Modeling of the spatial point pattern of hillside and stream using unmanned aerial vehicle in a part of loess plateau, Golestan province

Document Type : Complete scientific research article

Authors

1 PhD graduated, Department of Soil Science, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

2 Department of Soil Science, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

3 Department of Watershed and Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

Abstract

Background and Objectives: Understanding the ecological and geomorphological processes of the spatial distribution of loess deposits helps us to find their interactions in the arid and semi-arid regions. The Iranian loess plateau with a unique landscape and complex topography also located in steppe vegetation with semi-arid climate. The aims of present study are point pattern analysis of hillside, stream, and their interactions using different summary statistics in the particular part of Iranian loess plateau. As there is a small distance between hillslope and complex topography in the study area, unmanned aerial vehicle (UAV) imagery used to prepare precise colored aerial photos for statistical analyses. Further, regarding to the mentioned purposes, this research has enough novelty compare with previous studies, and thus, it is a new step in studying the spatial pattern of loess facies; In other words, this study attempts to find the effective relationship between the hillside and streams in terms of the expansion of channel erosion in the study region.
Materials and Methods: The study area has dry Xeric soil moisture and Thermic soil temperature regimes. The UAV technique used to prepare the precise colorful images with highly spatial/temporal resolution to model the spatial patterns of hillside and streams density. The topographic attributes (primary and secondary) obtained from digital elevation model (DEM) applied with a spatial resolution of 20×20 cm. The univariate and bivariate point analyses (modelling) used for variable analyses in Spatstat package in R and Progammita software. Finally, Mark correlation function (MCF) used to investigate the question of reducing the size of density dependent.
Results: The results of univariate g(r) and O-ring (r) showed that different hillside facing i.e. flat, north, south, east, and west have the aggregated pattern in all distances in the study area. These implied that the same directions are distributed more closely next to each other and their arrangements follow the special pattern. In addition, the interaction between streams and north-face using the bivariate g12(r) and O12(r) confirmed the positive interaction between streams and north-face in all distances in the study area. The MCF analysis also showed that the slope as the effective factor has positive interaction with streams, and steep slopes are more aggregated compare with the low slopes. This indicates which the steeper slopes are more likely to form streams than flat lands.
Conclusion: Generally, the streams probability form in the steep slopes more than flat areas due to their shear energy, which leads to more soil losses. Further, the soil loss on the steep slopes is more than flat terrain. Finally, UAV technologies are recommended in the study of spatial pattern of deposits for detailed observations, highly accurate data, and deciding natural resource managers to reduce soil erosion.

Keywords


1.Amini, A., and Najafinezhad, A. 1998. Effect of loess and loess- like in economic development of province. Proceedings of the 1th congress of capabilities of Golestan province.Pp: 13-15. (In Persian)
2.Ayalew, L., and Yamagishi, H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology. 65: 15-31.
3.Barneveld, R., Seeger, M., and Maalen-Johansen, I. 2013. Assessment of terrestrial laser scanning technology for obtaining high-resolution DEMs of soils. Earth Surf. Process. Landf. 38: 90-94.
4.Chang, B., and Chen, Y. 1983. Basic principles and methods of digital differential correction. J. Surv. Mapp.3: 31-40.
5.Churchill, D., Larson, A., Dahlgreen, M., Franklin, J., Hessburg, P., and Luts, J. 2013. Restoring forest resilience: from reference spatial patterns to silvicultural prescriptions and monitoring. Forest Ecol. Manag. 291: 442-457.
6.Cipriotti, P.A., Aguiar, M.R., Wiegand, T., and Paruelo, J.M. 2014. A complex network of interactions controls coexistence and relative abundances in Patagonian grass-shrub steppes. J. Ecol. 102: 776-788.
7.Cook, K.L. 2017. An evaluation ofthe effectiveness of low-cost UAVsand structure from motion for geomorphic change detection. Geomorphology.278: 195-208.
8.Dà-Jiāng Innovations Science and Technology Co (DJI). 2016. Phantom 3 Professional User Manual v1.8; DJI: Shenzhen, China, 59p.
9.Dale, M.R.T., and Powell, R.D. 2001.A new method for characterizing point patterns in plant ecology. J. Veg. Sci.12: 597-608.
10.Diggle, P.J. 2003. Statistical analysisof point processes. Academic Press, London, 240p.
11.Eltner, A., Mulsow, C., and Maas, H.G. 2013. Quantitative measurement of soil erosion from TLS and UAV data. ISPRS. XL-1/W2, UAV. 4-6 September, Rostock, Germany, 119-124.  
12.Fabris, M., and Pesci, A. 2005. Automated DEM extraction in digital aerial photogrammetry: precision and validation for mass movement monitoring. Ann. Geophys. 48: 973-988.
13.Fronzek, S., Carter, R., Rasanen, J., Ruokolainen, L., and Luoto, M. 2010. Applying probabilistic projections of climate change with impact models:a case study for subarctic palsa mires
in Fennoscandia. Climatic Change.99: 515-534.
14.Genet, A., Grabarnik, P., Sekretenko, O., and Pothier, D. 2014. Incorporating the mechanisms underlying inter-tree competition into a random point process model to improve spatial tree pattern analysis in forestry. Ecol. Model.288: 143-154.
15.Hengl, T., Gruber, S., and Shrestha, D. 2003. Digital Terrain Analysis in ILWIS. Lecture notes, International Institute for Geo-Information Science and Earth Observation (ITC) Enschede, 62p.
16.Höhle, J. 2009. Dem generation using a digital large-format frame camera. Photogramm. Eng. Remote. Sens.75: 87-93.
17.Hosseinalizadeh, M., Kariminejad, N., Alinejad, M., and Mohammadian Behbahani, A. 2018a. The spatial association between Halocnemum strobliaceum and Nebkas in Northof Golestan Province, Iran. DEEJ.1: 2. 55-66.
18.Hosseinalizadeh, M., Kariminejad, N., Campetella, G., Jalalifard, A., and Alinejad, M. 2018b. Spatial point pattern analysis of piping erosion in loess-derived soils in Golestan Province, Iran. Geoderma. 328: 20-29.
19.Hosseinalizadeh, M., Kariminejad, N., Chen, W., Pourghasemi, H.R., Alinejad, M., Mohammadian Behbahani, A., and Tiefenbacher, J.P. 2019. Spatial modelling of gully headcuts using UAV data and four best-first decision classifier ensembles (BFTree, Bag-BFTree, RS-BFTree, and RF-BFTree). Geomorphology. 329: 184-193.
20.Hu, Sh., Qiu, H., Xingang Wang, X., Gao, Y., Wang, N., Wu, J., Yang, D., and Cao, M. 2018. Acquiring high-resolution topography and performing spatial analysis of loess landslides
by using low-cost UAVs. Landslides. 15: 593-612.
21.Illian, J., Penttinen, A., Stoyan, H., and Stoyan, D. 2008. Statistical analysis and modeling of spatial point patterns. John Wiley & Sons Inc, 534p.
22.Kehl, M., and Khormali, F. 2014. Excursion book of international symposium on loess, soils & climate change in southern Eurasia. Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran, 51p.
23.Knapen, A., and Poesen, J. 2010. Soil erosion resistance effects on rill and gully initiation points and dimensions. Earth Surf. Process. Landf. 35: 217-228.
24.Kramm, T., Hoffmeister, D., Curdt,C., Maleki, S., Khormali, F., and Kehl, M., 2017. Accuracy assessment of landform classification approaches on different spatial scales for the Iranian loess plateau. ISPRS Int. J. Geo-Inf.6: 366. 1-22.
25.Karimi, A.R. 2008. Survey of soil development and determination of origin and dating of silty sediment in landforms at Mashhad. A thesis for the degree of PhD in soil sciences. Isfahan University. 108p. (In Persian)
26.Kung, O., Strecha, C., Beyeler, A., Zufferey, J.C., Floreano, D., Fua, P., and Gervaix, F. 2011. The accuracy of automatic photogrammetric techniques on ultra-light UAV imagery. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. XXXVIII: 1-7.
27.Laliberte, A., Herrick, J., Rango, A.,and Winters, C. 2010. Acquisition, orthorectification, and object-based classification of unmanned aerialvehicle (UAV) imagery for rangeland monitoring. Photogramm. Eng. Remote. Sens. 76: 661-672.
28.Liu, Y., Zheng, X., Ai, G., Zhang, Y., and Zuo, Y. 2018. Generating a High-Precision True Digital Orthophoto Map Based on UAV Images. International Journal of Geo-Information (ISPRS).
7: 333.
29.Lozano-García, B., Parras-Alcántara, L., and Brevik, E.C. 2016. Impact of topographic aspect and vegetation (native and reforested areas) on soil organic carbon and nitrogen budgets in Mediterranean natural areas. Sci. Total Environ. 544: 963-970.
30.Maleki, S., Khormali, F., and Karimi, A.R. 2014. Introducing different flow direction algorithms to map topographic wetness index and soil organic carbon in a loess hillslope of Toshan area, Golestan Province, Iran. J. Water Soil Cons. 21: 1. 145-162. (In Persian)
31.Maleki, S. 2018. Effect of accuracy of topographic data on improving estimations of digital soil mapping (Case study: a part of loess plateau, Golestan Province. A thesis for the degree of PhD in soil sciences. Gorgan University of Agricultural Sciences and Natural Resources. 153p. (In Persian)
32.Maleki, S., Khormali, F., Bagheri Bodaghabadi, M., Mohammadi, J., Hoffmeister, D., and Kehl, M. 2018. Role of geomorphic surface on the above-ground biomass and soil organic carbon storage in a semi-arid region of Iranian loess plateau. Quaternary International. Pp: 1-22. (In Press)
33.Mlambo, R., Woodhouse, I., Gerard, F., and Anderson, K. 2017. Structure from motion (SfM) Photogrammetry with drone data: a low cost method for monitoring greenhouse gas emissions from forests in developing countries. Forests. 8: 68.
34.Nelson, A., Reuter, H.I., and Gessler, P. 2009. DEM production methods and sources. Geomorphometry: concepts, software, applications. Dev. Soil Sci.33: 65-85.
35.Phillips, J.D. 2009. Changes, perturbations and responses in geomorphic systems. Prog. Phys. Geogr. 33: 1-14.
36.Pommerening, A., and Stoyan, D. 2008. Edgecorrection needs in estimating indices of spatial forest structure. Can. J. For. Res. 36: 1723-1739.
37.R Development Core Team. 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org.
38.Teka, K., Nyssen, J., Teha, N., Haile, M., and Deckers, J. 2015. Soil, land use and landform relationship in the Precambrian lowlands of northern Ethiopia. Catena. 131: 84-91.
39.Torri, D., and Poesen, J. 2014. Areview of topographic threshold conditions for gully head development in different environments. Earth Sci. Rev. 130: 73-85.
40.Vaezi, A.R., Gharehdaghli, H., and Marzvan, S. 2016. The role of slope steepness and soil properties in rill erosion in the hillslopes (A case study: Taham Chai catchment, NW Zanjan).
J. Water Soil Cons. 23: 4. 83-100.(In Persian)
41.Vega, F., Ramírez, F., Siaz, M., and Rosua, F. 2015. Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop. Biosyst. Eng. 132: 19-27.
42.Wang, X., Wei, H., Khormali, F., Taheri, M., Kehl, M., Frechen, M., Lauer, M., and Chen, M. 2016. Grain-size distribution of Pleistocene loess deposits in northern Iran and its palaeoclimatic implications. Quaternary International. 429: 1-11.
43.Wiegand, T., and Moloney, K.A. 2014. Handbook of spatial point-pattern analysis in ecology. CRC Press, New York, 538p.
44.Yu, H., Wiegand, T., Yang, X., and Ci, L. 2009. The impact of fire and density-dependent mortality on the spatial patterns of a pine forest in the Hulun Buir sandland, Inner Mongolia, China. For. Ecol. Manage. 257: 2098-2107.
45.Zare, L., Erfani Fard, S., and Karim Nejad, N. 2015. Effectiveness of distance sampling in the estimation of biochemical properties Pistacia atlantica subs. Mutica masses in Zagros. Research on Science and Technology of Wood and Forest. 125: 23-144.(In Persian).
46.Zongjian, L. 2008. UAV for mapping-low altitude photogrammetric survey. Int. Arch. Photogramm. Remote. Sens. Beijing China. 37: 1183-1186.
47.Zoratipour, A., and Moazami, M. 2016. The participation of hill slopes sediment delivery contribution in rainfalls different patterns by determine of the degraded rills volume. 23: 33. 327-336. (In Persian)