Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities

被引:3
|
作者
Melser, Ramon [1 ]
Coops, Nicholas C. [1 ]
Wulder, Michael A. [2 ]
Derksen, Chris [3 ]
机构
[1] Univ British Columbia, Dept Forest Resource Management, Vancouver, BC V6T 1Z4, Canada
[2] Canadian Forest Serv, Nat Resources Canada, Victoria, BC V8Z 1M5, Canada
[3] Environm & Climate Change Canada, Climate Res Div, Toronto, ON M3H 5T4, Canada
关键词
GROSS PRIMARY PRODUCTIVITY; NET PRIMARY PRODUCTIVITY; CLIMATE-CHANGE; SOIL-MOISTURE; CARBON-CYCLE; FOREST; IMPACTS; CANADA; LANDSCAPE; DYNAMICS;
D O I
10.1080/07038992.2023.2256895
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Understanding the processes driving terrestrial vegetation productivity dynamics in boreal ecosystems is critical for accurate assessments of carbon dynamics. Monitoring these dynamics typically requires a fusion of broad-scale remote sensing observations, climate information and other geospatial data inputs, which often have unknown errors, are difficult to obtain, or limit spatial and temporal resolutions of productivity estimates. The past decade has seen notable advances in technologies and the diversity of observed wavelengths from remote sensing instruments, offering new insights on vegetation carbon dynamics. In this communication, we review key current approaches for modeling terrestrial vegetation productivity, followed by a discussion on new remote sensing instruments and derived products including Sentinel-3 Land Surface Temperature, freeze & thaw state from the Soil Moisture & Ocean Salinity (SMOS) mission, and soil moisture from the Soil Moisture Active Passive (SMAP) mission. We outline how these products can improve the spatial detail and temporal representation of boreal productivity estimates driven entirely by a fusion of remote sensing observations. We conclude with a demonstration of how these different elements can be integrated across key land cover types in the Hudson plains, an extensive wetland-dominated region of the Canadian boreal, and provide recommendations for future model development. Il est essentiel de comprendre les processus regissant la dynamique de la productivite de la vegetation terrestre des ecosystemes boreaux pour pouvoir evaluer avec precision les flux de carbone. Le suivi de cette dynamique necessite la fusion d'observations de teledetection a grande echelle, d'informations climatiques, et d'autres donnees geospatiales, lesquelles comportent des erreurs inconnues, sont difficiles a obtenir, et limitent les resolutions spatiales et temporelles des estimations de la productivite. Au cours de la derniere decennie, des progres technologiques notables ont diversifie les longueurs d'onde observees par les instruments de teledetection, ce qui a permis de mieux comprendre la dynamique du carbone liee a la vegetation. Dans cette communication, nous passons en revue les principales approches pour la modelisation de la productivite de la vegetation terrestre, suivies d'une discussion sur les nouveaux instruments de teledetection et leurs produits derives, notamment la temperature de la surface terrestre extraite de Sentinel-3, l'etat de gel ou de degel du sol derive de la mission SMOS, et l'humidite du sol derivee de la mission SMAP. Nous decrivons comment ces produits peuvent ameliorer le detail spatial et la representation temporelle des estimations de la productivite boreale entierement basees sur une fusion d'observations de teledetection. Nous concluons par une demonstration de la facon dont ces differents elements peuvent etre integres dans les principaux types de couverture terrestre des plaines hudsoniennes, une vaste region de la foret boreale canadienne dominee par des zones humides, et nous formulons des recommandations pour de futures ameliorations du modele.
引用
收藏
页数:22
相关论文
共 50 条
  • [41] Multi-source remote sensing data fusion: status and trends
    Zhang, Jixian
    INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION, 2010, 1 (01) : 5 - 24
  • [42] A new method for multi-source remote sensing image fusion
    Zhang, SY
    Wang, PQ
    Chen, XY
    Zhang, X
    IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium, Vols 1-8, Proceedings, 2005, : 3948 - 3951
  • [43] Multi-source remote sensing data fusion in human settlements
    Dang, Anrong
    Mao, Qizhi
    Qinghua Daxue Xuebao/Journal of Tsinghua University, 2000, 40 (09): : 7 - 10
  • [44] Research on Provenance Model for Multi-source Remote Sensing Images
    Wu M.
    Zhang M.
    Li P.
    Zhang Y.
    Journal of Geo-Information Science, 2023, 25 (07) : 1325 - 1335
  • [45] The Potential of Moonlight Remote Sensing: A Systematic Assessment with Multi-Source Nightlight Remote Sensing Data
    Liu, Di
    Zhang, Qingling
    Wang, Jiao
    Wang, Yifang
    Shen, Yanyun
    Shuai, Yanmin
    REMOTE SENSING, 2021, 13 (22)
  • [46] Yield estimation of summer maize based on multi-source remote-sensing data
    Wang, Jingshu
    He, Peng
    Liu, Zhengchu
    Jing, Yaodong
    Bi, Rutian
    AGRONOMY JOURNAL, 2022, 114 (06) : 3389 - 3406
  • [47] Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China
    Huang, Xiaodong
    Deng, Jie
    Ma, Xiaofang
    Wang, Yunlong
    Feng, Qisheng
    Hao, Xiaohua
    Liang, Tiangang
    CRYOSPHERE, 2016, 10 (05): : 2453 - 2463
  • [48] Research on multi-source remote sensing image registration technology based on Baker mapping
    Ma, Li
    Huang, Lei
    INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION, 2024, 15 (03) : 293 - 309
  • [49] Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data
    LU Tianqi
    CHEN Shengbo
    TU Yuan
    YU Yan
    CAO Yijing
    JIANG Deyang
    Chinese Geographical Science, 2019, 29 (02) : 192 - 201
  • [50] Biomass Estimation and Saturation Value Determination Based on Multi-Source Remote Sensing Data
    Sa, Rula
    Nie, Yonghui
    Chumachenko, Sergey
    Fan, Wenyi
    REMOTE SENSING, 2024, 16 (12)