4 research outputs found
Identification of wet areas in forest using remote sensing data
ArticleAim of this study is to evaluate different remote sensing indices to detect spatial
distribution of wet soils using GIS based algorithms. Ar
ea of this study represents different soil
types on various quaternary deposits as well as different forest types. We analyzed 25 sites with
the area of 1 km
2
each in central and western part of Latvia. Data about soil characteristics like
thickness of pea
t layer and presence of reductimorphic colors in soil was collected during field
surveys in 228 random points within study sites. ANOVA test for comparing means of different
soil wetness classes and binary logistic regression analysis for evaluating the ac
curacy of different
remote sensing indices to model spatial distribution of wet areas are used for analysis. Main
conclusion of this study is that for different quaternary deposits and soil texture classes different
algorithms for soil wetness prediction s
hould be used. Data layers for predicting soil wetness in
this study are various modifications and resolutions of digital elevation model like depressions,
slope and SAGA wetness index as well as Sentinel
-
2 multispectral satellite imagery. Accuracy of
soil
wetness classification of soils on moraine, fluvial and eolian sediments exceeds 94%, whereas
on the clayey sediments it is close to 80%
GHG balance in drained organic forest soils – data revisited
The study is part of the SNS-120 project ‘Anthropogenic greenhouse gas emissions from organic forest soils: improved inventories and implications for sustainable management’ funded by Nordic Forest Research. http://dev.nordicforestresearch.org/sns-120/201
Correction to: Harmonised projections of future forest resources in Europe (Annals of Forest Science, (2019), 76, 3, (79), 10.1007/s13595-019-0863-6)
The original article was erroneously published without applying all the provided proof corrections in Section 5 and Table 1. © 2019, INRA and Springer-Verlag France SAS, part of Springer Nature