A New Method to Compute Canopy Height Using Waveform LiDAR

Canopy Height Estimation by Characterizing Waveform LiDAR Geometry Based on Shape-Distance Metric
by: Eric Ariel L. Salas, and Geoffrey M. Henebry

There have been few approaches developed for the estimation of height using waveform LiDAR data. Unlike any existing methods, we illustrate how the new Moment Distance (MD) framework can characterize the canopy height based on the geometry and return power of the LiDAR waveform without having to go through curve modeling processes. Our approach offers the possibilities of using the raw waveform data to capture vital information from the variety of complex waveform shapes in LiDAR. We assess the relationship of the MD metrics to the key waveform landmarks—such as locations of peaks, power of returns, canopy heights, and height metrics—using synthetic data and real Laser Vegetation Imaging Sensor (LVIS) data. In order to verify the utility of the new approach, we use field measurements obtained through the DESDynI (Deformation, Ecosystem Structure and Dynamics of Ice) campaign. Our results reveal that the MDI can capture temporal dynamics of canopy and segregate generations of stands based on curve shapes.
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How To Calculate MDI or Compute Moment Distance Index

The Moment Distance Index or MDI is a shape-based metric that is calculated with a matrix of distances. So far, MDI was used to analyze the spectral regions of chlorophyll and carotenoids to separate maize from soybean and other advance spectral analysis for vegetation studies.

Since MDI looks at the shape of the curve, it was used to exploit the spectral bands of Landsat for mapping sparse vegetation. More recently, MDI was utilized in an object-based image analysis (OBIA) and came out a very important variable for mapping summer vegetation and even greenhouses.

The application of MDI on shape analysis can be many. Recently, we applied MDI to LiDAR waveforms. Our results using LVIS dataset showed a promise.

So how can you compute the Moment Distance Index? See the steps below as I explain the Moment Distance (MD) framework.
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FREE: Open-Access Geographic Data for the Argali Habitat in the Southeastern Tajik Pamirs

Open-Access Geographic Data for the Argali Habitat in the Southeastern Tajik Pamirs
by Eric Ariel L. Salas, Raul Valdez, and Kenneth G. Boykin

Seven Geographic Information System (GIS) layers comprise this dataset intended for understanding the Marco Polo argali habitat in the southeastern Tajikistan Pamirs (37°33′ N, 74°09′ E). Extensive remote sensing habitat data processing and field data analysis of the Marco Polo sheep study area have yielded these layers that are now available online to download and for use by other researchers interested in studying the argali patterns and habitat suitability in the southeastern Tajik Pamirs. It is important to note that the layers were generated using a 30-m Landsat ETM image and field data from 2012.
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