Fusion of lidar and multispectral data to quantify salt marsh carbon stocks

Kulawardhana, R. W.; Popescu, Sorin C.; Feagin, Rusty A.

doi:10.1016/j.rse.2013.10.036

Cited by 59 publications

(44 citation statements)

References 72 publications

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“…Findings of previous studies indicated that the accuracy of LiDAR-derived DTM is largely determined by: (1) vegetation canopy structure characteristics [33]; (2) terrain, mainly including slope and terrain irregularities; and (3) sensor characteristics, such as laser point density. Thus, the accuracy of LiDAR-derived DTM generally showed significant difference in different environments [31,37]. Our findings revealed that the accuracy of LiDAR-derived DTM is high in our study area.…”

Section: Discussionmentioning

confidence: 53%

“…In this case, the fusion of LiDAR and hyperspectral data appears to be an interesting option for better estimating vegetation biomass. The previous studies have demonstrated the capability of fused hyperspectral and LiDAR data for providing robust vegetation biomass estimates in forest ecosystems [25,29,[34][35][36], even in herbaceous environments [22,37]. However, there is no literature that has focused on the study of crop biomass estimation through the fusion of hyperspectral and LiDAR data.…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Wang

Nie

et al. 2016

Remote Sensing

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Abstract:The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (H CV ) of digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the H CV combined with H mean performed better than one LiDAR-derived metric alone in biomass estimation (R 2 = 0.835, RMSE = 374.655 g/m 2 , RMSE CV = 393.573 g/m 2 ). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R 2 = 0.883, RMSE = 321.092 g/m 2 , RMSE CV = 337.653 g/m 2 ) compared with LiDAR or hyperspectral data alone.

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Section: Discussionmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Wang

Nie

et al. 2016

Remote Sensing

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“…However, the dimensions of filtering window determine the levels of exclusion of lidar data and smoothing, and therefore can be a key determinant of the accuracies of the derived DEMs. For example, significant differences are reported in the RMSEs in elevation estimates derived using different window sizes in coastal marshes (Kulawardhana et al 2014), grasslands (Ritchie et al 1996), croplands (Coby et al 2001), and shrub lands (Struetker & Glenn 2006). Some of these findings revealed increasing challenges in short stature environments and indicated that even last lidar returns filtered using the smallest window size were returning from inside vegetation rather than from the ground surface (Kulawardhana et al 2014).…”

Section: Methods and Algorithmsmentioning

confidence: 99%

“…For example, significant differences are reported in the RMSEs in elevation estimates derived using different window sizes in coastal marshes (Kulawardhana et al 2014), grasslands (Ritchie et al 1996), croplands (Coby et al 2001), and shrub lands (Struetker & Glenn 2006). Some of these findings revealed increasing challenges in short stature environments and indicated that even last lidar returns filtered using the smallest window size were returning from inside vegetation rather than from the ground surface (Kulawardhana et al 2014). This type of laser-vegetation interactions often results in collection of lidar point elevations that closely resemble a flat surface consistent with the ground surface (Gopfert & Heipke 2006), more specifically in dense vegetation cover.…”

Section: Methods and Algorithmsmentioning

confidence: 99%

Airborne lidar remote sensing applications in non-forested short stature environments: a review

2017

Self Cite

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Abstract. Lidar (light detection and ranging) remote sensing technology provides promising tools for 3D characterization of the earth's surface. In ecosystem studies, lidar derived structural parameters relating to vegetation and terrain have been extensively used in many applications and are rapidly expanding. Yet, most of the lidar applications have focused on tall, woody vegetation in forested environments and less research attention is given to non-forest, short stature vegetation dominated ecosystems. Similar to the lidar developments in forestry, novel methodological approaches and algorithm developments will be necessary to improve estimates of structural and biophysical properties (i.e. biomass and carbon storage) in non-forested short stature environments. Under changing climate scenarios, the latter is particularly useful to improve our understanding of their future role as terrestrial carbon sinks. In an attempt to identify research gaps in airborne lidar remote sensing application in short stature vegetation studies, in this review article we provide a comprehensive overview on the current state of airborne lidar applications. Our focus is mainly on the levels of accuracies and errors reported, as well as the potentials and limitations of the methods applied in these studies. We also provide insights into future research needs and applications in these environments.

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“…Accuracy tests used correlation coefficient (r) and Root Mean Square Error (RMSE) [14], [15], [16], [17], [5]. Equation :…”

Section: Test Accuracy With Rmsementioning

confidence: 99%

Estimation of Mangrove Carbon Stock With Hybrid Method Using Image Sentinel-2

Muhsoni¹

2018

GEOMATE

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ABSTRACT:Field survey data combined with remote sensing data were an ideal and practical method for estimating carbon stocks. The objective of this research was to get an estimation model of mangrove carbon stock with good accuracy. Modeling used hybrid methods, by combining satellite image analysis and field data. The result of this research was to get the mangrove carbon estimation model. Model 1 merging between NNIP vegetation index equation using regression of power/geometry and six variables multiple regression (NDRE or WVVI vegetation index, sediment depth, soil density,% C soil depth 0-15 cm, 15-50 cm and >50 cm). RMSE test resulted 0.4778 t 100 m -2 and % RMSE 16.12%. Model 2 NNIP vegetation index and three variable regression (VIRRE vegetation index, sediment depth, soil density). RMSE test resulted 0.5639 t 100 m -2 and % RMSE 19.03%. Model 3 uses NNIP vegetation index and two variable regression (NDRE vegetation index and sediment depth). RMSE test resulted 0.7295 t 100 m -2 and RMSE % 24.63%. Model 4 incorporation of NNIP vegetation index and multiple regression of 3 variables (VIRRE vegetation index, average sediment depth value 100.63 cm, soil density value 1.02 g cm -3 ). RMSE test resulted 1.0043 t 100 m -2 and % RMSE 33.89%.

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Fusion of lidar and multispectral data to quantify salt marsh carbon stocks

Cited by 59 publications

References 72 publications

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Airborne lidar remote sensing applications in non-forested short stature environments: a review

Estimation of Mangrove Carbon Stock With Hybrid Method Using Image Sentinel-2

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