Sammanfattning

Forest fragmentation has a relevant impact on biodiversity. An interesting alternative to estimate these indices is to use sampling data. This study aims to estimate aggregation index (AI) and the degree of clumping of forested landscape based on AI. The assessment was conducted using different point distances, inventory regions and cardinal directions. For this purpose, a dataset from one five-year periods (2007-2011) of the Swedish National Forest Inventory (NFI) was used. The estimation of AI from field-based inventory can give us a general picture of the current status of forest landscape. The results also show that the estimated AI is a distance dependent function. The corresponding estimated variance of the index is smaller for longer distances. The obtained results indicate that the estimated variance depends on both sample size and pair point distances. Estimated AI showed different values in different cardinal directions. To compare two regions or a given region over time, a given point distance should be used. The main advantage of the applied procedure is that a range of AI values can be produced rather than a single number. Furthermore, in field-based inventory, the obtained results are more reliable, because one works implicitly with a single forest definition only.

Nyckelord

NFI; sampling methods; forest landscape; environmental monitoring

Publicerad i

Scandinavian Journal of Forest Research
2021, Volym: 36, nummer: 4, sidor: 289-296
Utgivare: TAYLOR AND FRANCIS AS

SLU författare

• Ramezani, Habib

  • Institutionen för skoglig resurshushållning, Sveriges lantbruksuniversitet
    

Globala målen

Skydda, återställa och främja ett hållbart nyttjande av landbaserade ekosystem, hållbart bruka skogar, bekämpa ökenspridning, hejda och vrida tillbaka markförstöringen samt hejda förlusten av biologisk mångfald


UKÄ forskningsämne

Skogsvetenskap


Publikationens identifierare

DOI: https://doi.org/10.1080/02827581.2021.1908592

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/111557