Assessing Forest Species Diversity in Ghana's Tropical Forest Using PlanetScope Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F24%3A100551" target="_blank" >RIV/60460709:41320/24:100551 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3390/rs16030463" target="_blank" >http://dx.doi.org/10.3390/rs16030463</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs16030463" target="_blank" >10.3390/rs16030463</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Assessing Forest Species Diversity in Ghana's Tropical Forest Using PlanetScope Data

Popis výsledku v původním jazyce

This study utilized a remotely sensed dataset with a high spatial resolution of 3 m to predict species diversity in the Bobiri Forest Reserve (BFR), a moist semi-deciduous tropical forest in Ghana. We conducted a field campaign of tree species measurements to achieve this objective for species diversity estimation. Thirty-five field plots of 50 m x 20 m were established, and the most dominant tree species within the forest were identified. Other measurements, such as diameter at breast height (DBH >= 5 cm), tree height, and each plot's GPS coordinates, were recorded. The following species diversity indices were estimated from the field measurements: Shannon-Wiener (H '), Simpson diversity index (D2), species richness (S), and species evenness (J '). The PlanetScope surface reflectance data at 3 m spatial resolution was acquired and preprocessed for species diversity prediction. The spectral/pixel information of all bands, except the coastal band, was extracted for further processing. Vegetation indices (VIs) (NDVI-normalized difference vegetation index, EVI-enhanced vegetation index, SRI-simple ratio index, SAVI-soil adjusted vegetation index, and NDRE-normalized difference red edge index) were also calculated from the spectral bands and their pixel value extracted. A correlation analysis was then performed between the spectral bands and VIs with the species diversity index. The results showed that spectral bands 6 (red) and 2 (blue) significantly correlated with the two main species diversity indices (S and H ') due to their influence on vegetation properties, such as canopy biomass and leaf chlorophyll content. Furthermore, we conducted a stepwise regression analysis to investigate the most important spectral bands to consider when estimating species diversity from the PlanetScope satellite data. Like the correlation results, bands 6 (red) and 2 (blue) were the most important bands to be considered for predicting species diversity. The model equations from the stepwise regression were used to predict tree species diversity. Overall, the study's findings emphasize the relevance of remotely sensed data in assessing the ecological condition of protected areas, a tool for decision-making in biodiversity conservation.

Název v anglickém jazyce

Assessing Forest Species Diversity in Ghana's Tropical Forest Using PlanetScope Data

Popis výsledku anglicky

This study utilized a remotely sensed dataset with a high spatial resolution of 3 m to predict species diversity in the Bobiri Forest Reserve (BFR), a moist semi-deciduous tropical forest in Ghana. We conducted a field campaign of tree species measurements to achieve this objective for species diversity estimation. Thirty-five field plots of 50 m x 20 m were established, and the most dominant tree species within the forest were identified. Other measurements, such as diameter at breast height (DBH >= 5 cm), tree height, and each plot's GPS coordinates, were recorded. The following species diversity indices were estimated from the field measurements: Shannon-Wiener (H '), Simpson diversity index (D2), species richness (S), and species evenness (J '). The PlanetScope surface reflectance data at 3 m spatial resolution was acquired and preprocessed for species diversity prediction. The spectral/pixel information of all bands, except the coastal band, was extracted for further processing. Vegetation indices (VIs) (NDVI-normalized difference vegetation index, EVI-enhanced vegetation index, SRI-simple ratio index, SAVI-soil adjusted vegetation index, and NDRE-normalized difference red edge index) were also calculated from the spectral bands and their pixel value extracted. A correlation analysis was then performed between the spectral bands and VIs with the species diversity index. The results showed that spectral bands 6 (red) and 2 (blue) significantly correlated with the two main species diversity indices (S and H ') due to their influence on vegetation properties, such as canopy biomass and leaf chlorophyll content. Furthermore, we conducted a stepwise regression analysis to investigate the most important spectral bands to consider when estimating species diversity from the PlanetScope satellite data. Like the correlation results, bands 6 (red) and 2 (blue) were the most important bands to be considered for predicting species diversity. The model equations from the stepwise regression were used to predict tree species diversity. Overall, the study's findings emphasize the relevance of remotely sensed data in assessing the ecological condition of protected areas, a tool for decision-making in biodiversity conservation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10600 - Biological sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Remote Sensing

ISSN

2072-4292

e-ISSN

2072-4292

Svazek periodika

16

Číslo periodika v rámci svazku

3.0

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

001159943700001

EID výsledku v databázi Scopus

2-s2.0-85184718685