synthetic Aperture Radar (PALSAR) needs polarimetric calibration in order for PALSAR data to be utilized for various applications such as geophysical analysis. In case of a PALSAR system using L-band, the Faraday rotation (FR) effect, which rotates the polarization plane of radio waves, becomes a problem when full-polarimetric observation data are used, such as target classification using polarization synthesis. Therefore, we need to estimate and remove both antenna distortion matrices and the FR effect. In this paper, we propose a polarimetric calibration method taking both channel imbalance and crosstalk of receiving and transmitting antennas and FR effect into consideration using two reference reflectors, namely, polarization preserving reflector and polarization rotating one. Then, we apply our calibration method to PALSAR data and derive antenna distortion matrices and FR angle simultaneously. Our calibration results show that the estimated antenna distortion matrices are almost equal to the calibration results from the Japan Aerospace Exploration Agency and that the estimated FR angle has a reasonable value.