Modern Data Acquisition for Forest Inventories

Holopainen, Markus; Kalliovirta, Jouni

doi:10.1007/1-4020-4381-3_21

Cited by 9 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Incorporating structural data in resemblance assessments would allow the analyst to exploit valuable information obtained during field surveys (e.g. Holopainen & Kalliovirta ). If available, there is no reason to ignore the wealth of information regarding size when measuring community resemblance.…”

Section: Introductionmentioning

confidence: 99%

Dissimilarity measurements and the size structure of ecological communities

Cáceres

Legendre

2013

Methods Ecol Evol

View full text Add to dashboard Cite

Summary1. Measurements of community resemblance in ecology are often based on species composition, and the starting point for calculations is usually a site-by-species data table. However, resemblance measurements may not be sufficiently accurate when communities are described using species composition only. Characteristics such as the size of their constituting organisms are also important to understand community organization. 2. Here, we provide a framework that generalizes conventional resemblance measurements by incorporating the size structure of the compared communities. We first introduce the concept of cumulative abundance profile, which generalizes traditional species abundance values, and describe how to calculate it. We then explain our approach to compare cumulative abundance profiles in community resemblance measurements and use a small simulation study to determine which resemblance coefficients appropriately deal with compositional and structural differences. After that, we present an illustrative example where we study the structural and compositional variation between and within six Douglas-fir forest plots in British Columbia, Canada. 3. According to our investigations, the generalizations we suggest for the percentage difference (alias Bray-Curtis dissimilarity) and the Ru zi cka coefficients are appropriate to measure community resemblance in terms of size structure, species composition or both. 4. Our framework allows community resemblance to be measured in terms of either size structure or species composition, or both. A broad range of applications is expected. In the case of terrestrial plant communities, potential applications include analyses of community dynamics and classification of vegetation.

show abstract

Section: Introductionmentioning

confidence: 99%

Dissimilarity measurements and the size structure of ecological communities

Cáceres

Legendre

2013

Methods Ecol Evol

View full text Add to dashboard Cite

show abstract

“…Amongst other modern forest inventory methods (Holopainen & Kalliovirta 2006), total stations combining electronic theodolite, electronic distance measuring device and computer with specialized software have recently been implemented. It enables spatially exact determination of the position of e.g.…”

mentioning

confidence: 99%

A new technique for inventory of permanent plots in tropical forests: a case study from lowland dipterocarp forest in Kuala Belalong, Brunei Darussalam

Hédl¹,

Svátek²,

Dančák³

et al. 2009

blum - j plant tax and plant geog

View full text Add to dashboard Cite

“…However, data collection by conventional ground measurements is the most expensive, time-consuming, and labor-intensive stage in the forest planning process (Trotter et al, 1997;Eler, 2001;Teremit et al, 2005;Demirel and Özkan, 2018;Kanja et al, 2019). Therefore, terrestrial photogrammetry and optic remote sensing (RS) products such as multicameras, aerial photos, and satellite images have been widely utilized for estimations of forests' structural characteristics in combination with ground measurements both in Turkey (Demirel and Özkan, 2018;Günlü and Kadıoğulları, 2018;Çil et al, 2015;Bulut et al, 2016;Kanja et al, 2019;Sakici and Günlü, 2018;Yilmaz and Güngör, 2019) and in the world (Ozdemir and Karnieli, 2011;Holopainen and Kalliovirta, 2006;Forsman et al, 2016;Surovy et al, 2016;Ucar et al, 2018). Nevertheless, the estimations based on optic RS hardly meet the requirements for accuracy in FI studies compared with conventional ground measurements (Holopainen and Kalliovirta, 2006;Sefercik and Atesoglu, 2017).…”

Section: Introductionmentioning

confidence: 99%

Application of handheld laser scanning technology for forest inventory purposes in the NE Turkey

Vatandaşlar¹,

Zeybek²

2020

Turk J Agric For

View full text Add to dashboard Cite

Forest inventory (FI) is the most challenging stage of forest management and planning process. Therefore, in situ surveys are often reinforced by modern remote sensing (RS) methods for collecting forestry-related data more efficiently. This study tests a stateof-the-art data collection method for practical use in the Turkish FI system for the first time. To this end, forest sampling plots were conventionally measured to collect dendrometric data from 437 trees in Artvin and Saçınka Forest Enterprises. Then, each plot was scanned using a handheld mobile laser scanning (HMLS) instrument. Finally, HMLS data were compared against ground measurements via basic FI measures. Based on statistical tests, no apparent differences were found between the two datasets at the plot level (P < 0.05). There were also robust correlations for diameter breast height at individual tree level (r > 0.97; P < 0.01). Residual analysis showed that both positive and negative errors had a homogeneous distribution, except for plot 8 where tree stems were in irregular shapes due to anthropogenic pressures. When all plots' data were aggregated, average values for the number of trees, basal area, and timber volume were estimated as 535 trees/ha-1 , 49.6 m 2 /ha-1 , and 499.7 m 3 /ha-1 , respectively. Furthermore, secondary measures such as the number of saplings and slope were successfully retrieved using HMLS method. The highest overestimation was in timber volume with less than 10% difference at the landscape level. The differences were attributed to poor data quality of conventional measurements, as well as marginal site conditions in some plots. We concluded that the HMLS method met the accuracy standards for most FI measures, except for stand height. Thus, the Turkish FI system could benefit from this novel technology, which in turn supports the implementation of sound forest management and planning.

show abstract

Modern Data Acquisition for Forest Inventories

Cited by 9 publications

References 39 publications

Dissimilarity measurements and the size structure of ecological communities

Dissimilarity measurements and the size structure of ecological communities

A new technique for inventory of permanent plots in tropical forests: a case study from lowland dipterocarp forest in Kuala Belalong, Brunei Darussalam

Application of handheld laser scanning technology for forest inventory purposes in the NE Turkey

Contact Info

Product

Resources

About