**7. References**


Rice Crop Monitoring with Unmanned Helicopter Remote Sensing Images 269

Herwitz, S. R., L. F. Johnson, S. E. Dunagan, and R. G. Higgins. 2004. Imaging from an

Huang, Y., Y. Lan, W. C. Hoffmann, and B. Fritz. 2008. Development of an unmanned aerial

Hunt, E. R., C. L. Walthall, and C. S. T. Daughtry. 2005. High-resolution multispectral digital

IRRI. 2006. *Atlas of Rice and World Rice Statistics.* Manila, Philippines: International Rice

Ishii, K., N., Noguchi and R. Sugiura. 2005. Remote-sensing technology for vegetation

Jensen, A., B. Lorenzen, H. Spelling-Ostergaard, and E. Kloster-Hvelplund. 1990.

Johnson, R. A., and G. K. Bhattacharyya. 2001. *Statistics: Principles and Methods.* 4th ed. John

Jones, C. L., N. O. Maness, M. L. Stone, and R. Jayasekara. 2004. Chlorophyll estimation

Kahabka, J. E., H. M. V. Es, E. J. McClenahan, and W. J. Cox. 2004. Spatial analysis of

Kennedy, P. M. 1995. Intake and digestion in swamp buffaloes and cattle: 3. Comparison

Kim, Y., and J. F. Reid. 2006. Modeling and calibration of a multi-spectral imaging sensor for in-field crop nitrogen assessment. *Applied Eng. in Agric*. 22(6): 935-941. Lamb, D. W., and R. B. Brown. 2001. Remote sensing and mapping of weeds in crops. *J.* 

Lee, W. S., and S. Searcy. 2000. Multispectral sensor for detecting nitrogen in corn plants.

Lee, Y. S., C. M. Yang, and A. H. Chang. 2002. Changes of nitrogen and chlorophyll contents

Lenthe, J.H., E.C. Oerke and H.W. Dehne. 2007. Digital infrared thermography for monitoring canopy health of wheat. *Precision Agriculture*, 8(1):15-26. MacArthur, D., J. K. Schueller, and C. D. Crane. 2005. Remotely piloted mini-helicopter imaging of citrus. ASAE Paper No. 051055. St. Joseph, Mich.: ASABE.

and reflectance spectral characteristics to the application of nitrogen fertilizer in

*and Electronics in Agric.* 44(1): 49-61.

levels. *Intl. J. Remote Sensing* 11(10): 1809-1820.

043081. St. Joseph, Mich.: ASAE.

*J. Agric. Sci. Cambridge* 124(2): 265-275.

ASAE Paper No. 001010. St. Joseph, Mich.: ASAE.

rice plants. *J. Agric. Res. China,* 51(1): 1-14.

*Agric. Eng. Res.* 78(2): 117-125.

State University.

Sensing (ASPRS).

Research Institute.

Wiley and Sons.

463-476.

379.

unmanned aerial vehicle: Agricultural surveillance and decision support. *Computers* 

vehicle-based remote sensing system for site-specific management in precision agriculture. In *Proc. 9th Intl. Conf. on Precision Agriculture.* Denver, Colo.: Colorado

photography using unmanned airborne vehicles. In *Proc. 20th Biennial Workshop on Aerial Photography, Videography, and High-Resolution Digital Imagery for Resource Assessment.* Bethesda, Md.: American Society for Photogrammetry and Remote

monitoring using an unmanned helicopter, *Biosystems Engineering*, 90 (4), 369-

Radiometric estimation of biomass and N content of barley grown at different N

using multi-spectral reflectance and height sensing. ASAE/CSAE Paper No.

maize response to nitrogen fertilizer in central New York. *Precision Agric.* 5(5):

with four forage diets, and with rice straw supplemented with energy and protein.


Biermacher, J. T., F. M. Epplin, B. W. Brorsen, J. B. Solie, and W. R. Raun. 2006. Maximum

Chang, J., D. E. Clay, K. Dalsted, S. Clay, and M. O'Neill. 2003. Corn (*Zea mays* L.) yield

Chang, K. W., Y. Shen, and J. C. Lo. 2005. Predicting rice yield using canopy reflectance

Chang, Y. K., Q. U. Zaman, A. W. Schumann, and D. C. Percival. 2011. Performance tests of

de Matthaeis, P., P. Ferrazzoli, G. Schiavon and D. Solimini.1995. Crop type

Doraiswamy, P. C., S. Moulin, P. W. Cook, and A. Stern. 2003. Crop yield assessment from

Eisenbiss, H. 2004. A mini unmanned aerial vehicle (UAV): System overview and image

Fablo, M., and R. Felix. 2001. Analysis of GAC NDVI data for cropland identification and

FAOSTAT. 2007. *Agricultural Statistics Yearbook: 2006*. Rome, Italy: Food and Agriculture

Fukagawa, T., K. Ishii, N. Noguchi, and H. Terao. 2003. Detecting crop growth by a multispectral imaging sensor. ASAE Paper No. 033125. St. Joseph, Mich.: ASAE. Gebhardt, S., J. Schellberg, R. Lock, and W. Kuhbauch. 2006. Identification of broad-leaved

Gitelson, A. A., Y. J. Kaufman and M. N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS, *Rem. Sens. Environ.,* 58: 289–298. Hayward, R. C., D. Gebre-Egziabher, and J. D. Powell. 1998. GPS based attitude for aircraft:

Bangkok, Thailand: Field Crop Research Station.

*Precision Agriculture,* 7(3): 165-178.

www. waas. standford.edu.

remote sensing. *Photogram. Eng. and Remote Sensing,* 69(6): 665-674.

measured at booting stage. *Agronomy Journal,* 97(3): 872-878.

204.

*Environment,* 74(2): 229-23.

*Applications', International.*

*Sensing* 67(5): 593-602.

(ISPRS).

benefit of a precise nitrogen application system for wheat. *Precision Agric*. 7(3): 193-

prediction using multispectral and multidate reflectance. *Agron. J.* 95(6): 1447-1453.

g-ratio index and color co-occurrence matrix based machine vision algorithms in the wild blueberry fields. ASABE Paper No 09558 (ASAE, St. Joseph, MI, USA) Chen, S., C. W. Huang, C. C. Huang, C. K. Yang, T. H Wu, Y. Z. Tsai, and P. L. Miao. 2003.

Determination of nitrogen content in rice crop using multi-spectral imaging. ASAE Paper No. 031132. St. Joseph, Mich.: ASAE.Daughtry, C. S. T., C. L. Walthall, M. S. Kim, E. B. de Colstoun, and J. E. McMurtrey III. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. *Remote Sensing of* 

identification and biomass estimation by SAR. *Geoscience and Remote Sensing Symposium, 1995. IGARSS '95. 'Quantitative Remote Sensing for Science and* 

acquisition. In *Proc. Intl. Workshop on Processing and Visualization Using High-Resolution Imagery*. International Society for Photogrammetry and Remote Sensing

yield forecasting in Mediterranean African countries. *Photogram. Eng. and Remote* 

Organization of the United Nations.Fadel, M. 2004. Performance assessment of VRT-based granular fertilizer broadcasting systems. A*gricultural Engineering International: the CIGR Journal of Scientific Research and Development*, Manuscript PM 03 001, Vol.6.Field crop report. 1998. Field crop report for rice cropping in Thailand.

dock (*Rumex obtusifolius* L.) on grassland by means of digital image processing.


Rice Crop Monitoring with Unmanned Helicopter Remote Sensing Images 271

Tennakoon, S. B., V. V. N. Murty, and A. Eiumnoh. 1992. Estimation of cropped area

Thoma, D., S. Gupta, and M. Bauer. 2004. Evaluation of optical remote sensing models for crop residue cover assessment. *J. Soil and Water Cons. Soc.* 59(5): 224-233. Thomasson, J. A., R. Sui, and D. C. Akins. 2000. Spectral changes in picked cotton leaves

Thomoson, S. J., J. E., Hanks, and G. F. Sassenrath-Cole. 2002. Continuous georeferencing for

Thomson, S. J., and D. G. Sullivan. 2006. Crop status monitoring using multispectral and

Trotter, T. F., P.S. Frazier, M. G. Trotter and D. W. Lamb. 2008. Objective biomass

Tumbo, S. D., D. G. Wagner, and P. H. Heinemann. 2001. On-the-go sensing of chlorophyll

Wood, C. W., D. W. Reeves, and D. G. Himelrick. 1993. Relationships between chlorophyll

Xiang, H., and L. Tian. 2006. Development of autonomous unmanned helicopter-based

Xiang, H., and L. Tian. 2007a. An autonomous helicopter system for aerial image collection.

Xiang, H., and L. Tian. 2007b. Autonomous aerial image georeferencing for an UAV-based

Zaman, Q. U., K. C. Swain, A. W. Schumann, and D. C. Percival. 2010. Automated, low-

Zaman, Q. U., A. W. Schumann, and S. Shibusawa. 2006. Impact of variable rate

Zaman, Q., A. W. Schumann, and K. H. Hostler. 2006. Estimation of citrus fruit yield

Zhang, F., B. Wu, and C. Liu. 2003. Using time series of SPOT VGT NDVI for yield

status in corn. ASAE Paper No. 011175. St. Joseph, Mich.: ASAE.

Group, The University of New England, Australia.

ASABE Paper No. 071136. St. Joseph, Mich.: ASABE.

review. *Proc Agron. Soc. New Zealand* 23: 1-9.

073046. St. Joseph, Mich.: ASABE.

Conf. Minnesota. July 24-26, 2006.

427-439.

1189.

ASABE.

26(2): 225-232.

63.

N.J.: IEEE.

Ames, Iowa: USA.

St. Joseph, Mich.: ASABE.

and grain yield of rice using remote sensing data. *Intl. J. Remote Sensing* 13(3):

with time. In: *Proc. 5th Intl. Conf. on Precision Agriculture & Other Resources Mgmt.*

video-based remote sensing on agricultural air craft. *Trans. of ASAE*, 45(40): 1177-

thermal imaging systems for accessible aerial platforms. ASABE Paper No. 061179.

assessment using and active plant sensor (crop circle)- Preliminary experience on a variety of agricultural landscapes, Report from Precision Agriculture Research

meter readings and leaf chlorophyll concentration, N status, and crop yield: A

agricultural remote sensing system. ASABE Paper No. 063097. St. Joseph, Mich.:

data collection platform using integrated navigation system. ASABE Paper No.

costyield mapping of wild blueberry fruit. Applied Engineering in Agriculture.

fertilizationon nitrate leaching in citrus orchards. 8th Int. Precision Agriculture

usingultrasonically sensed tree size. *Applied Eng. in Agric.* 22(1): 39-44.Zaman, Q. U., and A. W. Schumann. 2006. Nutrient management zones for citrus based on variation in soil properties and tree performance. Precision Agriculture 7(1):45-

forecasting. In *Proc. Geoscience and Remote Sensing Symp.*, 1: 386-388. Piscataway,


MacArthur, D., J. K. Schueller, W. S. Lee, C. D. Crane, E. Z. MacArthur, and L. R. Parsons.

McNairn, H., D. Wood, Q. H. J. Gwyn, R. J. Brown, and F. Charbonneau. 1998. Mapping

Min, M., W. S. Lee, T. F. Burks, J. D. Jordan, A. W. Schumann, J. K. Schueller, and H. Xie.

Markwell, J., J. C. Osterman, and J. L. Mitchell. 1995. Calibration of the Minolta SPAD-502

Noh, H. K., Q. Zhang, and S. Han. 2004. Sensor-based variable-rate application of nitrogen

Okamoto, H., T. Murata, T. Kataoka, and S-I Hata. 2007. Plant classification for weed

Reese, G.A., R.L. Bayn, and N.E. West. 1980. Evaluation of double-sampling estimators of subalpine herbage production. *Journal of Range Management* 33:300-306. Reyniers, M., and E. Vrindts. 2006. Measuring wheat nitrogen status from space and

Rouse, J. W., R. H. Haas, J. A. Shell, and D. W. Deering. 1973. Monitoring vegetation systems

Seang, T. P. and J-P Mund. 2006. Geo-referenced balloon digital aerial photo technique: A

Stafford, J. V. 2000. Implementing precision agriculture in the 21st century. *J. Agric. Eng. Res.*

Stombaugh, T., A. Simpson, J. Jacobs and T. Mueller. 2003. A low cost platform for obtaining

Sugiura, R., N. Noguchi, K. Ishii, and H. Terao. 2002. The development of remote sensing

Sugiura, R., K. Ishii, and N. Noguchi. 2004. Remote sensing technology for field information

estimating of soil water status, *Biosystems Engineering*, 96(3), 301-313.

aboveground biomass in corn stover. *Biomass and Bioenergy* 26:337-343. Rama Rao, N., M. Kapoor, N. Sharma, and K. Venkateswarlu. 2007. Yield prediction and

063096. St. Joseph, Mich.: ASABE.

*Computers and Electronics in Agric*. 63(2): 215-226.

leaf chlorophyll meter", *Photosynth. Res.*, 46, 467-472.

techniques. *Intl. J. Remote Sensing* 28(7): 1561-1576.

Development Pvt. Ltd., Noida, India.

76(3): 267-275.

ASAE.

Werner, pp.665-676.

ground-based platform. *Intl. J. Remote Sensing* 27(3): 549-567.

*Symp.*, 1: 309-317. NASA SP-351. Washington, D.C.: NASA.

*Remote Sensing* 24(1): 110-115.

ASAE.

2006. Remotely piloted helicopter citrus yield map estimation. ASABE Paper No.

tillage and crop residue management practices with RADARSAT. *Canadian J.* 

2008. Design of a hyperspectral nitrogen sensing system for orange leaves.

by using multi-spectral image sensor. ASAE Paper No. 041133. St. Joseph, Mich.:

detection using hyperspectral imaging with wavelet analysis. *Weed Biol. and Mgmt*. 7(1): 31-37.Pordesimo, L.O., W.C. Edens, and S. Sokhansanj. 2004. Distribution of

waterlogging assessment for tea plantation land using satellite image-based

in the Great Plains with ERTS-1. In *Proc. 3rd Earth Resources Technology Satellite* 

low-cost high-resolution option for developing countries. In *Proc. Map Asia 2006: 5th Annual Conf. on Geographic Information Technology and Application*. GIS

remote sensed imagery. In: *Precision Agriculture*, Edited by J. Stafford and A.

system using unmanned helicopter. In *Proc. Automation Technology for Off-Road Equipment*, 120-128. ASAE Paper No. 701P0502. Q. Zhang, ed. St. Joseph, Mich.:

using an unmanned helicopter. In *Proc. Automation Technology for Off-Road Equipment*. ASAE Paper No. 701P1004. St. Joseph, Mich.: ASAE.Sujiura, R., N. Naguchi and K. Ishii. 2007. Correction of low-altitude thermal images applied to


**13** 

*Chile* 

**Geostatistical Estimation of Biomass Stock in** 

There are a variety of approaches to estimate above ground biomass (AGB), which can be classified according to the data source being used (Koch and Dees, 2008): field measurement, remotely sensed data or ancillary data used in GIS-based modeling. Field measurements are based on destructive sampling or direct measurement and the application of allometric equations (Madgwick, 1994). Recently, remotely sensed data, from both passive and active sensors, have become an important data source for AGB estimation. In this chapter we will focus on the use of optical multispectral data such as TM/ETM+ to estimate AGB. Generally, biomass is either estimated via a direct relationship between spectral response and biomass using multiple regression, k-nearest neighbor, neural networks, inverse canopy models or through indirect relationships, whereby attributes estimated from the remotely sensed data, such as leaf area index (LAI), structure (crown closure and height) or shadow fraction are used in equations to estimate biomass (Wulder, 1998). Here, we discuss the use of remote sensing data of moderate spatial resolution as input to estimate AGB. Research has demonstrated that it is more effective to generate relationships between field measurements and moderate spatial resolution remotely sensed data (e.g., LANDSAT), and then extrapolate these relationships over larger areas using comparable spectral properties from coarser spatial resolution imagery (e.g., MODIS) (Steininger, 2000; Lu, 2005; Phua and Saito, 2003; Foddy el al., 2003; Fazakas et al., 1999; Roy and Ravan, 1996; Zheng et al., 2004; Mickler et al., 2002). In general terms, LANDSAT TM and ETM+ data are the most widely used data of remotely sensed imagery for forest biomass estimation, but data from other moderate spatial resolution sensors have also been used, including ASTER and HYPERION data. In this chapter we present approaches that are currently being developed in Chile. Specifically, we introduce methods for the estimation of AGB using medium spatial resolution satellite imagery and digital elevation models. The main objective is to create, calibrate and validate such methods for applications. We developed an alternative approach in the estimation of AGB using LANDSAT ETM + imagery and SRTM digital elevation models as covariates for geostatistical modeling. From the spatial perspective, AGB data correspond to an array of points in space (x, y), while covariates correspond to a set of data that has a large number of samples in geographic space (extracted from each pixel), some of which having overlap with the available AGB

**1. Introduction** 

**Chilean Native Forests and Plantations** 

Jaime Hernández, Patricio Corvalán,

*Universidad de Chile* 

Xavier Emery, Karen Peña and Sergio Donoso

