用RapidEye和激光雷达描述新西兰Wairarapa地区小规模人工林的面积和产量

IF 1.5 4区 农林科学 Q2 FORESTRY
Cong Xu, B. Manley, J. Morgenroth
{"title":"用RapidEye和激光雷达描述新西兰Wairarapa地区小规模人工林的面积和产量","authors":"Cong Xu, B. Manley, J. Morgenroth","doi":"10.33494/nzjfs492019x16x","DOIUrl":null,"url":null,"abstract":"Background: New Zealand does not have a national forest inventory based on ground measurements. The National Exotic Forest Description (NEFD) is based on surveys of forest owners but lacks reliability in describing area and yield for small-scale forests, given that over half of the small-scale forests were not included in the direct NEFD survey. This has led to an insufficient understanding of the wood supply from these forests, which will raise problems as the majority of the small-scale forests are approaching maturity and are anticipated to contribute more than 40% of wood supply in the next decade. Therefore, having accurate estimation of this resources is critical for marketing, harvesting and logistic planning. Furthermore, the current NEFD does not incorporate a spatial representation. The aim of this study was to test the utility of remote sensing dataset for providing information on the area and yield of small-scale forest resources through a case study in the Wairarapa region of New Zealand. \nMethods: Classification and regression tree analysis was applied to both RapidEye and LiDAR datasets to map the net stocked area of small-scale plantation forests in Wairarapa. Results were compared against forest areas that have been determined from manual digitisation. For each forest, stand variables mean top height, basal area, volume and stand age were estimated using LiDAR-derived metrics. This allowed the development of maps for these stand variables. \nResults: The automatically mapped plantation area was 4.2% (1 614 ha) less than the reference area (i.e. manually digitised plantation area), together with manually digitised young plantations which were not detected from automated approach, the area differed only by -0.6% (235 ha) from the reference area. The yield tables developed using modelled stand variables were all within a realistic range and were comparable to wood availability forecast (WAF) yield tables, producing on average 27 m3 ha-1 less than the WAF yield. \nConclusions: The mapping approach produced comparable results to the area calculated using manual digitisation. However, this approach struggles to detect young plantings due to the resolution of the remote sensing datasets used; hence manual digitisation is required to map the young plantations that were within 3–5 years of planting. This study also confirmed that the remote sensing approach could be used to describe forest yield, although the approach failed to predict the full range of ages of the mapped forest resource. The estimation of plantation age could be improved by including plot data with a wider range of stand ages or investigating different models for age estimation. It is also possible to review time-series satellite imagery to detect establishment periods for the forests.","PeriodicalId":19172,"journal":{"name":"New Zealand Journal of Forestry Science","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2019-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Describing area and yield for small-scale plantation forests in Wairarapa region of New Zealand using RapidEye and LiDAR\",\"authors\":\"Cong Xu, B. Manley, J. Morgenroth\",\"doi\":\"10.33494/nzjfs492019x16x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: New Zealand does not have a national forest inventory based on ground measurements. The National Exotic Forest Description (NEFD) is based on surveys of forest owners but lacks reliability in describing area and yield for small-scale forests, given that over half of the small-scale forests were not included in the direct NEFD survey. This has led to an insufficient understanding of the wood supply from these forests, which will raise problems as the majority of the small-scale forests are approaching maturity and are anticipated to contribute more than 40% of wood supply in the next decade. Therefore, having accurate estimation of this resources is critical for marketing, harvesting and logistic planning. Furthermore, the current NEFD does not incorporate a spatial representation. The aim of this study was to test the utility of remote sensing dataset for providing information on the area and yield of small-scale forest resources through a case study in the Wairarapa region of New Zealand. \\nMethods: Classification and regression tree analysis was applied to both RapidEye and LiDAR datasets to map the net stocked area of small-scale plantation forests in Wairarapa. Results were compared against forest areas that have been determined from manual digitisation. For each forest, stand variables mean top height, basal area, volume and stand age were estimated using LiDAR-derived metrics. This allowed the development of maps for these stand variables. \\nResults: The automatically mapped plantation area was 4.2% (1 614 ha) less than the reference area (i.e. manually digitised plantation area), together with manually digitised young plantations which were not detected from automated approach, the area differed only by -0.6% (235 ha) from the reference area. The yield tables developed using modelled stand variables were all within a realistic range and were comparable to wood availability forecast (WAF) yield tables, producing on average 27 m3 ha-1 less than the WAF yield. \\nConclusions: The mapping approach produced comparable results to the area calculated using manual digitisation. However, this approach struggles to detect young plantings due to the resolution of the remote sensing datasets used; hence manual digitisation is required to map the young plantations that were within 3–5 years of planting. This study also confirmed that the remote sensing approach could be used to describe forest yield, although the approach failed to predict the full range of ages of the mapped forest resource. The estimation of plantation age could be improved by including plot data with a wider range of stand ages or investigating different models for age estimation. It is also possible to review time-series satellite imagery to detect establishment periods for the forests.\",\"PeriodicalId\":19172,\"journal\":{\"name\":\"New Zealand Journal of Forestry Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2019-11-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"New Zealand Journal of Forestry Science\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.33494/nzjfs492019x16x\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"FORESTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"New Zealand Journal of Forestry Science","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.33494/nzjfs492019x16x","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FORESTRY","Score":null,"Total":0}
引用次数: 1

摘要

背景:新西兰没有基于地面测量的国家森林清单。国家外来森林描述(NEFD)是基于对森林所有者的调查,但在描述小规模森林的面积和产量方面缺乏可靠性,因为超过一半的小规模森林没有包括在直接的NEFD调查中。这导致人们对这些森林的木材供应了解不足,这将带来问题,因为大多数小规模森林正在接近成熟,预计在未来十年将占木材供应的40%以上。因此,准确估计这些资源对于营销、收割和物流规划至关重要。此外,当前的NEFD不包含空间表示。本研究的目的是通过在新西兰Wairarapa地区的一个案例研究,检验遥感数据集在提供小规模森林资源面积和产量信息方面的效用。方法:将分类和回归树分析应用于RapidEye和LiDAR数据集,绘制Wairarapa小规模人工林的净蓄积面积图。结果与人工数字化确定的森林面积进行了比较。对于每一片森林,林分变量的平均顶部高度、基底面积、体积和林分年龄都是使用激光雷达衍生的指标进行估计的。这使得可以为这些林分变量绘制地图。结果:自动绘制的种植面积为4.2%(1 614公顷)小于参考面积(即人工数字化种植园面积),加上未通过自动方法检测到的人工数字化幼树种植园,面积与参考面积仅相差-0.6%(235公顷)。使用模拟林分变量开发的产量表都在现实范围内,与木材可用性预测产量表相当,平均产量比WAF产量低27 m3 ha-1。结论:绘图方法产生的结果与使用手动数字化计算的面积相当。然而,由于所使用的遥感数据集的分辨率,这种方法很难检测到幼苗;因此,需要手动数字化来绘制种植后3-5年内的年轻种植园地图。这项研究还证实,遥感方法可以用来描述森林产量,尽管该方法未能预测绘制的森林资源的全部年龄。可以通过包括具有更宽林分年龄范围的地块数据或研究不同的年龄估计模型来改进种植年龄的估计。还可以审查时间序列卫星图像,以检测森林的建立期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Describing area and yield for small-scale plantation forests in Wairarapa region of New Zealand using RapidEye and LiDAR
Background: New Zealand does not have a national forest inventory based on ground measurements. The National Exotic Forest Description (NEFD) is based on surveys of forest owners but lacks reliability in describing area and yield for small-scale forests, given that over half of the small-scale forests were not included in the direct NEFD survey. This has led to an insufficient understanding of the wood supply from these forests, which will raise problems as the majority of the small-scale forests are approaching maturity and are anticipated to contribute more than 40% of wood supply in the next decade. Therefore, having accurate estimation of this resources is critical for marketing, harvesting and logistic planning. Furthermore, the current NEFD does not incorporate a spatial representation. The aim of this study was to test the utility of remote sensing dataset for providing information on the area and yield of small-scale forest resources through a case study in the Wairarapa region of New Zealand. Methods: Classification and regression tree analysis was applied to both RapidEye and LiDAR datasets to map the net stocked area of small-scale plantation forests in Wairarapa. Results were compared against forest areas that have been determined from manual digitisation. For each forest, stand variables mean top height, basal area, volume and stand age were estimated using LiDAR-derived metrics. This allowed the development of maps for these stand variables. Results: The automatically mapped plantation area was 4.2% (1 614 ha) less than the reference area (i.e. manually digitised plantation area), together with manually digitised young plantations which were not detected from automated approach, the area differed only by -0.6% (235 ha) from the reference area. The yield tables developed using modelled stand variables were all within a realistic range and were comparable to wood availability forecast (WAF) yield tables, producing on average 27 m3 ha-1 less than the WAF yield. Conclusions: The mapping approach produced comparable results to the area calculated using manual digitisation. However, this approach struggles to detect young plantings due to the resolution of the remote sensing datasets used; hence manual digitisation is required to map the young plantations that were within 3–5 years of planting. This study also confirmed that the remote sensing approach could be used to describe forest yield, although the approach failed to predict the full range of ages of the mapped forest resource. The estimation of plantation age could be improved by including plot data with a wider range of stand ages or investigating different models for age estimation. It is also possible to review time-series satellite imagery to detect establishment periods for the forests.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.20
自引率
13.30%
发文量
20
审稿时长
39 weeks
期刊介绍: The New Zealand Journal of Forestry Science is an international journal covering the breadth of forestry science. Planted forests are a particular focus but manuscripts on a wide range of forestry topics will also be considered. The journal''s scope covers forestry species, which are those capable of reaching at least five metres in height at maturity in the place they are located, but not grown or managed primarily for fruit or nut production.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信