TY - JOUR ID - ahamed2011 AU - Ahamed, T. AU - Tian, L. AU - Zhang, Y. AU - Ting, K. C. TI - A review of remote sensing methods for biomass feedstock production UR - ://WOS:000292849200002 DO - 10.1016/j.biombioe.2011.02.028 T2 - Biomass & Bioenergy PY - 2011 DA - Jul SN - 0961-9534 VL - 35 IS - 7 SP - 2455-2469 N1 - ISI Document Delivery No.: 793UK Times Cited: 0 Cited Reference Count: 141 Ahamed, T. Tian, L. Zhang, Y. Ting, K. C. Pergamon-elsevier science ltd Oxford AB - Monitoring and maximization of bioenergy yield from biomass feedstock has recently become a critically important goal for researchers. Remote sensing represents a potential method to monitor and estimate biomass so as to increase biomass feedstock production from energy crops. This paper reviews the biophysical properties of biomass and remote sensing methods for monitoring energy crops for site-specific management. While several research studies have addressed the agronomic dimensions of this approach, more research is required on perennial energy crops in order to maximize the yield of biomass feedstock. Assessment of established methods could lead to a new strategy to monitor energy crops for the adoption of site-specific management in biomass feedstock production. In this article, satellite, aerial and ground-based remote sensing's were reviewed and focused on the spatial and temporal resolutions of imagery to adopt for site-specific management. We have concluded that the biomass yield prediction, the ground-based sensing is the most suitable to establish the calibration model and reference for aerial and satellite remote sensing. The aerial and satellite remote sensing are required for wide converge of planning and policy implementations of biomass feedstock production systems. (C) 2011 Elsevier Ltd. All rights reserved. KW - Perennial energy crops KW - Site-specific management KW - Vegetative indices KW - Leaf area index KW - Satellite imagery KW - Remote sensing KW - tropical forest biomass KW - landsat tm data KW - unmanned aerial vehicle KW - leaf-area index KW - aboveground biomass KW - radar backscatter KW - vegetation KW - index KW - water-stress KW - biophysical properties KW - image segmentation ER - TY - JOUR ID - gitelson2003 AU - Gitelson, Anatoly A. AU - Viña, Andrés AU - Arkebauer, Timothy J. AU - Rundquist, Donald C. AU - Keydan, Galina: Leavitt, Bryan TI - Remote estimation of leaf area index and green leaf biomass in maize canopies DO - 10.1029/2002gl016450 PR - AGU T2 - Geophys. Res. Lett. PY - 2003 SN - 0094-8276 VL - 30 IS - 5 SP - 1248 AB - Leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse studies. Remote sensing provides a considerable potential for estimating LAI at local to regional and global scales. Several spectral vegetation indices have been proposed, but their capacity to estimate LAI is highly reduced at moderate-to-high LAI. In this paper, we propose a technique to estimate LAI and green leaf biomass remotely using reflectances in two spectral channels either in the green around 550 nm, or at the red edge near 700 nm, and in the NIR (beyond 750 nm). The technique was tested in agricultural fields under a maize canopy, and proved suitable for accurate estimation of LAI ranging from 0 to more than 6. KW - Remote sensing, Instruments and techniques. General or miscellaneous ER - TY - GEN ID - hunt_jr_2011 AU - Hunt Jr., E. Raymond AU - Daughtry, C. S. T. AU - Eitel, Jan U. H. AU - Long, D. S. TI - Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index T2 - Agronomy Journal PY - 2011 VL - 103 SP - 1090-1099 AB - Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. We propose the triangular greenness index (TGI), which calculates the area of a triangle with three vertices: ('r, Rr), ('g, Rg), and ('b, Rb), where ' is the wavelength (nm) and R is the reflectance for bands in red (r), green (g), and blue (b) wavelengths. TGI was correlated with chlorophyll content using a variety of leaf and plot reflectance data. Generally, indices using the chlorophyll red-edge (710-730 nm) had higher correlations with chlorophyll content compared to TGI. However, with broad bands, correlations between TGI and chlorophyll content were equal or higher than other indices for corn and wheat. Simulations using the Scattering by Arbitrarily Inclined Leaves canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high crop LAI, TGI was only affected by leaf chlorophyll content. TGI will enable the use of low-cost sensors, including digital cameras, for nitrogen management by remote sensing. ER - TY - ELEC ID - idb AU - Henrich, V. AU - Krauss, G. AU - Götze, C. AU - Sandow, C. TI - The IndexDatabase UR - https://www.indexdatabase.de/ CY - Bonn PY - 2011 DA - 2011 ER -