| 来源类型 | Article |
| 规范类型 | 其他 |
| DOI | 10.1088/1748-9326/aa8352 |
| Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine. | |
| Bilous A; Myroniuk V; Holiaka D; Bilous S; See L; Schepaschenko D | |
| 发表日期 | 2017 |
| 出处 | Environmental Research Letters 12 (10): e105001 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | Forest inventory and biomass mapping are important tasks that require inputs from multiple data sources. In this paper we implement two methods for the Ukrainian region of Polissya: random forest (RF) for tree species prediction and k-nearest neighbors (k-NN) for growing stock volume and biomass mapping. We examined the suitability of the five-band RapidEye satellite image to predict the distribution of six tree species. The accuracy of RF is quite high: ~99% for forest/non-forest mask and 89% for tree species prediction. Our results demonstrate that inclusion of elevation as a predictor variable in the RF model improved the performance of tree species classification. We evaluated different distance metrics for the k-NN method, including Euclidean or Mahalanobis distance, most similar neighbor (MSN), gradient nearest neighbor, and independent component analysis. The MSN with the four nearest neighbors (k = 4) is the most precise (according to the root-mean-square deviation) for predicting forest attributes across the study area. The k-NN method allowed us to estimate growing stock volume with an accuracy of 3 m3 ha−1 and for live biomass of about 2 t ha−1 over the study area. |
| 主题 | Ecosystems Services and Management (ESM) |
| URL | http://pure.iiasa.ac.at/id/eprint/14855/ |
| 来源智库 | International Institute for Applied Systems Analysis (Austria) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/130863 |
| 推荐引用方式 GB/T 7714 | Bilous A,Myroniuk V,Holiaka D,et al. Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine.. 2017. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Bilous_2017_Environ.（2653KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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