{"title":"利用无人飞行器激光雷达(Lidar-UAV)指标和森林资源清查,对马莫拉栓皮栎林的生物量和碳储量进行建模和空间分析","authors":"","doi":"10.1016/j.rspp.2024.100127","DOIUrl":null,"url":null,"abstract":"<div><p>Recently, the concerns about climate change have heightened the need for effective methods for estimating and mapping Biomass and Carbon stock at local, national, continental, and global scales. Reliable Biomass and Carbon stock quantification and spatialization is a challenge, especially in degraded Mediterranean Cork oak forest. To estimate and map Biomass (B<sub>tree−Total</sub>) and Carbon stock (C<sub>st−total</sub>), we explored an improved approach using extracted metrics collected by Lidar-UAV (unmanned aerial vehicles Lidar), combined with forest inventory data. We approach three types of models for data analysis: Simple linear regression, multiple linear regressions, and stepwise multiple linear regression. The best Biomass and Carbon stock model fit is the Stepwise multiple linear regressions, involving the following metrics: maximum elevation, canopy cover and point cloud density and intensity. Our finding provides a quantification and spatialization Biomass and Carbon stock model based on Lidar-UAV metrics in Cork Oak Mediterranen forest and the results confirm the degraded state of Maamora Forest with a Biomass and Carbon stock relatively medium to low.</p></div>","PeriodicalId":45520,"journal":{"name":"Regional Science Policy and Practice","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S175778022400338X/pdfft?md5=d6c380b4a7c65e5fc24cbf226c99997a&pid=1-s2.0-S175778022400338X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Modeling and spatialization of biomass and carbon stock using unmanned Aerial Vehicle Lidar (Lidar-UAV) metrics and forest inventory in cork oak forest of Maamora\",\"authors\":\"\",\"doi\":\"10.1016/j.rspp.2024.100127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Recently, the concerns about climate change have heightened the need for effective methods for estimating and mapping Biomass and Carbon stock at local, national, continental, and global scales. Reliable Biomass and Carbon stock quantification and spatialization is a challenge, especially in degraded Mediterranean Cork oak forest. To estimate and map Biomass (B<sub>tree−Total</sub>) and Carbon stock (C<sub>st−total</sub>), we explored an improved approach using extracted metrics collected by Lidar-UAV (unmanned aerial vehicles Lidar), combined with forest inventory data. We approach three types of models for data analysis: Simple linear regression, multiple linear regressions, and stepwise multiple linear regression. The best Biomass and Carbon stock model fit is the Stepwise multiple linear regressions, involving the following metrics: maximum elevation, canopy cover and point cloud density and intensity. Our finding provides a quantification and spatialization Biomass and Carbon stock model based on Lidar-UAV metrics in Cork Oak Mediterranen forest and the results confirm the degraded state of Maamora Forest with a Biomass and Carbon stock relatively medium to low.</p></div>\",\"PeriodicalId\":45520,\"journal\":{\"name\":\"Regional Science Policy and Practice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S175778022400338X/pdfft?md5=d6c380b4a7c65e5fc24cbf226c99997a&pid=1-s2.0-S175778022400338X-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Regional Science Policy and Practice\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S175778022400338X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Regional Science Policy and Practice","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S175778022400338X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOGRAPHY","Score":null,"Total":0}
Modeling and spatialization of biomass and carbon stock using unmanned Aerial Vehicle Lidar (Lidar-UAV) metrics and forest inventory in cork oak forest of Maamora
Recently, the concerns about climate change have heightened the need for effective methods for estimating and mapping Biomass and Carbon stock at local, national, continental, and global scales. Reliable Biomass and Carbon stock quantification and spatialization is a challenge, especially in degraded Mediterranean Cork oak forest. To estimate and map Biomass (Btree−Total) and Carbon stock (Cst−total), we explored an improved approach using extracted metrics collected by Lidar-UAV (unmanned aerial vehicles Lidar), combined with forest inventory data. We approach three types of models for data analysis: Simple linear regression, multiple linear regressions, and stepwise multiple linear regression. The best Biomass and Carbon stock model fit is the Stepwise multiple linear regressions, involving the following metrics: maximum elevation, canopy cover and point cloud density and intensity. Our finding provides a quantification and spatialization Biomass and Carbon stock model based on Lidar-UAV metrics in Cork Oak Mediterranen forest and the results confirm the degraded state of Maamora Forest with a Biomass and Carbon stock relatively medium to low.
期刊介绍:
Regional Science Policy & Practice (RSPP) is the official policy and practitioner orientated journal of the Regional Science Association International. It is an international journal that publishes high quality papers in applied regional science that explore policy and practice issues in regional and local development. It welcomes papers from a range of academic disciplines and practitioners including planning, public policy, geography, economics and environmental science and related fields. Papers should address the interface between academic debates and policy development and application. RSPP provides an opportunity for academics and policy makers to develop a dialogue to identify and explore many of the challenges facing local and regional economies.