Separation of Ground and Low Vegetation Signatures in LiDAR Measurements of Salt-Marsh Environments

Wang, Cheng; Menenti, Massimo; Stoll, M. P.; Feola, Alessandra; Belluco, Enrica; Marani, Marco

doi:10.1109/tgrs.2008.2010490

Cited by 103 publications

(41 citation statements)

References 28 publications

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“…However, the dense cloud points generated by UAV could capture the entire height information of grassland, including the stem, but not the top of the vegetation. Consequently, inversed average vegetation height was always lower than measured mean canopy height, which was comparable to the results obtained using airborne lidar system [68,69].…”

Section: Estimation Of Grassland Canopy Height From Uavsupporting

confidence: 85%

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

et al. 2018

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Aboveground biomass is a key indicator of a grassland ecosystem. Accurate estimation from remote sensing is important for understanding the response of grasslands to climate change and disturbance at a large scale. However, the precision of remote sensing inversion is limited by a lack in the ground truth and scale mismatch with satellite data. In this study, we first tried to establish a grassland aboveground biomass estimation model at 1 m 2 quadrat scale by conducting synchronous experiments of unmanned aerial vehicle (UAV) and field measurement in three different grassland ecosystems. Two flight modes (the new QUADRAT mode and the commonly used MOSAIC mode) were used to generate point clouds for further processing. Canopy height metrics of each quadrat were then calculated using the canopy height model (CHM). Correlation analysis showed that the mean of the canopy height model (CHM_mean) had a significant linear relationship with field height (R 2 = 0.90, root mean square error (RMSE) = 19.79 cm, rRMSE = 16.5%, p < 0.001) and a logarithmic relationship with field aboveground biomass (R 2 = 0.89, RMSE = 91.48 g/m 2 , rRMSE = 16.11%, p < 0.001). We concluded our study by conducting a preliminary application of estimation of the aboveground biomass at a plot scale by jointly using UAV and the constructed 1 m 2 quadrat scale estimation model. Our results confirmed that UAV could be used to collect large quantities of ground truths and bridge the scales between ground truth and remote sensing pixels, which were helpful in improving the accuracy of remote sensing inversion of grassland aboveground biomass.

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Section: Estimation Of Grassland Canopy Height From Uavsupporting

confidence: 85%

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

et al. 2018

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“…The effect of enabling these optional treatments is further discussed in the results section. Although methods exist to account for vegetation cover in the DEM (Hladik and Alber, 2012;Wang et al, 2009;Sadro et al, 2007;Chassereau et al, 2011;Montané and Torres, 2006), we chose not to apply these corrections as we wanted to ensure that the TIP method can be applied without information on the vegetation assemblages at a given site.…”

Section: Preprocessing Topographic Datamentioning

confidence: 99%

“…The topographic data necessary to identify marsh platforms already exist: the proliferation of freely available highresolution topographic datasets from lidar or structure from motion (SfM) techniques means that DEMs with a grid cell size below 1 m are increasingly common on salt marshes and offer vertical accuracies below 20 cm even without correcting for vegetation (Sadro et al, 2007;Wang et al, 2009;Chassereau et al, 2011). At these resolutions, most scarps and channels are detectable on a DEM, and several automated topographic methods already allow the identification of tidal channel networks (Fagherazzi et al, 1999;Liu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised detection of salt marsh platforms: a topographic method

2018

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Abstract. Salt marshes filter pollutants, protect coastlines against storm surges, and sequester carbon, yet are under threat from sea level rise and anthropogenic modification. The sustained existence of the salt marsh ecosystem depends on the topographic evolution of marsh platforms. Quantifying marsh platform topography is vital for improving the management of these valuable landscapes. The determination of platform boundaries currently relies on supervised classification methods requiring near-infrared data to detect vegetation, or demands labourintensive field surveys and digitisation. We propose a novel, unsupervised method to reproducibly isolate salt marsh scarps and platforms from a digital elevation model (DEM), referred to as Topographic Identification of Platforms (TIP). Field observations and numerical models show that salt marshes mature into subhorizontal platforms delineated by subvertical scarps. Based on this premise, we identify scarps as lines of local maxima on a slope raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. We test the TIP method using lidar-derived DEMs from six salt marshes in England with varying tidal ranges and geometries, for which topographic platforms were manually isolated from tidal flats. Agreement between manual and unsupervised classification exceeds 94 % for DEM resolutions of 1 m, with all but one site maintaining an accuracy superior to 90 % for resolutions up to 3 m. For resolutions of 1 m, platforms detected with the TIP method are comparable in surface area to digitised platforms and have similar elevation distributions. We also find that our method allows for the accurate detection of local block failures as small as 3 times the DEM resolution. Detailed inspection reveals that although tidal creeks were digitised as part of the marsh platform, unsupervised classification categorises them as part of the tidal flat, causing an increase in false negatives and overall platform perimeter. This suggests our method may benefit from combination with existing creek detection algorithms. Fallen blocks and high tidal flat portions, associated with potential pioneer zones, can also lead to differences between our method and supervised mapping. Although pioneer zones prove difficult to classify using a topographic method, we suggest that these transition areas should be considered when analysing erosion and accretion processes, particularly in the case of incipient marsh platforms. Ultimately, we have shown that unsupervised classification of marsh platforms from high-resolution topography is possible and sufficient to monitor and analyse topographic evolution.

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“…A forested area that has a dynamic slope and a sharp relief are difficult to filter where LiDAR point outliers might cause errors to the returning pulses. These points have unusually high or low elevation values and must be eliminated during data preprocessing (Meng et al, 2010;Wang et al, 2009). The selection of LiDAR first or last returns during ground filtering have also been debated.…”

Section: Height Estimation and Tree Detection Based On Plot Level Assmentioning

confidence: 99%

Accuracy of LiDAR-based tree height estimation and crown recognition in a subtropical evergreen broad-leaved forest in Okinawa, Japan

Zawawi¹,

Shiba

Jemali

2015

For. syst.

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Aim of study: To present an approach for estimating tree heights, stand density and crown patches using LiDAR data in a subtropical broad-leaved forest.Area of study: The study was conducted within the Yambaru subtropical evergreen broad-leaved forest, Okinawa main island, Japan.Materials and methods: A digital canopy height model (CHM) was extracted from the LiDAR data for tree height estimation and a watershed segmentation method was applied for the individual crown delineation. Dominant tree canopy layers were estimated using multi-scale filtering and local maxima detection. The LiDAR estimation results were then compared to the ground inventory data and a high resolution orthophoto image for accuracy assessment. Main results: A Wilcoxon matched pair test suggests that LiDAR data is highly capable of estimating tree height in a subtropical forest (z = 4.0, p = 0.345), but has limitation to detect small understory trees and a single tree delineation. The results show that there is a statistically significant different type of crown detection from LiDAR data over forest inventory (z = 0, p = 0.043). We also found that LiDAR computation results underestimated the stand density and overestimated the crown size.Research highlights: Most studies involving crown detection and tree height estimation have focused on the analysis of plantations, boreal forests and temperate forests, and less was conducted on tropical and/or subtropical forests. Our study tested the capability of LiDAR as an effective application for analyzing a highly dense forest.Key words: Broad-leaved; inventory; LiDAR; subtropical; tree height.Abbreviations: DBH: Diameter at Breast Height, CHM: Canopy Height Model, DEM: Digital Elevation Model, DSM: Digital Surface Model, LiDAR: Light Detection and Ranging, YFA: Yambaru Forest Area.

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Separation of Ground and Low Vegetation Signatures in LiDAR Measurements of Salt-Marsh Environments

Cited by 103 publications

References 28 publications

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

Unsupervised detection of salt marsh platforms: a topographic method

Accuracy of LiDAR-based tree height estimation and crown recognition in a subtropical evergreen broad-leaved forest in Okinawa, Japan

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