Spatial predictions of the morpho-ecological state of Finnish palsa mires

Spatial predictions of the probability of a good morpho-ecological state are provided for Finnish palsa mires as a TIFF file with a 10 m resolution, using the EUREF FIN TM35FIN coordinate system.

These predictions were produced through spatial modeling that combined classified point data on the state of Finnish palsa mires (Ruuhijärvi et al., 2022) with high-resolution (10 m) environmental datasets. The predictions were computed for the extent of palsa mires (Tammilehto et al., 2024).  Modelling was conducted in mgcv package (version 1.9.0; Wood, 2011) in R (version 4.3.2; R Core Team 2023). The predictions were developed during the preparation of the manuscript: "The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling" (Leppiniemi et al., 2024, in-review).

References:

Leppiniemi, O., Karjalainen, O., Aalto, J., Yletyinen., E., Luoto, M., & Hjort, J. 2024. The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling. (In-review).

R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (accessed 14 October 2024).

Ruuhijärvi, R., Salminen, P., & Tuominen, S., 2022. Distribution range, morphological types, and state of palsa mires in Finland in the 2010s. Suo 73, 1–32. (In Finnish with English summary).

Tammilehto, A., Härmä, P., Kallio, M., Törmä, M., Saikkonen, A., Tuominen, S., Impiö, M., Heikkinen, M., Kervinen, M., Jussila, T., Böttcher, K., Pääkkö, E., Kokko, A., Mäkelä, K., & Anttila, S., 2024. Ylä-Lapin luonnon kaukokartoitus – Projektin loppuraportti osa 1 – Aineistot ja menetelmät. Vantaa. (In Finnish).

Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Series B Stat. Methodol. 73, 3–36. https://doi.org/10.1111/J.1467-9868.2010.00749.X

本数据集以分辨率为10米的TIFF（标签图像文件格式，Tagged Image File Format）文件形式，提供了芬兰冰核泥炭丘沼泽（palsa mires）的良好形态生态状态概率空间预测结果，采用EUREF FIN TM35FIN坐标系。

该预测结果通过空间建模生成，将芬兰冰核泥炭丘沼泽状态的分类点数据（Ruuhijärvi等，2022）与高分辨率（10米）环境数据集相结合。预测范围限定于冰核泥炭丘沼泽的分布区域（Tammilehto等，2024）。建模工作基于R语言（版本4.3.2；R核心团队，2023）中的mgcv包（版本1.9.0；Wood，2011）完成。本预测结果是在撰写论文"《亚北极芬诺斯坎迪亚冰核泥炭丘沼泽的形态生态状态：高分辨率空间建模的启示》"（Leppiniemi等，2024，待刊）期间开发的。

参考文献：
1. Leppiniemi, O., Karjalainen, O., Aalto, J., Yletyinen, E., Luoto, M., & Hjort, J. 2024. The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling.（待刊）
2. R核心团队（2023）. R：统计计算语言与环境. 奥地利维也纳：R统计计算基金会. https://www.R-project.org/（2024年10月14日访问）
3. Ruuhijärvi, R., Salminen, P., & Tuominen, S., 2022. 2010年代芬兰冰核泥炭丘沼泽的分布范围、形态类型与状态. Suo 73, 1–32.（含英文摘要）
4. Tammilehto, A., Härmä, P., Kallio, M., Törmä, M., Saikkonen, A., Tuominen, S., Impiö, M., Heikkinen, M., Kervinen, M., Jussila, T., Böttcher, K., Pääkkö, E., Kokko, A., Mäkelä, K., & Anttila, S., 2024. 上拉普兰自然远程制图——项目最终报告第一部分：数据与方法. 万塔.（芬兰语撰写）
5. Wood, S.N., 2011. 半参数广义线性模型的快速稳定受限极大似然与边际似然估计. 皇家统计学会B辑：统计方法学，73卷，3–36页. https://doi.org/10.1111/J.1467-9868.2010.00749.X