Multispectral remote sensing data are effective and robust in mapping regional forest soil organic carbon stocks in a northeast forest region in China

Remote Sensing

Volumn 12, Issue 3, 2020, Pages

  Wang, Shuai ^a,b,c   Gao, Jinhu ^d   Zhuang, Qianlai ^b   Lu, Yuanyuan ^e   Gu, Hanlong ^a   Jin, Xinxin ^a  

^a SHENYANG AGRICULTURAL UNIVERSITY   (China)

^b PURDUE UNIVERSITY   (United States)

^c INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^d Shanxi Academy of Agricultural Sciences   (China)

^e NANJING INSTITUTE OF ENVIRONMENTAL SCIENCES   (China)

Author keywords

Forestry ecology; Multispectral remote sensing; Soil organic carbon stocks; Spatial variation

Indexed keywords

CLIMATE MODELS; ECOSYSTEMS; FORESTRY; INFORMATION MANAGEMENT; MAPPING; MEAN SQUARE ERROR; ORGANIC CARBON; SOILS; TOPOGRAPHY;

BOOSTED REGRESSION TREES; ENVIRONMENT PROTECTION; ENVIRONMENTAL VARIABLES; MULTISPECTRAL REMOTE SENSING; ROOT MEAN SQUARE ERRORS; SATELLITE REMOTE SENSING; SOIL ORGANIC CARBON STOCKS; SPATIAL VARIATIONS;

REMOTE SENSING;

EID: 85080954631     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs12030393     Document Type: Article

References (43)

1. Liski, J.; Perruchoud, D.; Karjalainen, T. Increasing carbon stocks in the forest soils of western Europe. For. Ecol. Manag. 2002, 169, 159-175. [CrossRef]
2. Rapalee, G.; Trumbore, S.E.; Davidson, E.A.; Harden, J.W.; Veldhuis, H. Soil carbon stocks and their rates of accumulation and loss in a boreal forest landscape. Glob. Biogeochem. Cy. 1998, 12, 687-701. [CrossRef]
3. Fujisaki, K.; Perrin, A.S.; Desjardins, T.; Bernoux, M.; Balbino, L.C.; Brossard, M. From forest to cropland and pasture systems: A critical review of soil organic carbon stocks changes in Amazonia. Glob. Chang. Biol. 2015, 21, 2773-2786. [CrossRef] [PubMed]
4. Grinand, C.; Le Maire, G.; Vieilledent, G.; Razakamanarivo, H.; Razafimbelo, T.; Bernoux, M. Estimating temporal changes in soil carbon stocks at ecoregional scale in Madagascar using remote-sensing. Int. J. Appl. Earth Obs. 2017, 54, 1-14. [CrossRef]
5. Willaarts, B.A.; Oyonarte, C.; Munoz-Rojas, M.; Ibânez, J.J.; Aguilera, P.A. Environmental factors controlling soil organic carbon stocks in two contrasting Mediterranean climatic areas of southern Spain. Land. Degrad. Dev. 2016, 27, 603-611. [CrossRef]
6. Conforti, M.; Lucà, F.; Scarciglia, F.; Matteucci, G.; Buttafuoco, G. Soil carbon stock in relation to soil properties and landscape position in a forest ecosystem of southern Italy (Calabria region). Catena 2016, 144, 23-33. [CrossRef]
7. Qi, L.; Wang, S.; Zhuang, Q.; Yang, Z.; Bai, S.; Jin, X.; Lei, G. Spatial-temporal changes in soil organic carbon and pH in the Liaoning Province of China: A modeling analysis based on observational data. Sustainability 2019, 11, 3569. [CrossRef]
8. Wang, S.; Wang, Q.; Adhikari, K.; Jia, S.; Jin, X.; Liu, H. Spatial-temporal changes of soil organic carbon content in Wafangdian, China. Sustainability 2016, 8, 1154. [CrossRef]
9. Ottoy, S.; De Vos, B.; Sindayihebura, A.; Hermy, M.; Van Orshoven, J. Assessing soil organic carbon stocks under current and potential forest cover using digital soil mapping and spatial generalisation. Ecol. Indic. 2017, 77, 139-150. [CrossRef]
10. Rasel, S.M.M.; Groen, T.A.; Hussin, Y.A.; Diti, I.J. Proxies for soil organic carbon derived from remote sensing. Int. J. Appl. Earth Obs. 2017, 59, 157-166. [CrossRef]
11. Gahlod, N.S.S.; Jaryal, N.; Roodagi, M.; Dhale, S.A.; Kumar, D.; Kulkarni, R. Soil organic carbon stocks assessment in Uttarakhand State using remote sensing and GIS technique. Int. J. Curr. Microbiol. Appl. Sci. 2019, 8, 1646-1658. [CrossRef]
12. Chen, F.; Kissel, D.E.; West, L.T.; Adkins, W. Field-scale mapping of surface soil organic carbon using remotely sensed imagery. Soil Sci. Soc. Am. J. 2000, 64, 746-753. [CrossRef]
13. Fox, G.A.; Sabbagh, G.J. Estimation of soil organic matter from red and near-infrared remotely sensed data using a soil line Euclidean distance technique. Soil Sci. Soc. Am. J. 2000, 66, 1922-1929. [CrossRef]
14. Hill, J.; Schutt, B. Mapping complex patterns of erosion and stability in dry Mediterranean ecosystems. Remote Sens. Enviro. 2000, 74, 557-569. [CrossRef]
15. Elith, J.; Leathwick, J.R.; Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77, 802-813. [CrossRef]
16. Friedman, J.; Hastie, T.; Tibshirani, R. Additive logistic regression: A statistical view of boosting. Ann. Stat. 2000, 28, 337-407. [CrossRef]
17. Yang, R.M.; Zhang, G.L.; Liu, F.; Lu, Y.Y.; Yang, F.; Yang, F.; Yang, M.; Zhao, Y.G.; Li, D.C. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecol. Indic. 2016, 60, 870-878. [CrossRef]
18. Carslaw, D.C.; Taylor, P.J. Analysis of air pollution data at a mixed source location using boosted regression trees. Atmos. Environ. 2009, 43, 3563-3570. [CrossRef]
19. Yoon, L.C.; Pedro, J.; Leitâo, T.L. Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees. Spat. Spatio Tempor. Epidemiol. 2014, 10, 75-84.
20. De'Ath, G. Boosted trees for ecological modeling and prediction. Ecology 2007, 88, 243-251. [CrossRef]
21. Pouteau, R.; Rambal, S.; Ratte, J.P.; Gogé, F.; Joffre, R.; Winkel, T. Downscaling MODIS-derived maps using GIS and boosted regression trees: The case of frost occurrence over the arid Andean highlands of Bolivia. Remote Sens. Environ. 2011, 115, 117-129. [CrossRef]
22. Lampa, E.; Lind, L.; Lind, P.M.; Bornefalk-Hermansson, A. The identification of complex interactions in epidemiology and toxicology: A simulation study of boosted regression trees. Environ. Health 2014, 13, 57. [CrossRef] [PubMed]
23. Kim, J.; Grunwald, S. Assessment of carbon stocks in the topsoil using random forest and remote sensing images. J. Environ. Qual. 2016, 45, 1910-1918. [CrossRef] [PubMed]
24. Yimer, F.; Ledin, S.; Abdelkadir, A. Soil organic carbon and total nitrogen stocks as affected by topographic aspect and vegetation in the Bale Mountains, Ethiopia. Geoderma 2006, 135, 335-344. [CrossRef]
25. Schad, P.; Van Huyssteen, C.; Micheli, E. International Soil Classification System for Naming Soils and Creating Legends for Soil Maps; World Reference Base for Soil Resources: Rome, Italy, 2014; Volume 106, pp. 246-385.
26. Zhu, A.X.; Yang, L.; Li, B.; Qin, C.; English, E.; Burt, J.E.; Zhou, C.H. Purposive sampling for digital soil mapping for areas with limited data. In Digital Soil Mapping with Limited Data; Hartemink, A.E., McBratney, A.B., de Lourdes, M.-S., Eds.; Springer: Amsterdam, The Netherlands, 2008; Volume 8, pp. 233-245.
27. Yang, L.; Zhu, A.; Qin, C.; Li, B.; Pei, T. A soil sampling method based on representativeness grade of sampling points. Acta Pedol. Sin. 2011, 48, 938-946.
28. Yang, L.; Zhu, A.; Qin, C.; Li, B.; Pei, T.; Liu, B. Soil property mapping using fuzzy membership-a case study of a study area in Heshan Farm of Heilongjiang Province. Acta Pedol. Sin. 2009, 46, 9-15.
29. Sundaresan, A.; Varshney, P.K.; Arora, M.K. Robustness of change detection algorithms in the presence of registration errors. Photogramm. Eng. Rem. Sens. 2007, 73, 375-383. [CrossRef]
30. Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Enviro. 1988, 25, 295-309. [CrossRef]
31. Olaya, V.F. A Gentle Introduction to Saga GIS.; The SAGA User Group eV: Göttingen, Germany, 2004.
32. Gbm: Generalized Boosted Regression Models, R Package Version 1.6-3. Available online: http://127.0.0.1:31000/library/gbm/html/gbm-package.html (accessed on 26 November 2007).
33. Lin, L. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989, 45, 255-268. [CrossRef]
34. Wang, B.; Waters, C.; Orgill, S.; Gray, J.; Cowie, A.; Clark, A.; Li Liu, D. High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia. Sci. Total Environ. 2018, 630, 367-378. [CrossRef]
35. Nyssen, J.; Temesgen, H.; Lemenih, M.; Zenebe, A.; Haregeweyn, N.; Haile, M. Spatial and temporal variation of soil organic carbon stocks in a lake retreat area of the Ethiopian Rift Valley. Geoderma 2008, 146, 261-268. [CrossRef]
36. Xu, X.; Liu, W.; Zhang, C.; Kiely, G. Estimation of soil organic carbon stock and its spatial distribution in the Republic of Ireland. Soil Use Manag. 2011, 27, 156-162. [CrossRef]
37. Chi, Y.; Shi, H.; Zheng, W.; Sun, J. Simulating spatial distribution of coastal soil carbon content using a comprehensive land surface factor system based on remote sensing. Sci. Total Environ. 2018, 628, 384-399. [CrossRef] [PubMed]
38. Gomez, C.; Rossel, R.A.V.; McBratney, A.B. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma 2008, 146, 403-411. [CrossRef]
39. Winowiecki, L.; Vâgen, T.G.; Huising, J. Effects of land cover on ecosystem services in Tanzania: A spatial assessment of soil organic carbon. Geoderma 2016, 263, 274-283. [CrossRef]
40. Jenny, H. Factors of Soil Formation: A System of Quantitative Pedology; Beth, R., Ed.; Soil Science: Philadelphia, PA, USA, 1941; Volume 42, p. 415.
41. Adhikari, K.; Hartemink, A.E.; Minasny, B.; Kheir, R.B.; Greve, M.B.; Greve, M.H. Digital mapping of soil organic carbon contents and stocks in Denmark. PLoS ONE 2014, 9, e105519. [CrossRef] [PubMed]
42. Were, K.; Bui, D.T.; Dick, 0.B.; Singh, B.R. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol. Indic. 2015, 52, 394-403. [CrossRef]
43. Wang, S.; Zhuang, Q.; Yang, Z.; Yu, N.; Jin, X. Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China. Forests 2019, 10, 1023. [CrossRef]