Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

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Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 4 Renewable resources in rural areas: Vegetation, forestry, agriculture, soil survey, land and water use. Chairman: J. Besenicar, Liaisons: M. Molenaar, Th. A. de Boer

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: A preliminary study on NOAA images for non-destructive estimation of pasture biomass in semi-arid regions of China. Ding Zhi, Tong Qing-xi, Zheng Lan-fen & Wang Er-he, Xiao Qiang-Uang, Chen Wei-ying & Zhou Ci-song

Document type:: Multivolume work

Structure type:: Chapter

416 cess of plant maturation, and a surface plant cover sketch with .maximum or minimum biomass during whole growing season can be portrayed. An average ground biomass map during the plant grouwing season are also illustrated. Based upon perennial biomass of plant, a pe rennial maximum and minimum biomass will be produced, then an average Perennial biomass can be obtained, and a dynamic map of plant cover will be able to be portrayed. Such advantages promote the estimation of pasture biomass and crop production by using NOAA’s data, instead of LANDSAT’ S ones. 1.3 DATA COLLECTION Collection of the NOAA data took place on the 29th of July 1984 and the 13th June. 1985 a.nd to compare with these data, collection of 11 representatives and their correspond ing spectrural reflection data from 11 va rious types of pasture during June to August 1983 was made (Tal.2). Meteologic data(pre- ciptation, temperature, etc.) in 1983 and in 1985 were collected to fill in the time- non synchronous hap between NOAA’s data on 29th of July, 1984, and ground sampling data in June to August, 1983, and to provide ba sis for the establishment of correlation mo del and error analyses. According to various types of pastures or ranges, a representative sampling plot with an abea of 1 was collected. The clipped sample wet weight was immediately measured after clipping. Three subsamples location were averaged as a biomass for a sampling location. By visual plant cover, the total biomass per mu or per ha. can be calculated. 2 METHODS AND PROCEDURES 2.1 APPLIED MODELS As well known, by amount of ground mass spe ctral measurements, the 0.68 urn region cor responds strongly to the in vivo red region of chlorophyll absorption and is inversely related to the chlorophyll density. The 0.725-1.10 urn region corresponds to tne re gion of the spectrum where reflectance is proportional to the green leaf density. Ra tio combination of these two wavelength re- tions are thus related to the chlorophyll green leaf interraction (Gates, 1965; Wool- ley, 1971 ; Knipling, 1970). Recent years, using these two bands for estimating biomass has been confimed by many cases, such as u- ing LANDSAT’s and NOAA’s data to estimate soybean and winter grain yield, to predict aqricultureal crop production and pasture or range biomass. It should be pointed out that the two bands situated at 0.76-0.78 urn and 0.92-0.98 um are avoided,because the former is not sensitive to vegetation, and the latter is suitable for atmospheric water vapor absorption. To compare results from green leaf area response to absorption, reflection, and ra diation for the red and the near infrared with that of hand clipping method, the 101W field spectral radiator was used to integ rate the reflective spectrum situated at 0.55-0.68 vim, and 0.725-1 .10 um which is fully corresponded to CH1 and CH2 of NOAA, respectively. A regressional analysis(Fig.2) between measured spectrum and sampling fra- sh grass biomass corresponded in this area was made. It is illustrated that the corre lation coefficient is 0.77. For estimating pasture biomass with the AVHRR data of NOAA, an applied model, green leaf Normolized Difference(ND) (Rouse et al., 1973; Tucker et al., 1983 a , 1983) will be accepted, namely: ND=CH2 _ CHi/CHjj + GHi . It is clear that the ND reflects the dif ference between absorption of green leaf matter for red and reflection for near inf rared. Themore the chlorophyll density, the bigger this difference is. Therefore, it is refered to as a specific value to estimate green leaf biomass. It is noted that the ND value is effected by t 56° field of view be cause of the atmospheric path- length effec ts of solar rediace(Tucker, 1983), and if the angle of the sun’s altitude is high and sky is clear, this effect will be reduced to a minimum extent(Holben et al., 1984). 2.2 METHOD AND DATA ANALYSIS W ith NOAA magnetic types of the Tarim River Basin, Xinjiang of China received on the 29th of July, 1984, and the 13th of June, 1985, by the Beijing Receiving Station of NOAA Sa tellite in the Meterological Satellite Cen ter, National Meterological Bureau, China, by using the model ND = CH^-CH-]/CH2+CH1 , and I'able l. Chnracto inclinati ileinht at Number o£ Times of orbital F Latitudir Cycle dui Ground cc Field of Instantar ( 1 FuV ) Groud res h Gro^d res (maximi Samples j Number ol uata prêt ♦LANDSAT- 'of 16 da} .of prévit matic Ma| ♦•This is than the *** un Ut Table! wet b: Plots 1 2 3 4 5 6 7 8 9 10 11 * In L, **One thro in t: trib illu both ver dese To biom area gras and

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