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