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LiDAR derived topography and forest stand characteristics largely explain the spatial variability observed in MODIS land surface phenology

Misra, Gourav and Buras, Allan and Heurich, Marco and Asam, Sarah and Menzel, Annette (2018) LiDAR derived topography and forest stand characteristics largely explain the spatial variability observed in MODIS land surface phenology. Remote Sensing of Environment, 218, pp. 231-244. Elsevier. DOI: 10.1016/j.rse.2018.09.027 ISSN 0034-4257

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Official URL: https://www.sciencedirect.com/science/article/abs/pii/S0034425718304462#!

Abstract

In the past, studies have successfully identified climatic controls on the temporal variability of the land surface phenology (LSP). Yet we lack a deeper understanding of the spatial variability observed in LSP within a land cover type and the factors that control it. Here we make use of a high resolution LiDAR based dataset to study the effect of subpixel forest stand characteristics on the spatial variability of LSP metrics based on MODIS NDVI. Multiple linear regression techniques (MLR) were applied on forest stand information and topography derived from LiDAR as well as land cover information (i.e. CORINE and proprietary habitat maps for the year 2012) to predict average LSP metrics of the mountainous Bavarian Forest National Park, Germany. Six different LSP metrics, i.e. start of season (SOS), end of season (EOS), length of season (LOS), NDVI integrated over the growing season (NDVIsum), maximum NDVI value (NDVImax) and day of maximum NDVI (maxDOY) were modelled in this study. It was found that irrespective of the land cover, the mean SOS, LOS and NDVIsum were largely driven by elevation. However, inclusion of detailed forest stand information improved the models considerably. The EOS however was more complex to model, and the subpixel percentage of broadleaf forests and the slope of the terrain were found to be more strongly linked to EOS. The explained variance of the NDVImax improved from 0.45 to 0.71 when additionally considering land cover information, which further improved to 0.84 when including LiDAR based subpixelforest stand characteristics. Since completely homogenous pixels are rare in nature, our results suggest that incorporation of subpixel forest stand information along with land cover type leads to an improved performance of topography based LSP models. The novelty of this study lies in the use of topography, land cover and subpixel vegetation characteristics derived from LiDAR in a stepwise manner with increasing level of complexity, which demonstrates the importance of forest stand information on LSP at the pixel level.

Item URL in elib:https://elib.dlr.de/123051/
Document Type:Article
Title:LiDAR derived topography and forest stand characteristics largely explain the spatial variability observed in MODIS land surface phenology
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Misra, Gouravmisra (at) wzw.tum.deUNSPECIFIED
Buras, AllanUNSPECIFIEDUNSPECIFIED
Heurich, MarcoBavarian Forest National Park, Department of ResearchUNSPECIFIED
Asam, Sarahsarah.asam (at) dlr.dehttps://orcid.org/0000-0002-7302-6813
Menzel, Annetteamenzel (at) wzw.tum.deUNSPECIFIED
Date:1 December 2018
Journal or Publication Title:Remote Sensing of Environment
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:218
DOI :10.1016/j.rse.2018.09.027
Page Range:pp. 231-244
Publisher:Elsevier
ISSN:0034-4257
Status:Published
Keywords:NDVI LiDAR Land cover Phenology Modelling Spatial variability Forest stand characteristics Mountains Bavarian Forest National Park
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Asam, Dr. Sarah
Deposited On:13 Nov 2018 11:04
Last Modified:13 Nov 2018 11:04

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