Water stress detection using radar

Emmerik, Tim van

doi:10.31237/osf.io/943hr

Cited by 3 publications

(4 citation statements)

References 172 publications

(344 reference statements)

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“…In dense forest canopies, therefore, the backscatter from such microwaves easily saturates and represents only a cross-section of the top canopy. However, the edge of forests or canopy trees is particularly significant for radar system as such edges can provide conditions that result in "double-bounce" backscatter [69,78]. The co-polarized backscatter is sensitive to conditions of double bounce which is provided by surface components of vegetation [69].…”

Section: Sensitivity Of S-1a Backscatter (Vv Vs Vh) To Canopy Gap Fra...mentioning

confidence: 99%

Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon

Numbisi

Coillie

2020

Remote Sensing

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A reliable estimation and monitoring of tree canopy cover or shade distribution is essential for a sustainable cocoa production via agroforestry systems. Remote sensing (RS) data offer great potential in retrieving and monitoring vegetation status at landscape scales. However, parallel advancements in image processing and analysis are required to appropriately use such data for different targeted applications. This study assessed the potential of Sentinel-1A (S-1A) C-band synthetic aperture radar (SAR) backscatter in estimating canopy cover variability in cocoa agroforestry landscapes. We investigated two landscapes, in Center and South Cameroon, which differ in predominant vegetation: forest-savannah transition and forest landscape, respectively. We estimated canopy cover using in-situ digital hemispherical photographs (DHPs) measures of gap fraction, verified the relationship with SAR backscatter intensity and assessed predictions based on three machine learning approaches: multivariate bootstrap regression, neural networks regression, and random forest regression. Our results showed that about 30% of the variance in canopy gap fraction in the cocoa production landscapes was shared by the used SAR backscatter parameters: a combination of S-1A backscatter intensity, backscatter coefficients, difference, cross ratios, and normalized ratios. Based on the model predictions, the VV (co-polarization) backscatter showed high importance in estimating canopy gap fraction; the VH (cross-polarized) backscatter was less sensitive to the estimated canopy gap. We observed that a combination of different backscatter variables was more reliable at predicting the canopy gap variability in the considered type of vegetation in this study—agroforests. Semi-variogram analysis of canopy gap fraction at the landscape scale revealed higher spatial clustering of canopy gap, based on spatial correlation, at a distance range of 18.95 m in the vegetation transition landscape, compared to a 51.12 m spatial correlation range in the forest landscape. We provide new insight on the spatial variability of canopy gaps in the cocoa landscapes which may be essential for predicting impacts of changing and extreme (drought) weather conditions on farm management and productivity. Our results contribute a proof-of-concept in using current and future SAR images to support management tools or strategies on tree inventorying and decisions regarding incentives for shade tree retention and planting in cocoa landscapes.

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Section: Sensitivity Of S-1a Backscatter (Vv Vs Vh) To Canopy Gap Fra...mentioning

confidence: 99%

Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon

Numbisi

Coillie

2020

Remote Sensing

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“…All data were collected around the K34 observatory (130 m above Mean Sea Level) in the Amazon rainforest (2.6085 • S, 60.2093 • W), 60km Northwest of Manaus, Brazil (see van Emmerik et al, 2017). A map of the measurement location, including the measured trees and the K34 observatory is presented in Figure 1A.…”

Section: Measurement Locationmentioning

confidence: 99%

“…More recently, Jackson et al (in review) demonstrated that there is a significant difference between trees during summer (with leaves) and winter (no leaves). van Emmerik (2017) and van Emmerik et al (2018a) related changes in tree sway to water stress induced mass changes, which was hypothesized to be caused by decreasing tree water content and/or leaf fall. Future efforts might focus on using tree sway data to quantify tree mass changes on even lower time scales.…”

Section: Tree Mass Variationsmentioning

confidence: 99%

“…Accurate estimates of tree and canopy drag coefficient might allow better representation of the canopy in, for example, land-surface models. An approximation of tree drag coefficient was presented by van Emmerik (2017) and van Emmerik et al (2018a), based on the assumption that during turbulent conditions, available wind energy can be estimated using Kolmogorov's theory (Kolmogorov, 1991). If actual high frequency wind speed data is available, drag coefficients can be determined under all wind conditions using equation 2.…”

Section: Drag Coefficient and Roughness Lengthmentioning

confidence: 99%

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Tree Sway Time Series of 7 Amazon Tree Species (July 2015–May 2016)

et al. 2018

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Potential of Sentinel-1 SAR to Assess Damage in Drought-Affected Temperate Deciduous Broadleaf Forests

et al. 2023

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Understanding forest decline under drought pressure is receiving research attention due to the increasing frequency of large-scale heat waves and massive tree mortality events. However, since assessing mortality on the ground is challenging and costly, this study explores the capability of satellite-borne Copernicus Sentinel-1 (S-1) C-band radar data for monitoring drought-induced tree canopy damage. As droughts cause water deficits in trees and eventually lead to early foliage loss, the S-1 radiometric signal and polarimetric indices are tested regarding their sensitivities to these effects, exemplified in a deciduous broadleaf forest. Due to the scattered nature of mortality in the study site, we employed a temporal-only time series filtering scheme that provides very high spatial resolution (10 m ×10 m) for measuring at the scale of single trees. Finally, the anomaly between heavily damaged and non-damaged tree canopy samples (n = 146 per class) was used to quantify the level of damage. With a maximum anomaly of −0.50 dB ± 1.38 for S-1 Span (VV+VH), a significant decline in hydrostructural scattering (moisture and geometry of scatterers as seen by SAR) was found in the second year after drought onset. By contrast, S-1 polarimetric indices (cross-ratio, RVI, Hα) showed limited capability in detecting drought effects. From our time series evaluation, we infer that damaged canopies exhibit both lower leaf-on and leaf-off backscatters compared to unaffected canopies. We further introduce an NDVI/Span hysteresis showing a lagged signal anomaly of Span behind NDVI (by ca. one year). This time-lagged correlation implies that SAR is able to add complementary information to optical remote sensing data for detecting drought damage due to its sensitivity to physiological and hydraulic tree canopy damage. Our study lays out the promising potential of SAR remote sensing information for drought impact assessment in deciduous broadleaf forests.

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Water stress detection using radar

Abstract: NOMENCLATUREV 2 Water-cloud vegetation model parameter [-]

Cited by 3 publications

References 172 publications

Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon

Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon

Tree Sway Time Series of 7 Amazon Tree Species (July 2015–May 2016)

Potential of Sentinel-1 SAR to Assess Damage in Drought-Affected Temperate Deciduous Broadleaf Forests

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