Hydrological mapping of the vegetation using remote sensing products
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

Hydrological mapping of the vegetation using remote sensing products

on

  • 655 views

 

Statistics

Views

Total Views
655
Views on SlideShare
655
Embed Views
0

Actions

Likes
0
Downloads
31
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Hydrological mapping of the vegetation using remote sensing products Document Transcript

  • 1. Bulletin of the Transilvania University of Braşov • Vol. 3 (52) - 2010Series II: Forestry • Wood Industry • Agricultural Food Engineering HYDROLOGICAL MAPPING OF THE VEGETATION USING REMOTE SENSING PRODUCTS M.D. NI Ă1 I. CLINCIU1 Abstract: This paper proposes a new method based on FCD (Forest Cover Density) model, which maps the vegetation taking into consideration the canopy density. The critical analysis of well-known methods from Romanian literature, concluded that the age and stand consistency are the main factors in hydrological mapping of the vegetation. In the new technique we integrated several vegetation indexes, which made possible a hydrological mapping based entirely on remote sensing products. We have applied the new method in Upper Tărlung Watershed, and the results obtained revealed a strong correlation with the results offered by classical methods. Key words: hydrological mapping, remote sensing, vegetation index.1. Introduction evaluation and quantification of retention and infiltration at a specific rain, under The hydrological mapping of forest conditions encountered in watershed [1].vegetation is an activity used for some The characteristic coefficient for thehydrological parameters determination. hydrological categories established by theThese parameters characterize the vegetation hydrological mapping system proposed byfor mathematical quantification of the Apostol in 1972 plays a very importanthydrological processes which happen within role in the estimation of retention. Thisthe hydrographic watersheds. mapping system has been completed in The most common synthetic quantification 1973-1987 period by M. Ionescu, P.of the hydrologic mapping of the vegetation Dumitrescu and N. Lazăr, and it wasis represented by the flow coefficients. adapted by Păcurar for computer applicationThese coefficients are calculated after the in the GWBASIC programming languageanalysis of the factors that influence the [2]. In the next period, the dichotomichydrological balance between the flow and schematics used in the structure mentionedthe rain [1]. in [1] allowed the development of According to the peak discharge computer based mapping modules that cancalculation methodology of small, be found in today’s geographicpredominantly forested watersheds, the information systems like IDRISI [2] orflow coefficient estimation is based on the ESRI ArcMap [5].1 Dept. of Forest Management Planning and Terrestrial Measurement, Transilvania University of Braşov.
  • 2. 74 Bulletin of the Transilvania University of Braşov • Vol. 3 (52) - 2010 • Series II The mapping system proposed by represented by the precision of theApostol and the method proposed by measurement of subcompartment borders.Păcurar require as input data certaininformation about the watershed conditions 2. Objectiveswhich can be found in the ForestManagement of that area. The system The calculus of flow coefficients isproposed in 1972 did not offer a clear influenced directly by the hydrologicalmethodology of using the mapping system, mapping of vegetation. Irrespective of thethe schematics “having a qualitative nature methods they employ - such as using aand include descriptive elements for each specific term (ex. S term in SCS-CNcategory and subcategory” [2]. Therefore, method, which represents the maximumthe method proposed by Păcurar offers a value of the sum between the total retentionclear structure, developed for computer and the infiltration quantum); or usingapplication, based on quantitative facts. representative land cover categories (ex.The Păcurar method was created Apostol method); or using simplifiedspecifically to be applied in programs procedures such as the Frevert method orwhich work with punctual data as well as the Mo oc method - the mapping proceduresin GIS environments that work with are governed by the same developinggridded data. directions: The mapping systems mentioned above • Each method takes as main data inputuse as input data information about the the presence or absence of the vegetation;status of the soil, the stand age, the stand • The land cover is categorized in theconsistency and the production class. With following main categories: forests, pastures,the aid of the last criteria, these systems agricultural lands and bare soil lands;succeed in characterizing the forest • Qualitative and quantitative informationinfluence to the flow process, better than is used for the definition of further categoriesthe mapping system proposed by Frevert. and/or subcategories. The spatial and temporal precision of the In the present paper the main objective issimulation as well as the quality of the the realization and validation of a newresults are in direct link with the data used method of hydrological mapping of theas input. Therefore, by using the data vegetation based on information fromretrieved from the forest management plan, remote sensing products.the method can encounter several temporal The characterizing elements presentedand spatial difficulties: above influence each other; however, in • First, the forest management plan is many cases the stand age and theupdated once at every 10 years, and during consistency influence decisively the otherthis period modifications may appear in the elements. Therefore, the usage of satellitestructure of the vegetation within the images, aerophotograms or remote sensingstudied watershed; products in hydrological mapping of the • Second, provided the application of a vegetation can contribute significantly tosilvicultural work within a large sub- the estimation of principal characterizationdepartment is known, the development of elements (age and consistency).the work cannot be characterized and Temporal and spatial problems thatdetermined thoroughly (ex. the position of appear in traditional methods can bestripes from striped clear-cuts, the position minimized with the help of the newof patches from progressive cuts); technique, which is based on remote • Finally, another spatial issue is sensing products.
  • 3. Ni ă, M.D., et al.: Hydrological Mapping of the Vegetation Using Remote Sensing Products 753. Material and Methods The crown arrangement found in the forest stand leads to a shadow pattern that The hydrological mapping method of the affects the spectral responses. The shadowvegetation is based on the FCD model index SI quantifies this pattern and is ablestructure, which was developed [3], [4] on to differentiate between young stands anddata input taken from Landsat TM satellite mature stands. The young stands have aimages. The FCD model analyzes the lower canopy shadow index (SI) comparedvegetation on the basis of four vegetation to the mature natural forest stands. Theindexes: Advanced Vegetation Index older forest stands show flat and low(AVI), Bare Soil Index (BI), shadow Index spectral axis in comparison with the openor scaled Shadow Index (SI, SSI) and area. SI has been calculated using Eq. (3):Thermal Index (TI). The results obtained after the model run SI = 3 (256 − B1) ⋅ (256 − B 2) ⋅ (256 − B 3 ) ,offers information for monitoring thecanopy density, indicating the forest status (3)in the acquisition time of satellite image. The advanced vegetation index (AVI) is where: B1, B2, B3 - the 1st, 2nd, 3rd band ofrelated to the NDVI index, but is better LANDSAT image.that the latter in highlighting subtle For the mapping method application adifferences in canopy density. AVI uses LANDSAT TM, with the acquisition yearthe power degree of the infrared response 2009, was downloaded from LANDSATand this characteristic is making AVI more repository.sensitive to forest density and physiognomicvegetation classes. In our research, AVI 4. Results and Discussionshas been calculated using the Equation (1): For validation, the method for hydrological mapping of the vegetation was applied in AVI = 3 (B 4 + 1) ⋅ (256 − B 3) ⋅ B 43 , (1) Upper Tărlung Watershed, upstream the Săcele lake.where: B3, B4 - the 3rd and the 4th band of The procedure of hydrological mappingLandsat image; B43 - the difference between method contains the following five steps:B4 and B3. 1. Satellite image calibration; Bare soil areas, fallow lands and 2. Vegetation indexes calculation: AVI,vegetation with marked background BI and SI;response are enhanced using the bare soil 3. Division of the vegetation index valuesindex. Similar to the concept of AVI, the in three distinct classes (Low, Medium, High),bare soil index (BI) is a normalized index taking into consideration the value variance;which separates the vegetation with 4. Combination of the vegetation indexdifferent background visibility in classes;completely bare, sparse canopy and dense Vegetation index combination identificationcanopy. In our research we have used the and vegetation categorization in thefollowing formula to calculate BI: hydrological categories proposed by Apostol (Table 1). (B 5 + B 3) − (B 4 + B1) The vegetation indexes have different BI = ⋅ 100 + 100 , (2) (B 5 + B 3) − (B 4 + B1) behaviour as it follows (Figure 1): • AVI offer information about allwhere: B1, B3, B4, B5 - the 1st, 3rd, 4th and vegetation elements, both for forested areas5th band of LANDSAT image. and pastures;
  • 4. 76 Bulletin of the Transilvania University of Braşov • Vol. 3 (52) - 2010 • Series II Composed vegetation index classes grouping in hydrologic categories Table 1 Hydrologic categorySpecifications A B C D1 D2 L-M-L; L-M- M-M-M; M- L-L-L; L-L- M; L-H-M; M-L; H-L-M; AVI – SI – BI H-H-L; H-H- L-H-L; M-H- M; L-L-H; L- M-L-L; M-L- H-L-H; H-M- combination M; H-H-H L; M-H-M M-H; L-H-H; M; M-M-L; L; H-M-M; M-L-H M-H-H H-M-Hwhere: H = High; M = Medium; L=Low • SI takes higher values with thedecrease of the forest density; • The increase in the percent of baresoil determines a subsequent increase inthe value of BI. Fig. 2. Vegetation index class division The result is a thematic raster which Fig. 1. Vegetation indexes characteristics contains on every pixel the hydrological against the forest condition [4] class of the vegetation located in those coordinates (Figure 3). In method application, five hydrological After cropping the Landsat image on theclasses are obtained according to those region of interest, the pixel frequencyproposed by Apostol. distribution of the product offered by the In order to combine the vegetation proposed method shows a high percent ofindexes, they were divided in three classes: forest vegetation in the area. This is one oflow, medium and high, according to the the advantages of the results offered by thestatistical distribution shown in Figure 2. method, which fulfils the first condition of After reclassification, the final step in classical methods, namely to identify themapping the vegetation is the hydrological presence or the absence of the vegetation.class extraction. The method is developed Knowing, at pixel level, the land coveron a raster calculation algorithm, which category offers the first information bothcreates complex codes to every pixel, from quantitative and qualitative on forestsimple codes (Table 1). degree in the studied watershed.
  • 5. Ni ă, M.D., et al.: Hydrological Mapping of the Vegetation Using Remote Sensing Products 77 Fig. 5. Correlation between Frevert coefficients and new method coefficients The flow coefficients of 23 watersheds were calculated with classic methods and the new method and their values compared. Fig. 3. Hydrological classes thematic The results of the comparison of showed raster for Upper Tărlung Watershed a high correlation between traditional methods and the new method, as can be In the Upper Tărlung Watershed area seen in Figure 5.85% was identified as forested and 31%fell in category A, 51% fell in category B 5. Conclusionsand 3% in category C (Figure 4). The rest of the area was identified as By adapting the application method frombeing comprised of 14% pastures and 1% FCD model for hydrological mapping of thebare soil (i.e. rocks, roads and agricultural vegetation a new method was created basedland). on remote sensing products. The results of the method put it on a quality scale between Frevert method (which uses few data input) and Apostol method (which uses many data taken from forest management). Regarding the applicability, the proposed method proves to have a very high precision when applied to extended areas. The high correlation between the flow coefficient values determined from forest management data and those determined with the new method, gives the proposed method a high level of trust. Fig. 4. Hydrological class distribution of Using the remote sensing products offersvegetation from Upper Tărlung Watershed advantages, the most important being the actuality of the data. Using vegetation Having the land cover category at pixel indexes, the obtained results offer anlevel offers easy manipulation of data. From accurate mapping of the real situation inthis point, it was easy to determine the the field, indirectly improving the flowflow coefficient determination on the small coefficient calculation and implicitly, thewatersheds from Upper Tărlung Watershed. peak discharge prediction.
  • 6. 78 Bulletin of the Transilvania University of Braşov • Vol. 3 (52) - 2010 • Series II Another advantage is given by the high University Publishing House, 2001.degree of coverage of the product offered. 2. Pacurar, V.D.: O nouă metodă deThe application of the method has a cartare hidrologică a terenurilorminimum unity with 0.1 ha area, which forestiere utilizând sistemele demakes it suitable for the mapping of small informa ii geografice (A New Methodareas. At the same time, the product gives of Hydrological Mapping of Forestedthe possibility of mapping larger zones Lands using Geographic Informationwith the help of LANDSAT images. Systems). In: Revista Pădurilor 2 The method shows some disadvantages (2005), p. 28-31.at processing (level of expertise in remote 3. Rikimaru, A.: Landsat TM Datasensing must be intermediate) and inter- Processing Guide for Forest Canopypretation (clouds occurrence); nevertheless, Density Mapping and Monitoringit offers a better alternative of hydrological Model. In: Proceedings of ITTOmapping of the vegetation especially in Workshop on Utilization of Remotezones where is no GIS data is available or Sensing in Site Assessment andwhere such data are out of date. Planning for Rehabilitation of The offered product ensures easy Logged-Over Forest, Bangkok,manipulation in GIS in raster format. Thailand, July 30-August 1, 1996, p.Furthermore, the proposed method proves 18-24.to be useful not only in small watersheds 4. Rikimaru, A., Roy, P.S., Miyatake, S.:but in larger basins too. Tropical Forest Cover Density Mapping. In: Tropical Ecology 43 (2002) No. 1,Acknowledgements p. 39-47. 5. Tamaş, S., Clinciu, I.: Cercetări This paper is supported by the Sectoral privind posibilită ile de utilizare aOperational Programme Human Resources sistemelor de informa ii geograficeDevelopment (SOP HRD), financed from (GIS) în fundamentarea hidrologică athe European Social Fund and by the proiectării lucrărilor de amenajare aRomanian Government under the contract bazinelor hidrografice toren iale.number POSDRU/6/1.5/S/6. (Research on Usage of GIS in Hydrological Substantiation ofReferences Management Works in Hydrographic Torrential Watersheds). Scientific1. Clinciu, I.: Corectarea Toren ilor Report, Braşov. Transilvania University (Torrent Control). Braşov. Transilvania Publishing House, 2006.