Collaborative multiple change detection methods for monitoring the spatio-temporal dynamics of mangroves in Beibu Gulf, China

Figures & data

Figure 1. The study area: (a) locations of unmanned aerial vehicle (UAV) sampling areas and mangrove reserves; (b) the number of optical images from Landsat 5/7/8, Sentinel-2, and UAV sensors from 1986 to 2021, and the frequency of observations with good quality pixels.

Table 1. Summary of time-series remote sensing images from different platforms.

Sensors	Bands	Resolution(m)	Image count	Dates
Landsat 5 TM	B1, B2, B3, B4, B5, B7	30m	1876	1986–2012
Landsat 7 ETM+	B1, B2, B3, B4, B5, B7	30m	1301	1999–2021
Landsat 8 OLI	B2, B3, B4, B5, B7	30m	700	2014–2021
Sentinel-2 MSI 2A	B2, B3, B4, B8, B11, B12	10m	3511	2018–2021
SRTM	DEM	30m	-	2000
UAV	RGB/Blue, Green, Red, Red- edge, NIR	0.05m	336694	2020–2021

Figure 2. The workflow of this study.

Figure 3. The DMP framework for detecting the dynamics of mangroves: LT, LandTrendr; BF, BFAST; BFM, BFAST Monitor.

Figure 4. Distribution of sample points in four typical regions of the study areas: (a) Zhenzhu Bay; (b) Beihai Bay; and (c) Tieshan Port; (d) Maowei Sea.

Figure 5. The potential mangrove area obtained by the threshold segmentation method in comparison with Jia et al. and Yang et al. classifications from 1990 to 2020. (a) Changes in mangrove area in ten typical regions: I-i, mangrove area in the i-th year based on classification products; II-i, potential mangrove area in the i-th year based on the method; A, Zhenzhu Bay; B, Fangcheng Bay; C, Maowei Sea; D, Dafeng River; E, Beihai Bay; F, Tieshan Port; G, Dandou Sea; H, Yingluo Bay; I, Anpu Port; J, Danzhou. (b) Changes in mangrove area in Beibu Gulf.

Table 2. The Overall accuracy and kappa coefficient of extracting MGRs in ten typical regions of Beibu Gulf from 1986 to 2021.

Regions	Overall accuracy	Kappa	Regions	Overall accuracy	Kappa
Zhenzhu Bay	0.953	0.896	Tieshan Port	0.871	0.734
Fangcheng Bay	0.927	0.848	Dandou Sea	0.897	0.774
Maowei Sea	0.906	0.811	Yingluo Bay	0.859	0.659
Dafeng River	0.909	0.813	Anpu Port	0.881	0.752
Beihai Bay	0.857	0.708	Danzhou	0.865	0.730
Beibu Gulf	0.887	0.776

Figure 6. The change detection results of twenty-one spectral indices based on the LandTrendr algorithm in the major mangrove area of Golden Bay Mangrove Reserves of Beihai Bay.

Figure 7. The detection change results of mangroves using ten spectral indices. (a) Frequency of expansion years. The reference is the frequency of expansion years based on visual interpretation. (b) Deviation distribution of the expansion years detected by the six spectral indices (normalized difference vegetation index, enhanced vegetation index, tasseled cap angle, soil-adjusted vegetation index, combined mangrove recognition index, and mangrove vegetation index) and visual interpretation.

Figure 8. The frequency percentage of expansion events each year was calculated based on the LandTrendr algorithm and MK_m with NDVI and TCA time series datasets: (a) Beihai Sea, (b) Maowei Sea, (c) Zhenzhu Bay, and (d) Tieshan Port.

Figure 9. The inter-annual gain area from 1988–2020 (a); annual expansion (AE) (b); and the expansion index (UER_i) (c) for all typical regions from 1988 to 2020.

Table 3. Annual expansion (AE) of mangroves for ten typical regions during 1988–2020 (ha/year).

Regions	1988–1990	1991–1995	1996–2000	2001–2005	2006–2010	2011–2015	2016–2020	1988–2020
Zhenzhu Bay	5.663	8.485	15.747	8.018	5.276	20.339	16.610	11.799
Fangcheng Bay	6.713	15.860	11.904	4.280	3.172	24.024	34.581	14.826
Maowei Sea	7.464	15.810	15.929	20.728	36.772	136.573	85.242	47.808
Dafeng River	5.668	13.620	18.262	16.517	20.943	34.838	37.243	21.943
Beihai Bay	12.418	12.724	19.366	18.988	33.701	66.869	63.888	33.786
Tieshan Port	4.010	8.318	19.031	25.750	8.491	19.882	16.096	15.148
Dandou Sea	9.123	12.478	9.705	10.722	5.186	16.540	8.625	10.413
Yingluo Bay	13.686	14.118	8.121	8.553	4.502	25.739	7.545	11.635
Anpu Port	7.174	11.310	11.742	20.818	15.871	34.145	7.924	16.078
Danzhou	10.172	10.029	5.977	9.344	17.716	35.359	20.793	15.958
Beibu Gulf	96.330	139.480	155.509	170.988	191.951	499.893	367.202	239.822

Table 4. Evaluating the intensity of change in mangroves for all typical regions from 1988 to 2020 using the expansion index (UER_i).

Regions	1988–1990	1991–1995	1996–2000	2001–2005	2006–2010	2011–2015	2016–2020	1988–2020
Zhenzhu Bay	0.008	0.011	0.020	0.009	0.006	0.022	0.016	0.020
Fangcheng Bay	0.023	0.051	0.030	0.010	0.007	0.049	0.057	0.117
Maowei Sea	0.011	0.022	0.020	0.024	0.037	0.117	0.046	0.100
Dafeng River	0.007	0.017	0.021	0.017	0.020	0.030	0.028	0.032
Beihai Bay	0.011	0.011	0.016	0.014	0.024	0.042	0.033	0.035
Tieshan Port	0.011	0.022	0.046	0.050	0.013	0.029	0.021	0.085
Dandou Sea	0.012	0.016	0.011	0.012	0.005	0.017	0.008	0.017
Yingluo Bay	0.018	0.018	0.009	0.009	0.005	0.027	0.007	0.019
Anpu Port	0.013	0.020	0.019	0.031	0.020	0.040	0.008	0.037
Danzhou	0.007	0.006	0.004	0.006	0.010	0.020	0.011	0.012
Beibu Gulf	0.010	0.014	0.014	0.015	0.016	0.037	0.023	0.031

Figure 10. Spatio-temporal characteristics of mangrove forests in Beibu Gulf: (a) Beibu Gulf; (b) Zhenzhu Bay; (c) Maowei Sea; (d) Tieshan Port; and (e) Beihai Sea.

Figure 11. Historical change process of mangroves detected based on MK_m, LandTrendr, BFAST, and BFAST Monitor algorithms: (a) the expansion years of mangroves gained by the LandTrendr algorithm in Anpu Port; (b) information for the year corresponding to the intersections of UF_k and UB_k; (A1) region with deviated expansion years obtained by LandTrendr algorithm; (A2) region after correction of the expansion year by BFAST algorithm. PI, point of intersection; LT, LandTrendr; BF, BFAST; BFM, BFAST Monitor; BFM, BFAST Monitor; AF, algorithm fitting; SI, significant improvement; CL, confidence level; PI, point of intersection.

Figure 12. Monitoring results of the structural changes in the mangroves in regions B and C (Figure 11(a)) where the expansion years were earlier and there were longer SPH.

Figure 13. Predicted results using MK_s, Sen, Hurst, and 2013–2021 time series data in each typical region: (a) Beibu Gulf; (b) Zhenzhu Bay; (c) Fangcheng Bay; (d) Yingluo Bay; (e) Anpu Port; (f) Tieshan Port; (g) Dandou Sea; (h) Danzhou Port; (i) Maowei Sea; (j) Dafeng River; and (k) Beihai Bay.

Table 5. Classification of spatio-temporal trends in mangrove areas.

H	|Z|	Slope	Trends	TypeID
>0.5	>1.96	>0.0005	Sustainability and significant improvement	Trends-1
>0.5	<1.96	>0.0005	Sustainability and slight improvement	Trends-2
>0.5	>1.96	<-0.0005	Sustainability and significant degradation	Trends-3
>0.5	<1.96	<-0.0005	Sustainability and slight degradation	Trends-4
		−0.0005–0.0005	stability	Trends-5
<0.5	>1.96	>0.0005	Anti-sustainability and significant improvement	Trends-6
<0.5	<1.96	>0.0005	Anti-sustainability and slight improvement	Trends-7
<0.5	>1.96	<-0.0005	Anti-sustainability and significant degradation	Trends-8
<0.5	<1.96	<-0.0006	Anti-sustainability and slight degradation	Trends-9

Table 6. Summary of area percentages of each trend type in ten typical regions, and Beibu Gulf overall.

Regions	Trends-1	Trends-2	Trends-3	Trends-4	Trends-5	Trends-6	Trends-7	Trends-8	Trends-9
Zhenzhu Bay	47.245%	36.161%	0.411%	2.119%	0.765%	1.692%	9.401%	0.056%	1.603%
Fangcheng Bay	47.912%	35.243%	0.978%	4.614%	1.266%	0.771%	6.800%	0.012%	0.782%
Maowei Sea	75.349%	15.291%	0.126%	1.631%	0.395%	0.782%	5.268%	0.016%	0.798%
Dafeng River	63.482%	23.626%	0.307%	1.512%	0.461%	1.483%	7.645%	0.030%	1.046%
Beihai Bay	59.340%	20.210%	1.199%	5.164%	1.124%	1.000%	6.547%	0.227%	4.176%
Tieshan Port	31.355%	42.618%	1.933%	5.318%	1.693%	1.170%	9.842%	0.219%	4.221%
Dandou Sea	33.336%	29.299%	3.452%	8.009%	2.778%	1.779%	14.629%	0.187%	5.337%
Yingluo Bay	27.276%	28.372%	1.651%	10.209%	5.335%	1.326%	14.527%	0.365%	9.637%
Anpu Port	29.299%	31.346%	1.353%	12.907%	3.679%	0.990%	10.573%	0.304%	7.722%
Danzhou	25.372%	26.307%	2.111%	10.635%	3.273%	1.683%	19.556%	0.235%	9.127%
Beibu Gulf	46.023%	26.755%	1.238%	6.226%	1.850%	1.381%	10.806%	0.160%	4.449%

Figure 14. The slope, significance, and sustainability of time series in regions B2, C2, D1, E2, G1, and H1 (Figure 13) from 2013 to 2021. Y1, sustainability; Y2, significance.

Figure 15. Time series of the NDVI, EVI, and NDMI reconstructed based on maximum/mean/median spectral index composition methods and their results using the LandTrendr algorithm. P1, P2, and P3 are located in mangroves that expanded during different periods, and P4 is located in mudflats. A/B/C, maximum/mean/median spectral index composition method.

Figure 16. Predictions of future trends in mangroves at D1 and G1 (Figure 13) from 1986 to 2021, 2000 to 2021, and 2013 to 2021. Y1, sustainability; Y2, significance.

Table A1. Calculation formula for the spectral index in the study.

Spectral Index Name	Formula	Correction constant
Near-Infrared (NIR)	-	0
Ratio Vegetation Index (RVI)	$RVI=NIR/R$	0–1
Normalized Difference Vegetation Index (NDVI)	$NDVI=(NIR-R)/(NIR+R)$	0
Soil-Adjusted Vegetation Index (SAVI)	$SAVI=1.5\times (NIR-R)/(NIR+R+0.5)$	0
Enhanced Vegetation Index (EVI)	$EVI=2.5\times (NIR-RED)/(NIR+6\times RED-7.5\times BLUE+1)$	0
Water-Adjusted Vegetation Index (WAVI)	$WAVI=1.5\times (NIR-BLUE)/(NIR+BLUE+0.5)$	0
Rice Growth Vegetation Index (RGVI)	$RGVI=1-(B+R)/(NIR+SWIR1+SWIR2)$	0
Wetland Forest Index (WFI)	$WFI=(NIR-R)/SWIR2$	0–1.5
Tasseled Cap Greenness Index (TCG)	$\begin{array}{rl}& TCG=-0.1603\times BLUE-0.2819\times GREEN-0.4934\times RED\\ & +0.7940\times NIR-0.0002\times SWIR1-0.1446\times SWIR2\end{array}$	0–0.015
Tasseled Cap Brightness Index (TCB)	$\begin{array}{rl}& TCB=0.2043\times BLUE+0.4158\times GREEN+0.5524\times RED\\ & +0.5741\times NIR+0.3124\times SWIR1+0.2303\times SWIR2\end{array}$	-
Tasseled Cap Wetness Index (TCW)	$\begin{array}{rl}& TCW=0.0315\times BLUE+0.2021\times GREEN+0.3102\times RED\\ & +0.1594\times NIR-0.6806\times SWIR1-0.6109\times SWIR2\end{array}$	-
Tasseled Cap Angle (TCA)	$TCA=arctan(TCG/TCB)\times 180/\mathrm{P}\mathrm{I}$	0–5
Mangrove Index (MI)	$MI=10000\ast (NIR-SWIR\times SWIR/NIR)$	0
Combined Mangrove Recognition Index (CMRI)	$CMRI=NDVI-NDWI$	0
Modular Mangrove Recognition Index (MMRI)	$MMRI=(\left|MNDWI\right|-\left|NDVI\right|)/(\left|MNDWI\right|+\left|NDVI\right|)$	–
Mangrove Vegetation Index (MVI)	$MVI=(NIR-GREEN)/(SWIR1-GREEN)$	-*
Enhanced Mangrove Vegetation Index (EMVI)	$EMVI=(GREEN-SWIR2)/(SWIR1-GREEN)$	-*
Mangrove Discrimination Index 1 (MDI1)	$MDI1=(NIR-SWIR1)/SWIR1$	–
Mangrove Discrimination Index 2 (MDI2)	$MDI2=(NIR-SWIR2)/SWIR2$	0
Mangrove Recognition Index (MRI)	$MRI=(\left|TC{G}_L-TC{G}_H\right|)\times TC{G}_L\times (\left|TC{W}_L+TC{W}_H\right|)$	–
Submerged Mangrove Recognition Index (SMRI)	$SMRI=(\left|NDV{I}_L-NDV{I}_H\right|)\times (\left|NI{R}_L+NI{R}_H\right|)/NI{R}_H$	–
Normalized Difference Mangrove Index 1 (NDMI1)	$NDM{I}^1=(SWIR2-GREEN)/(SWIR2+GREEN)$	–
Normalized Difference Moisture Index (NDMI)	$NDMI=(NIR-SWIR1)/(NIR+SWIR1)$	–
Normalized Difference Water Index (NDWI)	$NDWI=(GREEN-NIR)/(GREEN+NIR)$	-
Modified Normalized Difference Water Index (MNDWI)	$NDWI=(GREEN-SWIR1)/(GREEN+SWIR1)$	-

Index_L and Index_H represent the observations obtained at low and high tide, respectively.

- represents spectral indices not used for detecting the spatio-temporal changes in mangroves.

- indicates that the spectral index cannot effectively detect the spatio-temporal changes in mangroves, and the correction constant cannot be confirmed.

-* indicates that there are abnormally high observations in the time series of the spectral index, and the correction constant cannot be confirmed.

Table A2. Summary of the DMP framework and its monitoring change information.

DMP	Algorithms	Change information	Functions
Detecting historical changes	LandTrendr	Expansion year	Monitoring the spatial expansion of mangroves, and tracking its change years
	MKm*	Mutation, obvious trend changes	Analyzing the spatial distribution of mutations and mangrove development trends (UFk>1.96)
	BFAST Monitor	Change year	Validating the year of mangrove expansion
	BFAST**	Expansion year, duration, magnitude	Correcting the bias of the year of mangrove expansion from the LandTrendr algorithm and providing intra-annual monitoring periods for fitting a model based on BFAST Monitor, and determining the duration and magnitude of expansion events
Monitoring near-real-time changes	BFAST Monitor	Change year, magnitude	Monitoring near-real-time and minor changes in mangroves
Predicting future changes	MKs	Significance	Determining significant changes and spatial distribution of mangroves
	Sen	Slope (Trend)	Analyzing the trends in mangrove growth and decline
	Hurst	Sustainability	Evaluation of the sustainability of mangrove changes

* Only the regions where the UB_k and UF_k curve of MK_m had a unique intersection were retained, with significance levels, α, of 0.01 and 0.05.

**The BFAST algorithm required enough data; therefore, this study supplemented the data of the intra-annual time series by linear interpolation.

Table A3. Parameter values for the LandTrendr algorithm applied in this study.

Parameters	Values
maxSegments	6
spikeThreshold*	0.25
vertexCountOvershoot	6
preventOneYearRecovery	true
recoveryThreshold	0.25
pvalThreshold	0.1
bestModelProportion	0.75
minObservationsNeeded	6

* Due to the possible effect of tides on the time series, we restrained the effect of peaks on the fitted model by setting a lower threshold of spikeThreshold.

Table A4. The Overall accuracy extracting the potential mangrove of ten typical regions of Beibu Gulf in 1991,1996,2001,2006,2011,2016 and 2021.

	1991	1996	2001	2006	2011	2016	2021
Zhenzhu Bay	0.931	0.883	0.924	0.893	0.887	0.886	0.919
Fangcheng Bay	0.700	0.975	0.871	0.913	0.933	0.919	0.957
Maowei Sea	0.791	0.898	0.845	0.888	0.897	0.870	0.902
Dafeng River	0.880	0.981	0.926	0.850	0.891	0.852	0.913
Beihai Bay	0.813	0.806	0.814	0.818	0.813	0.837	0.868
Tieshan Port	0.833	0.744	0.900	0.884	0.909	0.919	0.901
Dandou Sea	1.000	0.948	0.942	0.930	0.901	0.920	0.905
Yingluo Bay	0.778	0.928	0.888	0.897	0.855	0.908	0.882
Anpu Port	0.857	0.852	0.903	0.916	0.874	0.858	0.904
Danzhou	0.877	0.869	0.897	0.917	0.906	0.832	0.939
Beibu Gulf	0.868	0.886	0.892	0.886	0.884	0.868	0.901

Data availability statement

The mangrove products that support the findings of this study are openly available from National Earth System Science Data Center (http://www.geodata.cn) and Science Data Bank (https://www.scidb.cn).