Forecasting of forest land changes in the Chaloosrood watershed

Subject Areas : Geospatial systems development

Vajiheh Ghorbannia Kheybari ¹ , Mir Mehrdad Mirsanjari ² , Mohsen Armin ³

1 - PhD. Student of Environment, College of Natural Resources and Environments, Malayer University
2 - Assis. Prof. College of Natural Resources and Environments, Malayer University
3 - Assis. Prof. College of Agriculture and Natural Resources, Natural Resources and Environmental Research Institute, Yasouj University

Received: 2017-04-14 Accepted : 2017-07-24 Published : 2017-08-23

Keywords: Prediction, land use, Chaloosrood watershed, Logistic regression, Change detection,

Abstract :

Deforestation affects watershed processes and biochemical cycles and lead to soil erosion and lack of water in the catchment areas. This study is aimed to investigate the changes in forest land in the Chaloorood watershed on the west of Mazandaran province using Geomod. In this study, maps of forest in the years of 1987 and 2015 were prepared using satellite images. Then the suitability  forest map was produced by making a regression equation between suitability criteria maps and forest changes map in the period of 1987-2015. Finally, by using forest map in 1987, forest suitability map and the number of modified pixels in forest land between 1987 and 2015, Forecast of the forest map for 2043 was done using Geomod. Also, by using the Validate function and classified forest map 2015, as a reference map, and the forecasting forest map 2015,  as a comparative map, the validity of the production map was evaluated. The results showed that the area of forest land in 1987, 2015, and 2043 was 38683.65, 2464.354 and 15227.25 hectares, respectively. The extent of forest changes in the last 28 years and the next 28 years is 35.72% and 38.76% respectively. Forest changes in the period between 1987 and 2015 under the influence of factors such as distance from the road, forest cover density, distance from the village, slope and elevation above sea level, respectively. The Pseudo R2 and ROC coefficients are 0.29 and 0.85 respectively, which indicates the proper ability of the model to estimate forest changes over the past 28 years and the relative agreement of the model with the real changes. In this study the accuracy of resulting land use maps was 96%, which represent the appropriate capability of Geomod in land use changes modeling in Chaloosrood watershed.

References:

1. باقری، ر. و ش. شتایی جویباری. 1389. مدلسازی کاهش گستره جنگل با استفاده از رگرسیون لجستیک (مطالعه موردی: حوضه آبخیز چهل­چای استان گلستان). مجله جنگل ایران، 2(3): 243-252.

2. ضیاییان فیروز آبادی، پ.،  ع. ر. شکیبا، ع. ا. متکان و ع. صادقی. 1388. سنجش از دور (RS)، سیستم اطلاعات جغرافیایی (GIS) و مدل سلول­های خودکار (CA) به عنوان ابزاری برای شبیه­سازی تغییرات کاربری اراضی شهری (مطالعه موردی: شهر شهرکرد). علوم محیطی، 7(1): 133-148.

3. گلدوی، س.، م. محمدزاده، ع. ر. سلمان ماهینی و ع. نجفی­نژاد. 1393. مدل­سازی تغییرات اراضی جنگلی با روش رگرسیون لجستیک در دوره زمانی 1988 - 2007 و پیش­بینی شرایط آینده این اراضی در منطقه گرگان. فضای جغرافیایی، 46(20): 51-70. 

4. Annunzio R, Sandker M, Finegold Y, Min Z. 2015. Projecting global forest area towards 2030. Forest Ecology and Management, 352: 124-133.

5. Brown S, Hall M, Andrasko K, Ruiz F, Marzoli W, Guerrero G, Masera O, Dushku A, DeJong B, Cornell J. 2007. Baselines for land-use change in the tropics: application to avoided deforestation projects. Mitigation and Adaptation Strategies for Global Change, 12(6): 1001-1026.

6. Cabral P, Zamyatin A. 2006. Three land change models for urban dynamics analysis in Sintra-Cascais area. In: Proceedings of First Workshop of the EARSEL SIG on Urban Remote Sensing: Challenges and solutions. Humboldt-Universität zu Berlin, 2-3 March.

7. Clark WA,  Hosking PL. 1986. Statistical methods for geographers (Chapter 13). New York. John Wiley and Sons, 528 pp.  

8. DeFries R, Belward A. 2000. Global and regional land cover characterization from satellite data: an introduction to the Special Issue. International Journal of Remote Sensing, 21(6-1): 1083-1092.

9. Dushku A, Brown S. 2003. Spatial modeling of baselines for LULUCF Carbon projects: The GEOMOD modeling approach. In: 2003 International Conference on Topical Forests and Climate Change:" Carbon Sequestration and the Clean Development Mechanism, Manila - October 21.

10. Echeverria C, Coomes DA, Hall M, Newton AC. 2008. Spatially explicit models to analyze forest loss and fragmentation between 1976 and 2020 in southern Chile. Ecological Modelling, 212(3): 439-449.

11. Helmer EH, Brown S, Cohen W. 2000. Mapping montane tropical forest successional stage and land use with multi-date Landsat imagery. International Journal of Remote Sensing, 21(11): 2163-2183.

12. Hill R. 1999. Image segmentation for humid tropical forest classification in Landsat TM data. International Journal of Remote Sensing, 20(5): 1039-1044.

13. Houghton RA. 1995. Land‐use change and the carbon cycle. Global Change Biology, 1(4): 275-287.

14. Lillesand TM, Keifer RW. 1994. Remote sensing and image interpretation. John Wiley and sons. 750 pp.

15. Miller A, Bryant E, Birnie R. 1998. An analysis of land cover changes in the Northern Forest of New England using multitemporal Landsat MSS data. International Journal of Remote Sensing, 19(2): 245-265.

16. Pontius RG, Agrawal A, Huffaker D. 2003. Estimating the uncertainty of land-cover extrapolations while constructing a raster map from tabular data. Journal of Geographical Systems, 5(3): 253-273.

17. Pontius RG, Cornell JD, Hall CA. 2001. Modeling the spatial pattern of land-use change with GEOMOD2: application and validation for Costa Rica. Agriculture, Ecosystems & Environment, 85(1): 191-203.

18. Pontius RG, Malanson J. 2005. Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19(2): 243-265.

19. Pontius RG, Schneider LC. 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85(1): 239-248.

20. Pontius RG. 2000. Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering and Remote Sensing, 66(8): 1011-1016.

21. Pontius RG. 2002. Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric Engineering and Remote Sensing, 68(10): 1041-1050.

22. Rashmi M, Lele N. 2010. Spatial modeling and validation of forest cover change in Kanakapura region using GEOMOD. Journal of the Indian Society of Remote Sensing, 38(1): 45-54.

23. Smits P, Dellepiane S, Schowengerdt R. 1999. Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20(8): 1461-1486.

24. Soffianian A, Ahmadi Nadoushan M. 2010. Modelling urban changes using Geomod Model in Arak, Iran. 3^rd International Conference on Cartography and GIS. 15-20 June. Nessebar, Bulgaria.

25. Stow D. 1999. Reducing the effects of misregistration on pixel-level change detection. International Journal of Remote Sensing, 20(12): 2477-2483.

26. Wassenaar T, Gerber P, Verburg P, Rosales M, Ibrahim M, Steinfeld H. 2007. Projecting land use changes in the Neotropics: The geography of pasture expansion into forest. Global Environmental Change, 17(1): 86-104.

27. Ye B, Bai Z. 2007. Simulating land use/cover changes of Nenjiang County based on CA-Markov model. In: First IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture (CCTA 2007), Wuyishan, China, August 18-20.