山地地表生态参量遥感反演的理论、方法与问题
Principles and Methods for the Retrieval of Biophysical Variables in Mountainous Areas
查看参考文献82篇
文摘
|
山地在维持生物多样性、调节区域气候和涵养水源等多个方面具有非常重要的生态服务功能。由于存在高度的时—空异质性,山地地表生态参量的反演面临更多理论和技术上的难题,也对定量遥感的发展提出了新的要求。梳理了山地地表生态参量遥感反演的研究进展,讨论了进一步提高山地地表生态参量遥感反演能力的关键理论、技术及存在问题。只有模型、观测和数据处理方法3个环节的共同进步和有机结合才能推动山地定量遥感继续向前发展。反演山地生态参量不仅需要已有理论和技术的提升,同时还需要方法论上的突破。 |
其他语种文摘
|
Mountainous areas provide diverse ecosystem services in many aspects,including biodiversity maintenance, regional climate regulation and water conservation.The retrieval of biophysical variables in mountainous areas faces more challenges in technology and theory compared to that in flat terrain.These challenges derive from the significant surface heterogeneity and the lack of appropriate models for radiative transfer and ecological process.The related research progresses and key theory and techniques in montanic biophysical variables retrieval were reviewed in this paper.Further improvement of retrieval accuracy calls for paradigm shift in modeling,observation and data processing in mountainous areas. |
来源
|
遥感技术与应用
,2016,31(1):1-11 【核心库】
|
DOI
|
10.11873/j.issn.1004-0323.2016.1.0001
|
关键词
|
山地
;
生态参量
;
反演
;
定量遥感
|
地址
|
中国科学院水利部成都山地灾害与环境研究所, 四川, 成都, 610041
|
语种
|
中文 |
文献类型
|
研究性论文 |
ISSN
|
1004-0323 |
学科
|
自动化技术、计算机技术 |
基金
|
国家自然科学基金项目
;
中国科学院“百人计划”项目
;
中国科学院国际合作重点项目
;
中国科学院战略性先导科技专项
;
中国科学院创新团队国际合作伙伴计划
|
文献收藏号
|
CSCD:5664061
|
参考文献 共
82
共5页
|
1.
Gret-Regamey A. Mountain Ecosystem Services:Who Cares?.
Mountain Research and Development,2012,32:S23-S34
|
被引
12
次
|
|
|
|
2.
邓伟. 中国山地科学发展构想.
中国科学院院刊,2008,23(2):156-161
|
被引
15
次
|
|
|
|
3.
Li A N. Eco-environmental Vulnerability Evaluation in Mountainous Region Using Remote Sensing and GIS-A Case Study in the Upper Reaches of Minjiang River,China.
Ecological Modelling,2006,192(1/2):175-187
|
被引
44
次
|
|
|
|
4.
Theurillat J P. Potential Impact of Climate Change on Vegetation in the European Alps:A Review.
Climatic Change,2001,50(1/2):77-109
|
被引
30
次
|
|
|
|
5.
Li A N. An Improved Physics-based Model for Topographic Correction of Landsat TM Images.
Remote Sensing,2015,7(5):6296-6319
|
被引
6
次
|
|
|
|
6.
Li F Q. A Physics-based Atmospheric and BRDF Correction for Landsat Data over Mountainous Terrain.
Remote Sensing of Environment,2012,124:756-770
|
被引
6
次
|
|
|
|
7.
Schaaf C B. Topographic Effects on Bidirectional and Hemispherical Reflectances Calculated with a Geometric-optical Canopy Model.
IEEE Transactions on Geoscience and Remote Sensing,1994,32(6):1186-1193
|
被引
17
次
|
|
|
|
8.
Fan W L. Hybrid Geometric Optical-Radiative Transfer Model Suitable for Forests on Slopes.
IEEE Transactions on Geoscience and Remote Sensing,2014,52(9):5579-5586
|
被引
5
次
|
|
|
|
9.
李新. 陆地表层系统模拟和观测的不确定性及其控制.
中国科学:地球科学,2013,43(11):1735-1742
|
被引
28
次
|
|
|
|
10.
Chen J M. Defining Leaf-area Index for Non-flat Leaves.
Plant Cell and Environment,1992,15(4):421-429
|
被引
203
次
|
|
|
|
11.
Myneni R B. On the Relationship between FAPAR and NDVI.
Remote Sensing of Environment,1994,49(3):200-211
|
被引
35
次
|
|
|
|
12.
Zeng X B. Derivation and Evaluation of Global 1-km Fractional Vegetation Cover Data for Land Modeling.
Journal of Applied Meteorology,2000,39(6):826-839
|
被引
31
次
|
|
|
|
13.
Myneni R B. Global Products of Vegetation Leaf Area and Fraction Absorbed PAR from Year One of MODIS Data.
Remote Sensing of Environment,2002,83(1/2):214-231
|
被引
120
次
|
|
|
|
14.
Heinsch F A.
GPP and NPP (MOD17A2/A3)Products NASA MODIS Land Algorithm.MOD17User's Guide,2003:1-57
|
被引
1
次
|
|
|
|
15.
Baret F. GEOV1:LAI and FAPAR Essential Climate Variables and FCOVER Global Time Series Capitalizing over Existing Products.Part1:Principles of Development and Production.
Remote Sensing of Environment,2013,137:299-309
|
被引
35
次
|
|
|
|
16.
Camacho F. GEOV1:LAI,FAPAR Essential Climate Variables and FCOVER Global Time Series Capitalizing over Existing Products.Part 2:Validation and Intercomparison with Reference Products.
Remote Sensing of Environment,2013,137:310-329
|
被引
19
次
|
|
|
|
17.
Xiao Z Q. Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product from Time-series MODIS Surface Reflectance.
IEEE Transactions on Geoscience and Remote Sensing,2014,52(1):209-223
|
被引
43
次
|
|
|
|
18.
Beck J V. Inverse Problems and Parameter Estimation:Integration of Measurements and Analysis.
Measurement Science & Technology,1998,9(6):839-847
|
被引
2
次
|
|
|
|
19.
Kimes D. Inversion Methods for Physically-based Models.
Remote Sensing Reviews,2000,18(2/4):381-439
|
被引
14
次
|
|
|
|
20.
Knyazikhin Y. Synergistic Algorithm for Estimating Vegetation Canopy Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation from MODIS and MISR Data.
Journal of Geophysical Research-Atmospheres,1998,103(D24):32257-32275
|
被引
63
次
|
|
|
|
|