Remote Sensing of Environment

Volume 247 (5~6 / 55)

15 September 2020

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• 4. Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach (AR)

  ★★★☆☆

  • Intro

    • Context

      Accurate information on fire effects is critical to understanding post-fire ecological processes & designing appropriate land management strategies

      Multispectral imagery from optical passive sensors → commonly used to estimate fire damage, yet this type of data is only sensitive to the effects in the upper canopy

    • Objectives

      Evaluates the sensitivity of full waveform LiDAR data to estimate the severity of wildfires using a 3D radiative transfer model approach

  • M&M

    The FLIGHT 3D radiative transfer model employed to simulate full waveform data

    10 plots along with a wide range of post-fire scenarios characterized by different severity levels (the composite burn index, CBI)

    A new metric (=the waveform area relative change, WARC) proposed → provides a comprehensive severity assessment considering all strata and accounting for changes in structure and leaf and soil color.

  • Results & Contributions

    First attempt to evaluate the effect of different fire impacts, i.e. changes in vegetation structure as well as soil and leaf color, on the LiDAR signal

    New metric (WARC) showed a strong correlation with CBI values (Spearman's Rho = 0.9 ± 0.02), outperforming the relative change of LiDAR metrics commonly applied for vegetation modeling, such as the relative height of energy quantiles (Spearman's Rho = 0.56 ± 0.07, for the relative change of RH60, the second strongest correlation)

    Logarithmic models fitted for each plot based on the WARC yielded very good performance with R2 (± standard deviation) and RMSE (± standard deviation) of 0.8 (± 0.05) and 0.22 (± 0.03), respectively.

    Pseudo-waveforms were computed after radiometric normalization of the intensity data. The WARC showed again the strongest correlation with field measures of GeoCBI values (Spearman's Rho = 0.91), closely followed by the relative change of RH40 (Spearman's Rho = 0.89).

 

• 5. Improved offset tracking for predisaster deformation monitoring of the 2018 Jinsha River landslide (Tibet, China) (AR)

  ★★☆☆☆

  • Intro

    • Context

      Synthetic aperture radar (SAR) remote sensing is a potential technique for long-term monitoring of landslide-prone areas.

      Pixel offset tracking methods work well for fast-moving landslides (more than tens of cm/yr).

      However, existing methods may present some limitations of : (i) a high dependence of estimation window design on experience, (ii) a tradeoff between the accuracy of single points and the overall efficiency, and (iii) a low confidence in the results caused by heterogeneous in-window samples.

    • Objectives

      In this paper, an improved offset tracking method is proposed to address these problems.

       

  • M&M

    Workflow is optimized by a “preseparation” step added before offset estimation to distinguish between feature matching and speckle pattern matching.

    The optimized workflow is more efficient for natural scenes containing both feature and non-saliency regions.

    Second, an improved algorithm called adaptive incoherence speckle offset tracking based on homogeneous samples (AISOT-HS) is proposed for non-saliency regions. Its two key points are (i) adaptive design of the optimal estimation windows by introducing a coherence map as a guide and (ii) offset estimation without heterogeneous samples.

    SAR data from the Gaofen 3 (GF-3) satellite

    Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) system onboard (the Advanced Land Observing Satellite (ALOS) satellite)

     

  • Results & Contributions

    We also analyze the spatiotemporal displacement pattern of this landslide, which shows that the Jinsha River landslide was most likely a thrust load-caused landslide.

    Compared with the traditional method, the proposed method improves the efficiency and reduces the uncertainty.

    We also analyze the spatiotemporal displacement pattern of this landslide, which shows that the Jinsha River landslide was most likely a thrust load-caused landslide.

    This study demonstrates the role of SAR remote sensing in global landslide monitoring, especially where ground truth data are scarce.

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• TIL

  • Logarithmic models

  • Abstract 결과 부분에 R2, RMSE 같은 수치들을 직접적으로 언급하기. 단순히 "improved 되었다" 처럼 불친절하게 쓰지 말 것.