Height variation hypothesis: A new approach for estimating forest species diversity with CHM LiDAR data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F20%3A82301" target="_blank" >RIV/60460709:41330/20:82301 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1470160X2030457X?dgcid=rss_sd_all" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1470160X2030457X?dgcid=rss_sd_all</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecolind.2020.106520" target="_blank" >10.1016/j.ecolind.2020.106520</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Height variation hypothesis: A new approach for estimating forest species diversity with CHM LiDAR data

Popis výsledku v původním jazyce

An indirect method for estimating biodiversity from Earth observations is the Spectral Variation Hypothesis SVH. SVH states that the higher the spatial variability of the spectral response of an optical remotely sensed image, the higher the number of available ecological niches and hence, the higher the diversity of tree species in the considered area. Here for the first time we apply the concept of the SVH to Light Detection and Ranging LiDAR data to understand the relationship between the height heterogeneity HH of a forest and its tree species diversity, a concept we have named the Height Variation Hypothesis HVH. We tested HVH in two different European forest types a coniferous mountain forest in the eastern Italian Alps and a mixed temperate forest in southern Germany. We used the heterogeneity index Raos Q to estimate HH using a Canopy Height ModelCHM at different resolutions derived from LiDAR data, and linear regression models and relation analysis to assess the relationships between HH and t

Název v anglickém jazyce

Height variation hypothesis: A new approach for estimating forest species diversity with CHM LiDAR data

Popis výsledku anglicky

An indirect method for estimating biodiversity from Earth observations is the Spectral Variation Hypothesis SVH. SVH states that the higher the spatial variability of the spectral response of an optical remotely sensed image, the higher the number of available ecological niches and hence, the higher the diversity of tree species in the considered area. Here for the first time we apply the concept of the SVH to Light Detection and Ranging LiDAR data to understand the relationship between the height heterogeneity HH of a forest and its tree species diversity, a concept we have named the Height Variation Hypothesis HVH. We tested HVH in two different European forest types a coniferous mountain forest in the eastern Italian Alps and a mixed temperate forest in southern Germany. We used the heterogeneity index Raos Q to estimate HH using a Canopy Height ModelCHM at different resolutions derived from LiDAR data, and linear regression models and relation analysis to assess the relationships between HH and t

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ECOLOGICAL INDICATORS

ISSN

1470-160X

e-ISSN

1872-7034

Svazek periodika

2020

Číslo periodika v rámci svazku

117

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

000555557300010

EID výsledku v databázi Scopus

2-s2.0-85085268487