Monitoring the spatiotemporal dynamics of mangrove forests in Beibu Gulf, Guangxi Zhuang Autonomous Region, China, using Google Earth Engine and time-series active and passive remote sensing images

doi:10.6046/zrzyyg.2023370

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Abstract

Mangrove forests are recognized as one of the most biodiverse and productive marine ecosystems globally. This study investigated Beibu Gulf, Guangxi Province. Using Landsat, Sentinel, and PALSAR SAR images from 1985 to 2019 as data sources, as well as the Google Earth Engine (GEE) cloud platform, this study established a multisource dataset by integrating spectral bands, spectral indices, texture features, digital elevation models (DEMs), and backscatter coefficients. Furthermore, 14 classification schemes were developed, and a mangrove remote sensing recognition model was built using an object-based random forest (RF) algorithm. Accordingly, the long-time-series spatiotemporal dynamics of mangrove forests in Beibu Gulf were monitored. The monitoring results show that the object-based RF algorithm demonstrates a high ability to identify mangrove forests. Specifically, Scheme 3 combined with data from 2019 yielded the highest overall accuracy (96.3%) and a kappa coefficient of 0.956, which are 16.3% and 0.195 higher than those of Scheme 1 combined data from 1995, respectively. The classification schemes differed in the producer’s and user’s accuracy of different surface features in the Beibu Gulf. Specifically, these schemes yielded the highest user’s and producer’s accuracy of mangrove forests exceeding 94.6% and 92.0%, respectively. From 1985 to 2019, the area of mangrove forests in Beibu Gulf showed an increasing trend, with an annual changing rate of 6.63%, and the area expanded from inland to coastal areas. The results of this study provide a reference for the protection and sustainable management of mangrove forests while also verifying the feasibility of monitoring long-term spatiotemporal dynamics of mangrove forests based on the GEE platform.

Keywords Beibu Gulf mangrove forest Google Earth Engine active and passive remote sensing images random forest (RF) algorithm dynamic monitoring

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Issue Date: 09 May 2025

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	Jianming DENG
	Hang YAO
	Bolin FU
	Sen GU
	Jie TANG
	Yuanyuan GAN

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Jianming DENG,Hang YAO,Bolin FU, et al. Monitoring the spatiotemporal dynamics of mangrove forests in Beibu Gulf, Guangxi Zhuang Autonomous Region, China, using Google Earth Engine and time-series active and passive remote sensing images[J]. Remote Sensing for Natural Resources, 2025, 37(2): 235-245.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023370 OR https://www.gtzyyg.com/EN/Y2025/V37/I2/235

Fig.1 The location of study area

Tab.1 Multi-source remote sensing data

Tab.2 Interpretation signs and descriptions of ground objects in the study area

序号	指数类型	计算公式^①
1	归一化植被指数 (normalized difference vegetation index, NDVI)	$N D V I = (λ N I R - λ R E D) / (λ N I R + λ R E D)$
2	比值植被指数(ratio vegetation index, RVI)	$R V I = λ R E D / λ N I R$
3	差值植被指数(difference vegetation index, DVI)	$D V I = λ N I R - λ R E D$
4	增强植被指数(enhanced vegetation index, EVI)	$E V I = 2.5 × λ N I R - λ R E D λ N I R + 6.0 λ R E D - 7.5 λ B L U E + 1$
5	校正植被指数(corrected transformed vegetation index, CTVI)	$C T V I = N D V I + 0.5 \| N D V I + 0.5 \| \| N D V I + 0.5 \|$
6	非线性指数(non-linear vegetation index, NLI)	$N L I = (λ N I R 2 - λ R E D) / (λ N I R 2 + λ R E D)$
7	改进简单比值指数(modified simple ratio, MSR_NIR)	$M S R N I R = (λ N I R λ R E D - 1) / λ N I R λ R E D + 1$
8	归一化水体指数(normalized difference water index, NDWI)	$N D W I = (λ S W I R - λ N I R) / (λ S W I R + λ N I R)$
9	改进的归一化水体指数(modified normalized difference water index, MNDWI)	$N D W I S W I R 1 = (λ S W I R 1 - λ N I R) / (λ S W I R 1 + λ N I R)$ SWIR1(1.55~1.75 μm) $N D W I S W I R 2 = (λ S W I R 2 - λ N I R) / (λ S W I R 2 + λ N I R)$ SWIR2(2.08~2.35 μm)
10	自动提取水体指数(automated water extraction index, AWEI)	$A W E I = 4 (λ G R E E N - λ S W I R 1) / (0.25 λ N I R + 0.75 λ S W I R 2)$
11	归一化建筑指数(normalized difference built-up index, NDBI)	$N D B I S W I R 1 = (λ S W I R 1 - λ N I R) / (λ S W I R 1 + λ N I R)$ SWIR1(1.55~1.75 μm) $N D B I S W I R 2 = (λ S W I R 2 - λ N I R) / (λ S W I R 2 + λ N I R)$ SWIR2(2.08~2.35 μm)
12	基于红边波段的改进简单比值指数(modified simple ratio, MSR_RE1,MSR_RE2)	$M S R R E 1 = (λ R E 1 λ R E D - 1) / λ R E 1 λ R E D + 1$ RE1(703.9 nm) $M S R R E 2 = (λ R E 2 λ R E D - 1) / λ R E 2 λ R E D + 1$ RE2(740.2 nm)
13	基于红边波段的非线性指数(non-linear index, NLI_RE1,NLI_RE2)	$N L I R E 1 = (λ R E 1 2 - λ R E D) / (λ R E 1 2 + λ R E D)$ RE1(703.9 nm) $N L I R E 2 = (λ R E 2 2 - λ R E D) / (λ R E 2 2 + λ R E D)$ RE2(740.2 nm)
14	基于红边波段的增强植被指数(enhanced vegetation index, EVI_RE1,EVI_RE2)	$E V I R E 1 = 2.5 × λ R E 1 - λ R E D λ R E 1 + 6.0 λ R E D - 7.5 λ B L U E + 1$ RE1(703.9 nm) $E V I R E 2 = 2.5 × λ R E 2 - λ R E D λ R E 2 + 6.0 λ R E D - 7.5 λ B L U E + 1$ RE2(740.2 nm)
15	微波遥感指数HH/HV	PALSAR HH/HV
16	微波遥感指数VV/VH	Sentinel-1 VV/VH

Tab.3 Calculation formula of each type of remote sensing index

年份	传感器	方案	变量数	多源数据集
1985年	Landsat5 TM	方案1	21	多光谱波段(6个波段)、各类遥感指数(植被指数、水体指数、建筑指数)、数字高程模型(digital elevation model,DEM)、非监督分类结果
1985年	Landsat5 TM	方案2	55	多光谱波段(6个波段)、各类遥感指数(植被指数、水体指数、建筑指数)、DEM、非监督分类结果、B1(BLUE)波段纹理特征、B5(1.55~1.75 μm)波段纹理特征
1995年	Landsat5 TM	方案1	21	多光谱波段(6个波段)、各类遥感指数(植被指数、水体指数、建筑指数)、DEM、非监督分类结果
1995年	Landsat5 TM	方案2	38	多光谱波段(6个波段)、各类遥感指数(植被指数、水体指数、建筑指数)、DEM、非监督分类结果、B5(1.55~1.75 μm)波段纹理特征
2005年	Landsat7 ETM+	方案1	22	多光谱波段(7个波段)、各类遥感指数(13个)、DEM、非监督分类结果
		方案2	25	多光谱波段(7个波段)、各类遥感指数(13个)、DEM、非监督分类结果、PALSAR/PALSAR-2 HH,HV,HH/HV
		方案3	42	多光谱波段(7个波段)、各类指数(13个)、DEM、非监督分类结果、HSV波段融合、B4(NIR)波段纹理特征
2015年	Landsat8 OLI	方案1	55	多光谱波段(7个波段)、各类遥感指数(13个)、DEM、非监督分类结果
		方案2	25	多光谱波段(7个波段)、各类遥感指数(13个)、DEM、非监督分类结果、C-band Sentinel-1 GRD VV,VH,VV/VH
		方案3	42	多光谱波段(7个波段)、各类遥感指数(13个)、DEM、非监督分类结果、HSV波段融合、B7(2.11~2.29 μm)波段纹理特征
2019年	Sentinel-2 MSI	方案1	31	多光谱波段(10个波段)、各类遥感指数(16个)、DEM、非监督分类结果
		方案2	34	多光谱波段(10个波段)、各类遥感指数(16个)、DEM、非监督分类结果、C-band Sentinel-1 GRD VV,VH,VV/VH
		方案3	50	多光谱波段(10个波段)、各类遥感指数(16个)、DEM、非监督分类结果、B5(703.9 nm)波段纹理特征(9个)、B8(NIR)波段纹理特征(10个)
		方案4	48	多光谱波段(10个波段)、各类遥感指数(16个)、DEM、非监督分类结果、B12(2 202.4 nm)波段纹理特征

Tab.4 Classification schemes and multi-source datasets

Fig.2 Technical route of this study

Fig.3 Quantitative evaluation of the importance of different remote sensing data sets from 1985 to 2015

Fig.4 Classification results based on different remote sensing images from 1985 to 2015

Fig.5 Classification results derived from Sentinel 2 MSI image in 2019

Tab.5 The overall classification accuracies and Kappa coefficient of different remote sensing data from 1985 to 2019

Fig.6 Classification results of the same remote sensing data set in 1985 and 2019

Tab.6 The area of land use types from 1985 to 2019 (km²)

Tab.7 Annual change rates of land use types from 1985 to 2019 (%)

Fig.7 Comparison of mangrove distribution in typical area of Beibu Gulf in 1985, 2005 and 2019

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