Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks

Figures & data

Figure 1. Map of the study area at Vinh Quang coast, Tien Lang district, Hai Phong city. For full color versions of the figures in this paper, please see the online version.

Table 1. Acquired satellite remote-sensing data.

Satellite sensor	Date of acquisition	Level of process	Pixel spacing	Polarizations
ALOS-2 PALSAR	2015/7/31	2.1	6.25 m	L band (HH, HV)

Source: JAXA, Japan.

Figure 2. Flowchart used to identify S. caseolaris using ALOS-2 PALSAR imagery.

Figure 3. Measurement biophysical parameters of S. caseolaris and their roots.

Figure 4. Structure of the MLPNN model for estimating the AGB in this study.

Table 2. Performance of MLPNN with different number of neurons in the hidden layer.

Number of neurons in hidden layer	R^2	RMSE	MAE
1	0.510	0.403	0.296
2	0.029	0.685	0.526
3	0.177	0.694	0.514
4	0.357	0.591	0.448
5	0.776	0.299	0.237
6	0.480	0.573	0.388
7	0.274	0.766	0.441
8	0.286	0.746	0.44
9	0.325	0.700	0.418
10	0.196	0.839	0.479
11	0.238	0.714	0.431
12	0.194	0.855	0.525
13	0.151	0.872	0.513
14	0.303	0.764	0.403
15	0.174	0.890	0.509

Table 3. Characteristics of S. caseolaris in the study site.

Plot ID	Tree density (No./900 m^2)	DBH (cm)	Age (years)	Tree height (meter)	AGB (kg/900 m^2)	Root weight (kg/900 m^2)	BGB (kg/900 m^2)	AGB (Mg ha^−1)	BGB (Mg ha^−1)
1	57	10.67	17	5.71	542.91	7.709	823.444	6.887	9.149
2	45	13.22	17	6.42	699.36	9.309	1045.777	8.496	11.620
3	72	11.98	10	6.06	890.00	8.787	1345.252	11.049	14.947
4	83	11.49	10	6.08	956.30	6.992	1414.689	11.686	15.719
5	110	11.81	10	4.85	1085.77	5.093	1992.168	13.347	22.135
6	98	12.06	10	5.33	1094.05	6.663	1859.210	13.517	20.658
7	42	18.79	17	5.67	1099.30	7.655	2131.574	12.844	23.684
8	48	23.07	17	6.56	2077.34	11.480	3843.674	24.015	42.707
9	65	21.18	17	6.67	2442.24	11.006	4302.659	28.328	47.807
10	84	19.02	17	10.30	3846.09	15.965	4378.573	44.181	48.651
11	54	31.58	17	6.33	3976.38	12.977	8679.311	45.412	96.437
12	56	28.84	23	12.40	6409.29	26.660	7356.972	72.552	81.744
13	126	20.42	23	11.00	6955.34	18.150	7689.924	79.592	85.444
14	105	26.11	17	8.64	7264.79	22.896	11066.636	83.812	122.963
15	115	26.25	17	8.17	7632.43	18.383	12261.683	87.680	136.241
16	63	29.25	22	13.40	7928.94	31.490	8538.093	89.744	94.868
17	117	21.82	22	12.50	8162.52	28.125	8271.390	93.620	91.904
18	89	43.75	23	8.00	14535.88	8.800	29492.032	162.598	327.689
Average	79	21.18	17	8.10	4311.052	14.341	6471.837	49.409	71.909

Table 4. Pearson’s correlation matrix of various biophysical parameters of S. caseolaris (N = 18).

	Tree density	DBH	Height	Age	Tree weight	Root weight	Total AGB
Tree density	1
DBH	0.016	1
Height	0.244	0.459	1
Age	−0.007	0.703**	0.760**	1
Tree weight	0.421	0.644**	0.644**	0.731**	1
Root weight	0.204	0.454	0.929**	0.673**	0.580*	1
Total AGB	0.421	0.846**	0.747**	0.734**	0.933**	0.726**	1

Note: **Correlation is significant at the 0.01 level, *correlation is significant at the 0.05 level.

Table 5. Person’s correlation matrix between backscattering coefficient σ° and various biophysical parameters of S. caseolaris (N = 18).

	HH	HV	Tree density	DBH	Height	Age	Tree weight	Root weight	Total AGB
HH	1
HV	0.878	1
Tree density	0.291	0.341	1
DBH	0.392	0.401	−0.126	1
Height	0.368	0.559*	0.181	0.527*	1
Age	0.481	0.548*	0.028	0.707**	0.798**	1
Tree weight	0.412	0.497	0.276	0.836**	0.693**	0.776**	1
Root weight	0.347	0.432	0.849**	0.101	0.455	0.267	0.469	1
Total AGB	0.334	0.537*	0.431	0.846**	0.747**	0.734**	0.933**	0.726**	1

Note: **Correlation is significant at the 0.01 level, *correlation is significant at the 0.05 level.

Table 6. Machine learning models with leave-one-out (LOO) cross-validation for estimation of above-ground biomass in this study.

No.	Machine learning model	RMSE	MAE	R^2
1	Multi-layer perceptron neural networks (MLPNN)	0.299	0.237	0.776
2	Support vector regression (SVR)	0.359	0.268	0.596
3	Radial basis function neural networks (RBFNN)	0.388	0.333	0.612
4	Gaussian process (GP)	0.379	0.345	0.483
5	Random forests (RF)	0.402	0.364	0.372

Table 7. Comparison of MLPNN, SVR, RBFNN, GP, and RF models, using AIC and BIC.

	MLPNN	SVR	RBFNN	GP	RF
AIC	−29.473	−22.745	−20.101	−20.510	−18.151
BIC	−23.240	−16.512	−13.868	−14.278	−11.918

Figure 5. Spatial distribution pattern of biomass in the study area.