Imaging Radar for Ecosystem Studies

Waring, Richard H.; Way, J.; Hunt, E. Raymond; Morrissey, L. A.; Ranson, K.J.; Weishampel, John F.; Oren, Ram; Franklin, Steven E.

doi:10.2307/1312677

Cited by 113 publications

(55 citation statements)

References 52 publications

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“…QSCAT observations are not impacted by cloud cover, atmospheric aerosol and radiation condition, and are sensitive to the structure and water content of forest canopy, providing a reliable remote sensing technique rstb.royalsocietypublishing.org Phil Trans R Soc B 368: 20120306 to monitor impact of climate on tropical forests [17,18,26]. Given the high frequency (2.2 cm) and the steep incidence angle (468-548) of the QSCAT radar, the backscatter measurement over dense tropical forests is sensitive only to the top canopy structure and moisture and has relatively no information about the underlying soil moisture [27][28][29][30][31] (see the electronic supplementary material). We used 4-day composited QSCAT backscatter data (s 0 ) with enhanced resolutions of 4.45 km at H polarization in ascending mode from morning passes (6.00 LST) to create backscatter anomalies of monthly, seasonal and DQ (averaged backscatter values of three consecutive months with lowest monthly backscatter values) for the entire time series.…”

Section: Methodsmentioning

confidence: 99%

Response of African humid tropical forests to recent rainfall anomalies

Asefi-Najafabady

Saatchi

2013

Phil. Trans. R. Soc. B

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During the last decade, strong negative rainfall anomalies resulting from increased sea surface temperature in the tropical Atlantic have caused extensive droughts in rainforests of western Amazonia, exerting persistent effects on the forest canopy. In contrast, there have been no significant impacts on rainforests of West and Central Africa during the same period, despite large-scale droughts and rainfall anomalies during the same period. Using a combination of rainfall observations from meteorological stations from the Climate Research Unit (CRU; 1950–2009) and satellite observations of the Tropical Rainfall Measuring Mission (TRMM; 1998–2010), we show that West and Central Africa experienced strong negative water deficit (WD) anomalies over the last decade, particularly in 2005, 2006 and 2007. These anomalies were a continuation of an increasing drying trend in the region that started in the 1970s. We monitored the response of forests to extreme rainfall anomalies of the past decade by analysing the microwave scatterometer data from QuickSCAT (1999–2009) sensitive to variations in canopy water content and structure. Unlike in Amazonia, we found no significant impacts of extreme WD events on forests of Central Africa, suggesting potential adaptability of these forests to short-term severe droughts. Only forests near the savanna boundary in West Africa and in fragmented landscapes of the northern Congo Basin responded to extreme droughts with widespread canopy disturbance that lasted only during the period of WD. Time-series analyses of CRU and TRMM data show most regions in Central and West Africa experience seasonal or decadal extreme WDs (less than −600 mm). We hypothesize that the long-term historical extreme WDs with gradual drying trends in the 1970s have increased the adaptability of humid tropical forests in Africa to droughts.

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Section: Methodsmentioning

confidence: 99%

Response of African humid tropical forests to recent rainfall anomalies

Asefi-Najafabady

Saatchi

2013

Phil. Trans. R. Soc. B

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“…They found the strongest relationship between biomass and the NIR band, with an R One of the major limitations of using satellite or CIR imagery to estimate aboveground biomass is the so-called "saturation problem," wherein optical/infrared sensors are unable to distinguish variations in green biomass in areas of dense vegetation canopies. This saturation occurs when the canopy closure and leaf area density reach a threshold (varies by vegetation type) beyond which the optical/infrared sensor cannot detect increasing levels of green biomass response [14,15]. To address this problem, it has been proposed that biomass estimation could be improved by combining imagery from optical/infrared sensors with data acquired from active remote sensors, such as radar and airborne laser scanning (ALS, commonly referred to as "lidar" (light detection and ranging)) [14,16].…”

Section: Introductionmentioning

confidence: 99%

“…This saturation occurs when the canopy closure and leaf area density reach a threshold (varies by vegetation type) beyond which the optical/infrared sensor cannot detect increasing levels of green biomass response [14,15]. To address this problem, it has been proposed that biomass estimation could be improved by combining imagery from optical/infrared sensors with data acquired from active remote sensors, such as radar and airborne laser scanning (ALS, commonly referred to as "lidar" (light detection and ranging)) [14,16]. These active sensors provide additional information about topography and/or vegetation height that could potentially improve the quantitative models developed to predict aboveground biomass and other vegetation characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…These active sensors provide additional information about topography and/or vegetation height that could potentially improve the quantitative models developed to predict aboveground biomass and other vegetation characteristics. Waring et al [14] argued that Landsat imagery could be combined with synthetic aperture radar (SAR) data to improve biomass estimation in areas of dense vegetation, and Moghaddam et al [17] reported significant improvements in regression models relating foliage biomass to Landsat TM data when combined with airborne SAR data collected over old-growth coniferous forests in the Pacific Northwest. In their study of woodland, shrub, grass, and mixed vegetation ecosystems in the Canadian arctic, Chen et al [18] found that, in all cases, regression models combining Landsat TM and JERS-1 SAR data explained more of the variation in aboveground biomass than models containing either data source alone.…”

Section: Introductionmentioning

confidence: 99%

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Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density

et al. 2015

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Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three-class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.

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“…In undisturbed environments (no buildings or agriculture) it can be assumed that scattering is governed by soil type and vegetation cover. The influence of vascular plants on signal interaction is, however, limited at C band (approximately 5.6 cm wavelength; Waring et al, 1995). Surface roughness thus plays an important role for spatial differences in backscatter during frozen conditions in tundra regions.…”

Section: Introductionmentioning

confidence: 99%

Can C-band synthetic aperture radar be used to estimate soil organic carbon storage in tundra?

et al. 2016

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Abstract.A new approach for the estimation of soil organic carbon (SOC) pools north of the tree line has been developed based on synthetic aperture radar (SAR; ENVISAT Advanced SAR Global Monitoring mode) data. SOC values are directly determined from backscatter values instead of upscaling using land cover or soil classes. The multi-mode capability of SAR allows application across scales. It can be shown that measurements in C band under frozen conditions represent vegetation and surface structure properties which relate to soil properties, specifically SOC. It is estimated that at least 29 Pg C is stored in the upper 30 cm of soils north of the tree line. This is approximately 25 % less than stocks derived from the soil-map-based Northern Circumpolar Soil Carbon Database (NCSCD). The total stored carbon is underestimated since the established empirical relationship is not valid for peatlands or strongly cryoturbated soils. The approach does, however, provide the first spatially consistent account of soil organic carbon across the Arctic. Furthermore, it could be shown that values obtained from 1 km resolution SAR correspond to accounts based on a high spatial resolution (2 m) land cover map over a study area of about 7 × 7 km in NE Siberia. The approach can be also potentially transferred to medium-resolution C-band SAR data such as ENVISAT ASAR Wide Swath with ∼ 120 m resolution but it is in general limited to regions without woody vegetation. Global Monitoring-mode-derived SOC increases with unfrozen period length. This indicates the importance of this parameter for modelling of the spatial distribution of soil organic carbon storage.

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Imaging Radar for Ecosystem Studies

Cited by 113 publications

References 52 publications

Response of African humid tropical forests to recent rainfall anomalies

Response of African humid tropical forests to recent rainfall anomalies

Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density

Can C-band synthetic aperture radar be used to estimate soil organic carbon storage in tundra?

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