A fuzzy multi-criteria decision-making approach for the assessment of forest health applying hyper spectral imageries: A case study from Ramsar forest, North of Iran

Abstract

The hyperspectral images have so far been widely utilized in monitoring and detecting the changes in a broad range of environmentally related matters. The hyperspectral image analysis yields maps that show spatial dispersion of physical and ecological characteristics of the terrain. Within the scope of the current study, an integrated Fuzzy-MCDM in a Geographic Information Systems (GIS) platform was used to map the health condition of Ramsar forest. Spectral indices can provide different methods for identifying vegetation coverings. For forest health analysis, spectral indices such as NDWI, CRI1, PSRI, PRI, and NDVI were used to infer the causative factors of forest health. The findings highlight the suitability of the used methodology in identifying potential forest statuses, where forest health protection measures can be taken in advance. The results also suggest that the southern and the western aspects of the study area are of “very low” to “low” forest health. Furthermore, the results indicate a high potentiality for applying the spatial MCDM techniques as an effective tool for the forest health investigation.

Keywords

• Hyperspectral
• Fuzzy set
• Forest health
• Vegetation indices
• Ramsar

References

1. Akgun A, Sezer E A, Nefeslioglu H A, Gokceoglu C & Pradhan B (2012). An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci, 38, 23–34.
2. Baidya P, Chutia D, Sudhakar S, Goswami C, Goswami J, Saikhom V, ... & Sarma K K (2014). Effectiveness of Fuzzy Overlay Function for Multi-Criteria Spatial Modeling—A Case Study on Preparation of Land Resources Map for Mawsynram Block of East Khasi Hills District of Meghalaya, India. Journal of Geographic Information System, 6, 605-612.
3. Bolle H J, Eckardt M, Koslowsky D, Maselli F, Miralles J M, Menenti M, ... & Van de Griend A A (Eds.). (2006). Mediterranean landsurface processes assessed from space. Berlin, Heidelberg: Springer Berlin Heidelberg.
4. Boyer J S (1982). Plant productivity and environment. Science, 218(4571), 443-448.
5. Carter G A & Miller R L (1994). Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sensing of Environment, 50(3), 295-302.
6. Ceccato P, Flasse S, Tarantola S, Jacquemond S & Gregoire J M (2001). Detecting vegetation water content using reflectance in the optical domain. Remote Sensing of Environment, 77, 22–33.
7. Chapin F S (1991). Integrated responses of plants to stress. BioScience, 41, 29–36.
8. Colombo S J & Parker W C (1999). Does Canadian forestry need. Physiology research For. Chronicle, 75, 667–673.

9. Cox E (2005). Fuzzy modeling and genetic algorithms for data mining and exploration. Elsevier.
10. Delgado-Vargas F, Jiménez A R & Paredes-López O (2000). Natural pigments: carotenoids, anthocyanins, and betalains—characteristics, biosynthesis, processing, and stability. Critical reviews in food science and nutrition, 40(3), 173-289.
11. Feizizadeh B, Blaschke T & Roodposhti M S (2013). Integrating GIS-based fuzzy set theory in Multi-Criteria evaluation methods for Landslide susceptibility mapping. International Journal of Geoinformatics, 9(3).
12. Feizizadeh B, Blaschke T & Zamani H (2012). GIS-based ordered weighted averaging and Dempstershafermwthod for landslide susceptibility map in Urmia lake basin, Iran. International journal of digital earth. DOI: 10:1080. 17538,2012.
13. Feizizadeh B, Blaschke T (2012). Comparing GIS Multi-criteria decision analysis for landslide susceptibility mapping for the Urmia lake basin Iran, (IGARSS)2012 IEEE International 10.1109, IGARSS 2012.
14. Gamon J A & Surfus J S (1999). Assessing leaf pigment content and activity with a reflectometer.New Phytol1999;143:105-117.
15. Gamon J, Serrano L & Surfus J S (1997). The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia, 112(4), 492-501.
16. Gao B-C (1996). NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257-266.
17. Gitelson A A, Zur Y, Chivkunova O B & Merzlyak M N (2002). Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and photobiology, 75(3), 272-281.
18. Iliadis L (2007). Intelligent Information Systems and Applications in Risk Estimation (in Greek) Stamoulis Publishing Thessaloniki, Greece
19. Jackson R D (1986). Remote sensing of biotic and abiotic plant stress. Annual review of Phytopathology, 24(1), 265-287.
20. Jaiswal R K, Mukherjee S, Raju D K (2001). Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation, 4, 1-10.
21. Keeney R L, Raiffa H & Meyer R F (1993). Decisions with multiple objectives: preferences and value trade-offs. Cambridge university press.
22. Leondes C T (1998). Fuzzy logic and expert systems applications. Elsevier.
23. Malczewski J (2002). Fuzzy screening for land suitability analysis. Geographical and Environmental Modelling 6, 27–39. DOI: 10.1080/13615930220127279.
24. Mesgari M S, Pirmoradi A & Fallahi G R (2008). Implementation of overlay function based on fuzzy logic in spatial decision support system. World Applied Sciences Journal, 3(1), 60-65.
25. Moroni M, Lupo E, Marra E & Cenedese A (2013). Hyperspectral image analysis in environmental monitoring: setup of a new tunable filter platform. Procedia Environmental Sciences, 19, 885-894.
26. Peñuelas J, Savé R, Marfà O & Serrano L (1992). Remotely measured canopy temperature of greenhouse strawberries as indicator of water status and yield under mild and very mild water stress conditions. Agricultural and Forest Meteorology, 58(1-2), 63-77.
27. Pomerol J C & Barba-Romero S (2000). Multi criterion decision in management: Principles and management. Boston: Kluwer Academic
28. Saaty (1968). Fuzzy sets. Information and Content,8, 338–356. DOI: 10.1016/S0019-9958(65)90241-X.
29. Sadiq R & Husain T (2005). A fuzzy-based methodology for aggregative environmental risk assessment: A case study of drilling waste. Environmental Modelling&Software, 20, 33–46. DOI: 10.1016/j.envsoft.2003.12.007.
30. Sampson P H, Mohammed G H, Zarco-Tejada P J, Miller J R, Noland T L, Irving D, Treitz P M, Colombo S J & Freementle J (2000). The bioindicators of forest condition project: Aphysiological, remote sensing approach. For. Chronicle, 76(6), 941–952.
31. Sims A D & Gamon J A (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, 337- 354.
32. Thenkabail P S, Smith R B & De Pauw E (2002). Evaluation of Narrowband and Broadband Vegetation Indices for Determining Optimal Hyperspectral Wavebands for Agricultural Crop Characterization. Photogrammetric Engineering and Remote Sensing, 68(6), 607-621.
33. Thomas H & Stoddart J L (1980). Leaf senescence. Annual review of plant physiology, 31(1), 83-111.
34. Verstraeten W W, Veroustraete F & Feyen J (2006). On temperature and water limitation of net ecosystem productivity: Implementation in the C-Fix model. Ecological Modelling, 199(1), 4-22.
35. Yan D & Yang X (2011). Health assessment of forest ecosystem based fuzzy synthesis assessment. In 2011 International Conference on Electric Technology and Civil Engineering (ICETCE) (4941-4944). IEEE.
36. Zadeh L A (1968). Fuzzy sets. Information and Content,8, 338–356. doi: 10.1016/S0019-9958(65)90241-X.
37. Zarco-Tejada P J, Miller J R, Mohammed G H, Noland T L & Sampson P H (2002). Vegetation Stress Detection through Chlorophyll a + b Estimation and Fluorescence Effects on Hyperspectral Imagery, Journal of. Environ. Qual, 31, 1433–1441