Compilation of 1:50,000 vegetation type map with remote sensing images based on mountain altitudinal belts of Taibai Mountain in the North-South transitional zone of China

doi:10.1007/s11442-020-1727-6

Abstract

Abstract:

The compilation of 1:250,000 vegetation type map in the North-South transitional zone and 1:50,000 vegetation type maps in typical mountainous areas is one of the main tasks of Integrated Scientific Investigation of the North-South Transitional Zone of China. In the past, vegetation type maps were compiled by a large number of ground field surveys. Although the field survey method is accurate, it is not only time-consuming, but also only covers a small area due to the limitations of physical environment conditions. Remote sensing data can make up for the limitation of field survey because of its full coverage. However, there are still some difficulties and bottlenecks in the extraction of remote sensing information of vegetation types, especially in the automatic extraction. As an example of the compilation of 1:50,000 vegetation type map, this paper explores and studies the remote sensing extraction and mapping methods of vegetation type with medium and large scales based on mountain altitudinal belts of Taibai Mountain, using multi-temporal high resolution remote sensing data, ground survey data, previous vegetation type map and forest survey data. The results show that: 1) mountain altitudinal belts can effectively support remote sensing classification and mapping of 1:50,000 vegetation type map in mountain areas. Terrain constraint factors with mountain altitudinal belt information can be generated by mountain altitudinal belts and 1:10,000 Digital Surface Model (DSM) data of Taibai Mountain. Combining the terrain constraint factors with multi-temporal and high-resolution remote sensing data, ground survey data and previous small-scale vegetation type map data, the vegetation types at all levels can be extracted effectively. 2) The basic remote sensing interpretation and mapping process for typical mountains is interpretation of vegetation type-groups→interpretation of vegetation formation groups, formations and subformations→interpretation and classification of vegetation types & subtypes, which is a combination method of top-down method and bottom-up method, not the top-down or the bottom-up classification according to the level of mapping units. The results of this study provide a demonstration and scientific basis for the compilation of large and medium scale vegetation type maps.

Key words: vegetation type map, high resolution remote sensing data, mountain altitudinal belts, remote sensing interpretation, Taibai Mountain

YAO Yonghui, SUONAN Dongzhu, ZHANG Junyao. Compilation of 1:50,000 vegetation type map with remote sensing images based on mountain altitudinal belts of Taibai Mountain in the North-South transitional zone of China[J].Journal of Geographical Sciences, 2020, 30(2): 267-280.

Figures/Tables 7

Figure 1

Figure 2

Table 1

Table 2

Vegetation classification system of Taibai Mountain"

Vegetation type groups	Vegetation types, sub-types	Vegetation formation groups, formations, sub-formation
Meadows	Kobresia, forbs alpine meadows	Polygonum macrophyllum D. D, Polygonum viviparum meadow
	Temperate grasses, forbs meadows	Carex lanceolata, grasses meadow
	Temperate grasses, forbs meadows	Carex lanceolata, forbs meadow
Shrubs	Subalpine deciduous broad-leaved shrubs	Salix cupularis shrub
	Subalpine deciduous broad-leaved shrubs	Spiraea mongolica shrub
	Subalpine evergreen leather-leaved shrubs	Rhododendron lapponicum shrub
		Rhododendron clementinae subsp. Aureodorsale shrub
		Rhododendron purdomii shrub
		Rhododendron capitatum shrub
Coniferous forests	Temperate coniferous forests	Platycladus orientalis forest
	Temperate coniferous forests	Pinus tabuliformis forest
	Subtropical and tropical montane coniferous forests	Abies fargesii, Larix chinensis forest
		Abies fargesii forest
		Larix chinensis forest
	Subtropical coniferous forests	Pinus armandii, Larix chinensis forest
	Subtropical coniferous forests	Pinus armandii forest
Mixed forests	Temperate coniferous and deciduous broad-leaved mixed forests	Platycladus orientalis, Quercus aliena var. acuteserrata forest
		Platycladus orientalis, Quercus variabilis forest
		Pinus tabuliformis, Quercus serrata var. brevipetiolata forest
		Pinus tabuliformis, Quercus aliena var. acuteserrata forest
		Pinus tabuliformis, quercus variabilis forest
	Subtropical montane coniferous and deciduous broad-leaved mixed forests	Abies fargesii, Betula albo-sinensis forest
		Abies fargesii, Betula albo-sinensis var. septentrionalis forest
		Pinus armandii, Quercus spinosa forest
		Pinus armandii, Quercus serrata var. brevipetiolata forest
		Pinus armandii, Betula albo-sinensis forest
		Pinus armandii, Quercus baronii forest
		Pinus armandii, Quercus liaotungensis forest
		Pinus armandii, Betula albo-sinensis var. septentrionalis forest
		Pinus armandii, Quercus aliena var. acuteserrata forest
		Pinus armandii, Fagus longipetiolata forest
		Pinus armandii, Populus davidiana forest
		Pinus armandii, Quercus variabilis forest
Broadleaf forests	Temperate deciduous broad-leaved forests	Betula albo-sinensis, Quercus liaotungensis forest
		Betula albo-sinensis forest
		Quercus baronii forest
		Quercus liaotungensis forest
Vegetation type groups	Vegetation types, sub-types	Vegetation formation groups, formations, sub-formation
		Betula albo-sinensis var. septentrionalis forest
		Quercus aliena var. acuteserrata, Pterocarya stenoptera forest
		Quercus aliena var. acuteserrata, Quercus acutissima forest
		Quercus aliena var. acuteserrata, Populus davidiana forest
		Quercus aliena var. acuteserrata forest
		Populus davidiana, Quercus liaotungensis forest
		Populus davidiana forest
		Quercus variabilis, Quercus aliena var. acuteserrata forest
		Quercus variabilis, Populus simonii forest
		Quercus variabilis, Ulmus pumila forest
		Quercus variabilis forest
Cultivated plants	Dry farming of three matures in two years or double cropping in one year	Triticum aestivum, Zea mays Linn. Sorghum bicolor (L.) Moench
Cultivated plants	Deciduous orchard	Castanea mollissima BL., Juglans regia L.

Table 2

Figure 3

Figure 4

Figure 5

References 26

[1]	Chen B Q, Li X P, Xiao X M et al., 2016. Mapping tropical forests and deciduous rubber plantations in Hainan Island, China by integrating PALSAR 25-m and multi-temporal Landsat images. International Journal of Applied Earth Observation and Geoinformation, 50:117-130.
[2]	Chinese Vegetation Map Editorial Committee (CVMEC), Chinese Academy of Sciences, 2000. Atlas of 1:1000000 Chinese Vegetation. Beijing: Science Press. (in Chinese)
[3]	Chinese Vegetation Map Editorial Committee (CVMEC), Chinese Academy of Sciences, 2007. Vegetation and Its Geographical Patterns in China: Instructions for Vegetation Maps of the People’s Republic of China. Beijing: Geology Press. (in Chinese)
[4]	Demir B, Bovolo F, Bruzzone L et al., 2013. Updating land-cover maps by classification of image time series: A novel change-detection-driven transfer learning approach. IEEE Transactions on Geoscience and Remote Sensing, 51(1):300-312.
[5]	Fang Z, Gao S Z Vegetation altitudinal belts on the north and south slopes of Taibai Mountain in Qinling Mountains. Series of Plant Ecology and Geobotany, 1963, ( 1):162-163. (in Chinese)
[6]	Hou X Y, Ma R Z , 1956. Vegetation-Soil Zoning Map of China. Beijing: Map Press. (in Chinese)
[7]	Hou X Y, Sun S Z, Zhang J W et al., 1979. Vegetation Map of the People’s Republic of China (1:4000000). Beijing: Map Press. (in Chinese)
[8]	Hu B, Ju H B, Liu Hua et al., 2017. Combining multiple classifiers based on evidence theory for large scale vegetation types classification by remote sensing images. Forestry Research, 30(2):194-199. (in Chinese)
[9]	Jia M M, Ren C Y, Liu D W et al., 2014. Object-oriented forest classification based on combination of HJ-1 CCD and MODIS NDVI data. Acta Ecologica Sinica, 34(24):7167-7174. (in Chinese)
[10]	Lei G B, Li A N, Tan J B et al., 2016. Forest types mapping in mountainous area using multi-source and multi-temporal satellite images and decision tree models. Remote Sensing Technology and Application, 31(1):31-41. (in Chinese)
[11]	Li H N Study on plant species diversity and vertical distribution patterns on the north slope of Taibai Mountain[D]. Xi’an: Shaanxi Normal University, 2007. (in Chinese)
[12]	Li Z, Yang X M, Meng F et al., 2013. LULC classification based on random forest with the aid of phonological feature. Remote Sensing Information, 28(6):48-55. (in Chinese)
[13]	Liu J Y , 1996. China Resources and Environment Remote Sensing Macro Survey and Dynamic Research. Beijing: China Science and Technology Press. (in Chinese)
[14]	Liu J Y, Liu M L, Zhuang D F et al., 2003a. Study on spatial pattern of land-use change in China during 1995-2000. Science in China Series D: Earth Sciences, 46(4):373-384.
[15]	Liu J Y, Zhang Z X, Zhuang D F et al., 2003b. A study on the spatial-temporal dynamic changes of land-use and driving forces analyses of China in the 1990s. Geographical Research, 22(1):1-12. (in Chinese)
[16]	Liu J Y, Zhang Z X, Xu X L et al., 2010. Spatial patterns and driving forces of land use change in China during the early 21st century. Journal of Geographical Sciences, 20(4):483-494.
[17]	Liu X F, Pan Y Z, Zhu X F et al., 2015. Spatiotemporal variation of vegetation coverage in Qinling-Daba Mountains in relation to environmental factors. Acta Geographica Sinica, 70(5):705-716. (in Chinese)
[18]	Luo J C, Wu G J, Li J L et al., 2017. Spatial-spectral Recognition of Remote Sensing. Beijing: Sciences Press. (in Chinese)
[19]	Marsetic A, Ostir K, Fras M K , 2015. Automatic orthorectification of high-resolution optical satellite images using vector roads. IEEE Transactions on Geoscience and Remote Sensing, 53(11):6035-6047.
[20]	Nielsen A A , 2007. The regularized iteratively reweighted MAD method for change detection in multi-and hyperspectral data. IEEE Transaction on Image Processing, 16(2):463-478.
[21]	Qian C S , 1956. Draft of Vegetation Regionalization in China. Beijing: Science Press. (in Chinese)
[22]	Wu T J, Luo J C, Xia L G et al., 2014. An automatic sample collection method for object-oriented classification of remotely sensed imageries based on transfer learning. Acta Geodaetica et Cartographica Sinica, 43(9):908-916. (in Chinese)
[23]	Xia L G , 2011. Research on automatic classification of remote sensing images coupled with “graph-spectrum” characteristics [D]. Hangzhou: Zhejiang University of Technology. (in Chinese)
[24]	Yue M , 2015. Vegetation altitudinal belts of Qinling Mountains. Forests and Human Beings, 2:76-81. (in Chinese)
[25]	Zhang J J, Zhu W B, Zhao F et al., 2018. Spatial variations of terrain and their impacts on landscape patterns in the transition zone from mountains to plains: A case study of Qihe River Basin in the Taihang Mountains. Science China Earth Sciences, 2018,48(4):476-486.
[26]	Zhang X D, Zhu W B, Zhang J J et al., 2018. Phenology of forest Qihe River Basin in the Taihang Mountains. Science China Earth Sciences, 48(4):476-486.(in Chinese) vegetation and its response to climate change in the Funiu Mountains. Acta Geographica Sinica, 73(1): 41-53. (in Chinese)

Sensor	Time	Resolution	Sensor	Time	Resolution
GF2	August 3, 2017	0.8 m	ZY3	September 28, 2017	2 m
	September 7, 2017	0.8 m		August 5, 2017	2 m
	September 7, 2017	0.8 m		August 31, 2016	2 m
	August 3, 2017	0.8 m		February 3, 2018	2 m
GF1	August 28, 2016	2 m		February 8, 2018	2 m
	November 30, 2016	2 m		September 28, 2017	2 m
	October 28, 2017	2 m		August 5, 2017	2 m
	February 12, 2017	2 m		August 31, 2016	2 m
	August 28, 2016	2 m		February 3, 2018	2 m
	November 30, 2016	2 m		February 8, 2018	2 m
	October 28, 2017	2 m		July 14, 2017	2 m
	February 12, 2017	2 m		September 22, 2016	2 m
	August 8, 2017	16 m		September 22, 2016	2 m
	January 20, 2015	16 m		November 10, 2016	2 m
	October 1, 2015	16 m		November 4, 2017	2 m
	July 1, 2017	16 m		January 13, 2017	2 m
	February 12, 2017	16 m		July 14, 2017	2 m
ZY3	November 4, 2017	2 m		September 22, 2016	2 m
ZY3	January 13, 2017	2 m		November 10, 2016	2 m