Developing remote sensing based methods for land cover change detection in national parks from GEE platform: a case study from the Qianjiangyuan National Park pilot area

doi:10.12302/j.issn.1000-2006.202101013

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (2): 213-220.doi: 10.12302/j.issn.1000-2006.202101013

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Developing remote sensing based methods for land cover change detection in national parks from GEE platform: a case study from the Qianjiangyuan National Park pilot area

MAO Lijun¹^,²(), LI Haitao³, XUE Xiaoming², LI Jianwei⁴^,^*(), LI Mingshi¹^,⁵

1. College of Forestry, Nanjing Forestry University, Nanjing 210037, China
2. College of Criminal Science and Technology, Nanjing Forest Police College; Key Laboratory of State Forest and Grassland Administration on Wildlife Evidence Technology, Nanjing 210023, China
3. Linyi City Management Comprehensive Service Center, Linyi 276000, China
4. Dali Branch of Yunnan Forestry Survey and Planning Institute, Dali 671000, China
5. College of Forestry, Co-Innovation Center for the Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

Received:2021-01-08 Accepted:2021-04-18 Online:2022-03-30 Published:2022-04-08
Contact: LI Jianwei E-mail:111207@nfpc.edu.cn;dalijw@163.com

Abstract

Abstract:

【Objective】Efficient and reliable extraction of land cover change information plays an important role in formulating management and protection plans for national parks. The primary objective of the current work was to develop an efficient and economical remote sensing detection method to extract land cover change information in the Qianjiangyuan National Park pilot area from 2001 to 2017, and to assess the effectiveness of the adopted protection and management measures. 【Method】A land cover classification scheme, including cropland, forest, grassland, water, artificial surface, and bare land, was first defined, followed by the selection of the historical training dataset using an enhanced visual interpretation method. Next, the original spectral bands, spectral indices, and textural features derived from Landsat multi-season composite data and terrain features were incorporated as input variables for implementing the random forest classification algorithm in the Google Earth Engine (GEE)cloud platform to generate land cover datasets. Finally, the major land cover conversion types and their spatial distributions were mapped by establishing land cover conversion rules. 【Result】The validation results derived from an independent sample set indicated that the overall accuracies for the 2001, 2009 and 2017 classifications were 83.12%, 81.82% and 87.35%, respectively. Afforestation activities, cropland abandonment, and distributions of development and construction projects demonstrated the effectiveness of the protection and management measures adopted in this park. 【Conclusion】The enhanced visual interpretation process helps to identify historical sample points efficiently and reliably to support the creation of historical land cover datasets. With cloud computing-based change detection technology, it is possible to quickly assess the protection and management efficacy and locate ecologically vulnerable zones. Moreover, the method has the advantages of high efficiency, economy, and fewer limitations on computing resources; thus, the GEE-based land cover change detection method proposed in the current work is suitable for an application in other similar scenarios.

Key words: Qianjiangyuan National Park, land cover change, remote sensing monitoring, Google Earth Engine(GEE), enhanced visual interpretation

CLC Number:

TP79

MAO Lijun, LI Haitao, XUE Xiaoming, LI Jianwei, LI Mingshi. Developing remote sensing based methods for land cover change detection in national parks from GEE platform: a case study from the Qianjiangyuan National Park pilot area[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(2): 213-220.

Figures/Tables 8

Fig.1

Table 1

Table 2

The spectral indexes used in the study"

光谱指数 spectral index	计算公式 calculation formula	参考文献 reference
NDVI	I_NDVI= $ρ N I R - ρ R e d ρ N I R + ρ R e d$	[23]
EVI	I_EVI=2.5× $ρ N I R - ρ R e d ρ N I R + 6 × ρ R e d - 7.5 × ρ B l u e + 1$	[24]
GNDVI	I_GNDVI= $ρ N I R - ρ G r e e n ρ N I R + ρ G r e e n$	[25]
GLI	I_GLI= $ρ G r e e n - ρ R e d + (ρ G r e e n - ρ B l u e) 2 × ρ G r e e n + ρ R e d + ρ B l u e$	[26]
VARI	I_VARI= $ρ G r e e n - ρ R e d ρ G r e e n + ρ R e d - ρ B l u e$	[27]
GARI	I_GARI= $ρ N I R - [ρ G r e e n - 1.7 × ρ B l u e - ρ R e d] ρ N I R + [ρ G r e e n - 1.7 × ρ B l u e - ρ R e d]$	[28]
MSAVI	I_MSAVI= $2 × ρ N I R + 1 - (2 × ρ N I R + 1) 2 - 8 × (ρ N I R - ρ R e d) 2$	[29]
MNDWI	I_MNDWI= $ρ G r e e n - ρ S W I R ρ G r e e n + ρ S W I R$	[30]

Table 2

Table 3

Table 4

Fig.2

Fig.3

Table 5

References 35

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年份 year	传感器 sensor	可用图像数量 number of available images
年份 year	传感器 sensor	春季 spring	秋季 autumn	总数 total
2001	Landsat-7 ETM+	4	6	10
2009	Landsat-5 TM	4	4	8
2017	Landsat-8 OLI	5	8	13

类型 type	2001年		2009年		2017年
类型 type	用户精度/% user accuracy	生产者精度/% producer accuracy	用户精度/% user accuracy	生产者精度/% producer accuracy	用户精度/% user accuracy	生产者精度/% producer accuracy
耕地cropland	76.27	94.74	80.00	76.71	77.08	88.10
森林forest	93.38	94.78	90.60	96.43	93.10	96.43
草地grassland	73.33	70.00	74.41	77.55	86.27	91.67
水体water	89.47	78.00	95.24	76.67	89.66	86.47
人造地表artificial surface	70.00	68.00	71.05	75.00	76.32	70.73
裸地bare land	50.00	50.00	75.00	63.08	92.31	73.16
总体精度/% overall accuracy	83.12		81.82		87.35

年份 year	类别 type	古田山国家级自然保护区 Gutianshan National Nature Reserve		钱江源省级风景名胜区 Qianjiangyuan Provincial Scenic Spot		连接地带 connection area		钱江源国家公园 Qianjiangyuan National Park
年份 year	类别 type	面积/hm² area	比例/% proportion	面积/hm² area	比例/% proportion	面积/hm² area	比例/% proportion	面积/hm² area	比例/% proportion
2001—2009	造林活动	609.69	31.42	306.37	15.79	1 024.49	52.79	1 940.55	100.00
2009—2017	造林活动	238.05	22.40	124.61	11.73	699.83	65.87	1 062.49	100.00
2001—2009	耕地废弃	477.84	26.54	273.58	15.20	1 048.95	58.26	1 800.36	100.00
2009—2017	耕地废弃	122.12	21.89	96.78	17.35	339.06	60.77	557.96	100.00

Developing remote sensing based methods for land cover change detection in national parks from GEE platform: a case study from the Qianjiangyuan National Park pilot area

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年份 year	耕地 cropland	森林 forest	草地 grassland	水体 water	人造地表 artificial surface	裸地 bare land
2001	250	338	54	74	88	26
2009	166	339	124	71	92	38
2017	119	333	151	80	96	51