Comparative study of the possibility estimation of some structural quantitative attributes of Caspian forests using Radar and integrating Radar and Lidar data
Subject Areas : Geospatial systems development
Mehrsa Yazdani
1
,
Shaban Shataee Joibari
2
,
Jahangir Mohammadi
3
,
Yaser Maghsoudi
4
1 - MSc. Student of Forestry, Gorgan University of Agricultural Sciences and Natural Resources
2 - Prof. College of Forest Science, Gorgan University of Agricultural Sciences and Natural Resources
3 - Assis. Prof. College of Forest Science, Gorgan University of Agricultural Sciences and Natural Resources
4 - Assis. Prof. College Geodesy & Geomatics Engineering, Khajeh Nasir Toosi University of Technology
Keywords: Radar data, Forest structure attributes, Caspian forests, Integration Lidar and radar,
Abstract :
The purpose of this study was to compare the estimation of the structural attributes of stand volume, basal area, and tree stem density per hectare of the Caspian forests using Radar data and integration of Radar and Lidar data in some parts of the district I and II the ShastKalateh forest in the Golestan province. Forest structural data were measured and computed from 307 circular plots. The required pre-processing and processing was performed using raw data of Radar (2009) and Lidar (2011), and the corresponding values of sample plots were extracted on all Radar and Lidar derived indices. The modeling was performed using extracted Radar features as individual and also using Lidar and Radar extracted features as integrated with the non-parametric random forest algorithm in 75% of samples. The modeling validity was performed using 25% of the remained samples by absolute and relative root mean square error (RMSe) and Bias. The percentage RMSe and the Bias values using Radar data were obtained form stand volume (44.09% and -0.99%), basal area per hectare (35.72% and -3.15%) and tree stem density per hectare (42.73% and 3.52%), respectively, and using the integration of Radar and Lidar data for stand volume (37.23% and 0.76%), basal area per hectare (31.37% and -3.14%), and tree density per hectare (36.44% and 0.95%). The results showed that the integration of Radar and Lidar data could improve the estimates, especially in the stand volume, compared to using Radar data as individually.
1. امینی، ج. و ی. صادقی. 1390. تصاویر ماهوارهای نوری و راداری در مدلسازی زیست تودهی جنگلهای شمال ایران. سنجش از دور و GIS ایران، 4(4): 69-82.
2. خرمی، ر. 1383. بررسی قابلیت دادههای ماهوارهای+Landsat7 ETM در برآورد حجم سرپای توده‌های راش (مطالعه موردی: جنگلهای سنگده). پایان‌نامه کارشناسی ارشد، دانشگاه تهران. 80 صفحه.
3. درویشصفت، ع. 1390. سنجش از دور برای مدیران GIS. انتشارات دانشگاه تهران، 701 صفحه.
4. عزیزی، ز.، ا. نجفی آشتیانی، پ. فاتحی و م. پیرباوقار. 1389. بررسی امکان برآورد حجم سرپای جنگل با استفاده از دادههای سنجنده Liss-IV ماهواره IRS P6 مطالعه موردی لیرهسر تنکابن. تحقیقات جنگل و صنوبر ایران، 18(1): 143-151.
5. محمدی، ج. 1392. بهبود برآورد برخی مشخصههای کمی ساختار جنگل با استفاده از تلفیق دادههای لیدار و تصاویر هوایی رقومی در جنگلهای پهن برگ شصت کلاته گرگان. رساله دکتری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان. 241 صفحه.
6. مروی مهاجر، م. 1389. جنگل‌شناسی و پرورش جنگل. انتشارات دانشگاه تهران، 387 صفحه.
7. مقصودی، ی. و س. مهدوی. 1395. مبانی سنجش از دور راداری. انتشارات دانشگاه خواجه نصیرالدین طوسی، 287 صفحه.
8. وفایی، س.، ج. سوسنی، ک. عادلی، ه. فدایی و ح. نقوی. 1396. برآورد زی‌توده روی زمینی درختان جنگل با استفاده از تصویرهای نوری و راداری (مطالعه موردی: حوضه ناو اسالم گیلان). تحقیقات جنگل و صنوبر ایران، 25(2): 320-331.
9. Attarchi S, Gloaguen R. 2014. Improving the estimation of above ground biomass using dual polarimetric PALSAR and ETM+ data in the Hyrcanian mountain forest (Iran). Remote Sensing, 6(5): 3693-3715.
10. Banskota A, Wynne RH, Johnson P, Emessiene B. 2011. Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests. Annals of Forest Science, 68(2): 347-356.
11. Bergen K, Goetz S, Dubayah R, Henebry G, Hunsaker C, Imhoff M, Nelson R, Parker G, Radeloff V. 2009. Remote sensing of vegetation 3‐D structure for biodiversity and habitat: review and implications for lidar and radar spaceborne missions. Journal of Geophysical Research: Biogeosciences, 114(6): 1-13.
12. Brandtberg T, Warner TA, Landenberger RE, McGraw JB. 2003. Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote sensing of Environment, 85(3): 290-303.
13. Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32.
14. Carreiras JM, Vasconcelos MJ, Lucas RM. 2012. Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sensing of Environment, 121: 426-442.
15. Cartus O, Kellndorfer J, Rombach M, Walker W. 2012. Mapping canopy height and growing stock volume using airborne lidar, ALOS PALSAR and Landsat ETM+. Remote Sensing, 4(11): 3320-3345.
16. Chen Q, Baldocchi D, Gong P, Kelly M. 2006. Isolating individual trees in a savanna woodland using small footprint lidar data. Photogrammetric Engineering & Remote Sensing, 72(8): 923-932.
17. Chowdhury TA, Thiel C, Schmullius C. 2014. Growing stock volume estimation from L-band ALOS PALSAR polarimetric coherence in Siberian forest. Remote Sensing of Environment, 155: 129-144.
18. Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ. 2007. Random forests for classification in ecology. Ecology, 88(11): 2783-2792.
19. Dalpone M, Tonolli S, Vescovo L, Neteler M, Gianelle D. 2010. Fusion of multispectral and Lidar remote sensing data for the estimation of forest attributes in an Alpine region. The 10th International Conference on LIDAR Applications for Assessing Forest Ecosystems, Germany, 14-17 September.
20. Hollaus M, Wagner W, Maier B, Schadauer K. 2007. Airborne laser scanning of forest stem volume in a mountainous environment. Sensors, 7(8): 1559-1577.
21. Holopainen M, Haapanen R, Karjalainen M, Vastaranta M, Hyyppä J, Yu X, Tuominen S, Hyyppä H. 2010. Comparing accuracy of airborne laser scanning and TerraSAR-X radar images in the estimation of plot-level forest variables. Remote Sensing, 2(2): 432-445.
22. Hyde P, Dubayah R, Walker W, Blair JB, Hofton M, Hunsaker C. 2006. Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sensing of Environment, 102(1-2): 63-73.
23. Hyyppa J. 1999. Detecting and estimating attributes for single trees using laser scanner. Photogramm Journal of Finland, 16: 27-42.
24. Ioki K, Imanishi J, Sasaki T, Morimoto Y, Kitada K. 2010. Estimating stand volume in broad-leaved forest using discrete-return LiDAR: plot-based approach. Landscape and Ecological Engineering, 6(1): 29-36.
25. Kaasalainen S, Holopainen M, Karjalainen M, Vastaranta M, Kankare V, Karila K, Osmanoglu B. 2015. Combining lidar and synthetic aperture radar data to estimate forest biomass: status and prospects. Forests, 6(1): 252-270.
26. Kalbi S, Fallah A, Shataee S. 2014. Estimation of forest attributes in the Hyrcanian forests, comparison of advanced space-borne thermal emission and reflection radiometer and satellite poure I’observation de la terre-high resolution grounding data by multiple linear, and classification and regression tree regression models. Journal of Applied Remote Sensing, 8(1): 2-18.
27. Koch B, Heyder U, Weinacker H. 2006. Detection of individual tree crowns in airborne lidar data. Photogrammetric Engineering & Remote Sensing, 72(4): 357-363.
28. Latifi H, Nothdurft A, Koch B. 2010. Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors. Forestry, 83(4): 395-407.
29. Lee H, Slatton KC, Roth BE, Cropper Jr W. 2010. Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests. International Journal of Remote Sensing, 31(1): 117-139.
30. Lee J, Pottier SE. 2009. Polarimetric Radar imaging: from basics to applications. Boca Raton, FL, USA: CRC press, 422 pp.
31. Lim K, Treitz P, Wulder M, St-Onge B, Flood M. 2003. LiDAR remote sensing of forest structure. Progress in Physical Geography, 27(1): 88-106.
32. Lu D, Mausel P, Brondızio E, Moran E. 2004. Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198(1-3): 149-167.
33. Lu D. 2006. The potential and challenge of remote sensing‐based biomass estimation. International Journal of Remote Sensing, 27(7): 1297-1328.
34. Mäkelä H, Pekkarinen A. 2004. Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data. Forest Ecology and Management, 196(2-3): 245-255.
35. Maltamo M, Malinen J, Packalén P, Suvanto A, Kangas J. 2006. Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data. Canadian Journal of Forest Research, 36(2): 426-436.
36. Maltamo M, Mustonen K, Hyyppä J, Pitkänen J, Yu X. 2004. The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve. Canadian Journal of Forest Research, 34(9): 1791-1801.
37. Mitchard ET, Saatchi SS, White L, Abernethy K, Jeffery KJ, Lewis SL, Collins M, Lefsky MA, Leal ME, Woodhouse IH. 2012. Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park, Gabon: overcoming problems of high biomass and persistent cloud. Biogeosciences, 9(1): 179-191.
38. Mohammadi J, Shataee Joibary S, Yaghmaee F, Mahiny A. 2010. Modelling forest stand volume and tree density using Landsat ETM+ data. International Journal of Remote Sensing, 31(11): 2959-2975.
39. Mohammadi J, Shataee S, Namiranian M, Næsset E. 2017. Modeling biophysical properties of broad-leaved stands in the hyrcanian forests of Iran using fused airborne laser scanner data and ultraCam-D images. International Journal of Applied Earth Observation and Geoinformation, 61: 32-45.
40. Næsset E, Bjerknes K-O. 2001. Estimating tree heights and number of stems in young forest stands using airborne laser scanner data. Remote Sensing of Environment, 78(3): 328-340.
41. Packalén P, Maltamo M. 2007. The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs. Remote Sensing of Environment, 109(3): 328-341.
42. Santoro M, Eriksson L, Askne J, Schmullius C. 2006. Assessment of stand‐wise stem volume retrieval in boreal forest from JERS‐1 L‐band SAR backscatter. International Journal of Remote Sensing, 27(16): 3425-3454.
43. Shataee S, Kalbi S, Fallah A, Pelz D. 2012. Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms. International Journal of Remote Sensing, 33(19): 6254-6280.
44. Shataee S. 2013. Forest attributes estimation using aerial laser scanner and TM data. Forest Systems, 22(3): 484-496.
45. Shataeea S, Weinaker H, Babanejad M. 2011. Plot-level forest volume estimation using airborne laser scanner and TM data, comparison of boosting and random forest tree regression algorithms. Procedia Environmental Sciences, 7: 68-73.
46. Sithole G, Vosselman G. 2004. Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 59(1-2): 85-101.
47. Solberg S, Astrup R, Breidenbach J, Nilsen B, Weydahl D. 2013. Monitoring spruce volume and biomass with InSAR data from TanDEM-X. Remote Sensing of Environment, 139: 60-67.
48. Thapa RB, Watanabe M, Motohka T, Shimada M. 2015. Potential of high-resolution ALOS–PALSAR mosaic texture for aboveground forest carbon tracking in tropical region. Remote Sensing of Environment, 160: 122-133.
49. Tsui OW, Coops NC, Wulder MA, Marshall PL, McCardle A. 2012. Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest. ISPRS Journal of Photogrammetry and Remote Sensing, 69: 121-133.
50. Tsui OW, Coops NC, Wulder MA, Marshall PL. 2013. Integrating airborne LiDAR and space-borne radar via multivariate kriging to estimate above-ground biomass. Remote Sensing of Environment, 139: 340-352.
51. Vincent G, Sabatier D, Blanc L, Chave J, Weissenbacher E, Pélissier R, Fonty E, Molino J-F, Couteron P. 2012. Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure. Remote Sensing of Environment, 125: 23-33.
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