Advances in the estimation of vegetation productivity and biomass with the aids of remote sensing

Abstract

Abstract: There are mainly four types of models for estimating vegetation productivity and biomass, including empirical, physical, semi empirical and integrated models. The application of these models is limited by factors such as atmosphere, background, geography, vegetation cover and structure. The rapid development of remote sensing technique, especially the use of thermal infrared,microwave, laser remote sensors and the techniques such as multi angle, hyperspectral and high resolution remote sensing, will be helpful to eliminate or reduce the effect of these factors.

Key words: Film-mulched cultivation, Nitrogen nutrition physiology, Stress-resistance physiology, Rice

CLC Number:

Q948.15

DAI Xiaohua, YU Shixiao. Advances in the estimation of vegetation productivity and biomass with the aids of remote sensing[J]. cje, 2004, (4): 92-98.

References

[1] 方红亮,等.1998.高光谱遥感在植被监测中的研究综述[J].遥感技术与应用,13(1):62～69.
[2] 金亚秋,等.1997.人工神经网络模型反演植被生物量参数[J].遥感学报,1(2):84～87.
[3] 童庆禧,等.1997.湿地植被成像光谱遥感研究[J].遥感学报,1(1):50～57.
[4] 徐冠华,等.1996.遥感信息科学的进展和展望[J].地理学报,51(5):385～397.
[5] 徐希孺,等.2000.热红外多角度遥感和反演混合像元组分温度[J].北京大学学报,34(4):555 560.
[6] 张建奇,等.1997.植被红外辐射统计特征理论模型[J].西安电子科技大学学报,24(3):386～390.
[7] 张良培,等.1997.利用高光谱对生物变量进行估计[J].遥感学报,1(2):111～114.
[8] Vanderbilt VC,et al.1984.用激光技术判断小麦冠层中的太阳辐照度分布[A].见:作物气象译丛(3)[C].北京:科学出版社,74～82.
[9] Bergen KM, et al. 1999. Integration of remotely sensed radar imagery in modeling and mapping of forest biomass and net primary production [J]. Ecol. Mod., 122 : 257～274.
[10] Boyd DS, et al. 1999. The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0～5.0 μm) [J]. Int. J. Remote Sens.,20(5): 1017～1023.
[11] Boyd DS, et al. 1997. Potential vegetation indices for determining global forest cover [J]. Int .J. Remote Sens., 18(6): 1395～1401.
[12] Gong P, et al. 1995. Inverting a canopy reflectance model using an artificial neural network[A]. In: Engman ET, eds. Remote Sensing for Agriculture, Forestry, and Natural Resources [C].European Optical Society and The International Society for Optical Engineering, Paris, France, September 23～28,312～322.
[13] Hame T, et al. 1997. A new methodology for the estimation of biomass of conifer-dominated boreal forest using NOAA AVHRR data [J]. Int . J. Remote Sens ., 18(15) :3211～3243.
[14] Harrell PA. 1997. Evaluation of approaches to estimating aboveground biomass in Southern pine forests using SIR-C data [J].Remote Sens. Environ., 59: 223～233.
[15] Kaufman YJ, et al. 1994. Detection of forests using mid-IR reflectance: An application for aerosol studies[J]. IEEE Trans.Geosci . Remote Sens ., 32: 672～683.
[16] Kimes DS. 1984. Modeling the directional reflectance from complete homogenous vegetation canopies with various leaf-orientation distributions[J]. J . Opt. Soc. Amer., A1: 725～737.
[17] Kimes DS, et al. 1991. A knowledge-based expert system for inferring vegetation characteristics [J]. Int. J. Remote Sens., 12(10): 1987 ～2020.
[18] Kimes, DS, et al. 1982. Radiative transfer model for heterogeneous 3-D scenes [J]. Appl. Opt., 21:4119～4129.
[19] Leroy M, et al. 1994. Sun and view angle correction on reflectance derived from NOAA/AVHRR data [J]. IEEE Trans.Geosci. Remote Sens., 32: 684 ～697.
[20] Li X, et al. 1986. Geometric-optical bidirectional reflectance modeling of a conifer forest canopy[J]. IEEE Trans. Geosci. Remote Sens.,GE-24: 906～919.
[21] Li X, et al. 1988. Modeling the gap probability of discontinuous vegetation canopy [J]. IEEE Trans. Geosci. Remote Sens., 26:161～170.
[22] Myneni RB, et al. 1994. On the relationship between FAPAR and NDVI [J]. Remote Sens. Environ., 49: 200 ～211.
[23] Nemani RR, et al. 1993. Forest ecosystem processes at the watershed scale: Sensitivity to remotely-sensed leaf area index estimates [J]. Int .J. Remote Sens., 14:2519～2534.
[24] Potter CS, et al. 1993. Terrestrial ecosystem production: a process model based on global satellite and surface data [J]. Global Biogeochem . Cycles,7:811 ～841.
[25] Prince SD, et al. 1995. Global primary production: remote sensing approach[J]. J. Biogeogr., 22: 815～835.
[26] Rees WG, et al. 1998. Are vegetation indices useful in the Arctic? [J]. Polar Record, 34(191): 333～336.
[27] Ricotta C, et al. 2000. Analysis of the spatial distribution of net primary productivity across Corsica (France) using the echelon approach [J]. Int. J. Remote Sens., 21 (11): 2301 ～2306.
[28] Ricotta C, et al. 1998. Fractal modelling of the remotely sensed two-dimensional net primary production pattern with annual cumulative AVHRR NDVI data[J]. Int. J. Remote Sens., 19(12) :2413～2418.
[29] Roy DP, et al. 1997. Combination of the normalized difference vegetation index and surface temperatures for regional scale european forest cover mapping using AVHRR data[J]. Remote Sens. Environ., 18(5): 1189～1195.
[30] Thenkabail PS, et al. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics [J]. Remote Sens. Environ., 71:158～182.