Estimating mean groundwater levels in peatlands using a Bayesian belief network approach with remote sensing data

Stachowicz, Marta; Banaszuk, Piotr; Ghezelayagh, Pouya; Kamocki, Andrzej; Mirosław-Świątek, Dorota; Grygoruk, Mateusz

Estimating mean groundwater levels in peatlands using a Bayesian belief network approach with remote sensing data

Scientific Review Engineering and Environmental Sciences

Volume 33 (2024): Issue 4 (December 2024)

By: Marta Stachowicz

, Piotr Banaszuk

, Pouya Ghezelayagh

, Andrzej Kamocki

, Dorota Mirosław-Świątek

and Mateusz Grygoruk

Open Access

|Oct 2024

Figures & Tables

Map of the Biebrza National Park with hydrological network and locations of the piezometers
Source: own work. — Map of the Biebrza National Park with hydrological network and locations of the piezometers Source: own work.

Correlation between synthetic aperture radar backscatter coefficient and groundwater level (gray line represents the regression line; gray area represents the confidence interval of 95%; linear model equation: y = −0.08x + −1.66)
Source: own work. — Correlation between synthetic aperture radar backscatter coefficient and groundwater level (gray line represents the regression line; gray area represents the confidence interval of 95%; linear model equation: y = −0.08x + −1.66) Source: own work.

Conceptual model of the Bayesian belief network
Source: own work. — Conceptual model of the Bayesian belief network Source: own work.

Maps showing 100-hectare polygons (A) and the Biebrza National Park plots (B) with a synthetic aperture radar backscatter coefficient raster layer as a background
Source: own work. — Maps showing 100-hectare polygons (A) and the Biebrza National Park plots (B) with a synthetic aperture radar backscatter coefficient raster layer as a background Source: own work.

Example results from the Bayesian belief network: A – percentage contribution of model parameters in Polygon 9, B – percentage contribution of model parameters in Biebrza National Park Plot 26. Groundwater level node represents the results as a probability distribution of the occurrence of certain GWL classes
Source: own work. — Example results from the Bayesian belief network: A – percentage contribution of model parameters in Polygon 9, B – percentage contribution of model parameters in Biebrza National Park Plot 26. Groundwater level node represents the results as a probability distribution of the occurrence of certain GWL classes Source: own work.

Confusion matrices displaying the number of matched and unmatched classes between predicted and actual groundwater level values for 100 ha polygons (A) and the Biebrza National Park plots (B) used as the validation set
Source: own work. — Confusion matrices displaying the number of matched and unmatched classes between predicted and actual groundwater level values for 100 ha polygons (A) and the Biebrza National Park plots (B) used as the validation set Source: own work.

Classification of parameters used in the Bayesian network modela

Parameter	Class	Value
Groundwater level [m]	C1	< −0.4
	C2	−0.4 to −0.3
	C3	−0.3 to −0.2
	C4	−0.2 to −0.1
	C5	−0.1 to 0.0
	C6	> 0.0
SAR backscatter coefficient (σ°) [dB]	SAR1	< −18
	SAR2	−18 to −16
	SAR3	> −16
Peat subsidence rate [m·year⁻¹]	Subs1	−0.05 to −0.02
	Subs2	−0.02 to −0.01
	Subs3	−0.01 to 0.05
Distance to the watercourse [m]	D1	0 to 25
	D2	25 to 100
	D3	100 to 440
	D4	> 440

Spearman’s rank correlation results between model variables

Pair of compared model variables	Spearman’s rank correlation parameters
Pair of compared model variables	ρ	p-value
Synthetic aperture radar backscatter coefficient–subsidence	−0.11	0.47
Synthetic aperture radar backscatter coefficient–distance to the watercourses	0.11	0.45
Subsidence–distance to the watercourses	−0.03	0.82

Piezometers within the Biebrza National Park used in the study

ID	Piezometer/Transect name	Start of measurement	End of measurement	Mean GWT [m]
1	–	2014	2022	0.001
2	–	2015	2022	−0.002
3	–	2014	2021	−0.004
4	–	2014	2021	−0.004
5	–	2015	2021	−0.003
6	–	2015	2021	−0.001
7	–	2015	2021	−0.004
8	–	2014	2021	−0.002
9	–	2015	2021	−0.001
10	–	2015	2021	−0.001
11	–	2017	2021	−0.001
12	–	2015	2021	−0.001
13	–	2011	2018	−0.001
14	Brzeziny Ciszewskie	1998	2022	−0.343
15	Brzeziny Ciszewskie	1998	2022	−0.320
16	Ciszewo	1994	2022	−0.351
17	Ciszewo	1994	2022	−0.267
18	Ciszewo	1994	2022	−0.380
19	Ciszewo	1994	2022	−0.247
20	Ciszewo	1994	2022	−0.314
21	Czerwone Bagno T	2008	2015	−0.062
22	Czerwone Bagno T	2008	2015	−0.039
23	Długa Luka	2009	2022	−0.023
24	Grobla Honczarowska	1998	2022	0.027
25	Grobla Honczarowska	1998	2022	0.089
26	Grobla Honczarowska	1998	2022	−0.010
27	Grzędy I	1996	2022	−0.385
28	Grzędy I	1996	2022	−0.498
29	Grzędy I	1996	2022	−0.363
30	Grzędy I	1996	2022	−0.381
31	Grzędy I	1996	2022	−0.330
32	Grzędy II	1996	2022	−0.514
33	Gugny	2009	2022	−0.123
34	Gugny	2009	2022	−0.095
35	Gugny II	2009	2022	−0.085
36	Gugny II	2009	2022	−0.125
37	Gugny II	2009	2022	−0.012
38	Jałowo	1998	2022	−0.302
39	Jałowo	1998	2022	−0.034
40	Kapice	2012	2021	−0.263
41	Kuligi	1994	2022	−0.314
42	Kuligi	1994	2022	−0.330
43	Kuligi	1994	2022	−0.369
44	Trójkąt I	1996	2022	−0.333
45	Trójkąt I	1996	2022	−0.338
46	Trójkąt I	1996	2022	−0.500
47	Trójkąt II	1996	2022	−0.275
48	Trójkąt II	1996	2022	−0.299
49	Trójkąt II	1996	2022	−0.304

Conditional probabilities of groundwater level classes generated from the Bayesian belief network

V.m.	No	Avg. obs. GWL [m]	GWL class probability [%]						GWL class
V.m.	No	Avg. obs. GWL [m]	C1	C2	C3	C4	C5	C6	Obs.	Pred.
Polygon	1	−0.364	10	28.5	10.6	6.95	37.8	6.05	C2	C5
	2	−0.293	5.96	22.5	52	5.83	7.85	5.82	C3	C3
	3	−0.391	20.8	50.8	7.07	5.87	9.66	5.79	C2	C2
	4	−0.314	7.38	39.1	14	6.76	27	5.73	C2	C2
	5	−0.309	10.6	34.4	20.8	6.52	21.4	6.19	C2	C2
	6	−0.332	16.9	35.4	9.91	7.63	23.4	6.77	C2	C2
	7	−0.001	6.94	8.55	7.25	6.94	59.7	10.6	C5	C5
	8	−0.006	8.67	12.6	9.17	7.98	48.8	12.8	C5	C5
	9	−0.004	6.02	10.3	5.52	15	58.2	4.99	C5	C5
	10	−0.002	7.03	29.1	10.5	8.04	38.8	6.51	C5	C5
	11	−0.109	6.17	13.5	8.09	7.99	59.4	4.89	C4	C5
	12	−0.012	5.74	7.16	5.88	8.61	65.6	6.98	C5	C5
BbPN plot	1	−0.002	6.68	21	8.24	8.87	49.5	5.71	C5	C5
	2	−0.338	7.9	50.4	9.52	6.23	19.9	6.05	C2	C2
	3	−0.500	14.2	34.2	16.6	9.9	14.7	10.4	C1	C2
	4	−0.338	5.26	67.8	10.4	5.26	6.08	5.26	C2	C2
	5	−0.351	20.6	56.2	4.64	4.64	9.24	4.64	C2	C2
	6	−0.267	4.57	52.3	18.6	4.57	15.4	4.57	C3	C2
	7	0.001	7.75	7.75	7.75	7.75	55.2	14.2	C6	C5
	8	−0.002	6.06	6.06	6.06	6.06	67.8	7.93	C5	C5
	9	−0.304	6.49	30.2	32.1	6.44	18.4	6.4	C2	C3
	10	−0.001	10.2	38.7	10.5	7.78	25.3	7.45	C5	C2
	11	−0.003	5.72	15.6	8.9	7.11	57	5.72	C5	C5
	12	−0.287	6.65	16.9	52.8	6.65	10.4	6.65	C3	C3
	13	−0.314	9.32	49.5	20.8	6.24	7.95	6.14	C2	C2
	14	−0.514	19.6	44.7	9.63	6.48	13.4	6.13	C1	C2
	15	−0.247	6.51	56	18.9	6.03	6.52	6.02	C3	C2
	16	−0.333	8.04	35.7	26.6	5.77	18.4	5.45	C2	C2
	17	−0.372	21.7	52.8	6.65	5.35	8.17	5.3	C2	C2
	18	−0.062	9.27	24.3	12.8	7.14	39	7.47	C5	C5
	19	−0.005	6.14	11.8	6.24	7.86	61.6	6.37	C5	C5
	20	−0.023	6.51	6.51	6.51	6.51	66.4	7.54	C5	C5
	21	−0.001	5.81	8.9	5.78	10.9	63	5.61	C5	C5
	22	−0.001	3.93	3.93	3.93	3.93	74.6	3.93	C5	C5
	23	−0.001	8.24	10.6	9.72	8.08	47.8	15.6	C5	C5
	24	−0.001	11.8	17.5	11.7	9.62	36.4	12.9	C5	C5
	25	−0.001	12.1	22	11.5	9.08	33.5	11.9	C5	C5
	26	−0.320	23.9	50.3	11.7	4.71	4.71	4.71	C2	C2

Area percentage contributions of each model variables class in polygons and plots used for validation

V.m.	No	Area [ha]	Avg. obs. GWL [m]	SAR backscatter coefficient class distribution [%]			Subsidence class distribution [%]			Distance to the watercourse class distribution [%]
V.m.	No	Area [ha]	Avg. obs. GWL [m]	1	2	3	1	2	3	1	2	3	4
Polygon	1	100	−0.364	48.3	21.9	29.8	21.7	60	18.3	0	4	53.4	42.6
	2	100	−0.293	3.5	26.7	69.8	0.6	31.4	68	0	0	0	100
	3	100	−0.391	6.6	11.9	81.5	64.3	34.6	1.1	3.6	10.4	71.6	14.4
	4	100	−0.314	30.1	41.0	28.9	17.1	76	6.9	0	0	0	100
	5	100	−0.309	23.2	22.8	54.0	11.1	57.2	31.7	0	0.7	35.3	64
	6	100	−0.332	29.6	4.4	66.0	52.9	44.8	2.3	5.8	13.5	40.4	40.3
	7	100	−0.001	96.0	4.0	0.0	24.3	65.6	10.1	10.6	23.4	66	0
	8	100	−0.006	79.9	10.3	9.8	11	55.9	33.1	15.5	41.2	43.3	0
	9	100	−0.004	81.5	13.6	4.9	63.3	35.6	1.1	0	0	3	97
	10	100	−0.002	48.6	46,0	5.4	22.6	60.5	16.9	0.3	1.7	25.9	72.1
	11	100	−0.109	79.8	6.6	13.6	22.6	65.2	12.2	0	0	14.1	85.9
	12	100	−0.012	96.7	3.3	0.0	34.6	61.5	3.9	4	10.8	42.4	42.8
BbPN plot	1	5.72	−0.002	64.6	27.4	8	30	60	10	0	0	18.5	81.5
	2	4.75	−0.338	20.3	65.8	13.9	10	80	10	0	1.3	53.9	44.8
	3	1.41	−0.5	10.7	44.3	45	33.3	66.7	0	13.6	45.5	40.9	0
	4	0.92	−0.338	1.1	78.4	20.5	0	100	0	0	0	0	100
	5	1.74	−0.351	6.9	24.7	68.4	33.3	66.7	0	0	0	100	0
	6	1.73	−0.267	14.4	29.3	56.3	0	100	0	0	0	0	100
	7	7.21	0.001	100	0	0	33.3	41.7	25	22.5	25	52.5	0
	8	1.3	−0.002	100	0	0	0	33.3	66.7	4.8	19	76.2	0
	9	53.24	−0.304	19.3	56.1	24.6	1.1	47.8	51.1	0	0	0	100
	10	38.63	−0.001	29.2	50.7	20.1	32.8	61.2	6	3.3	10.2	52.6	33.9
	11	18.46	−0.003	76.7	23.3	0	9.1	63.6	27.3	0	0	0	100
	12	53.57	−0.287	6.8	47.8	45.4	0	18.7	81.3	0	0	0	100
	13	50.47	−0.314	2.6	46.9	50.5	18.9	71.1	10	0	0	0	100
	14	65.53	−0.514	12.3	9.9	77.8	53.8	43.4	2.8	3.6	9.8	52.8	33.8
	15	19.53	−0.247	0.7	73.6	25.7	5.7	80	14.3	0	0	0	100
	16	50.94	−0.333	19.4	20.3	60.3	9	64	27	0	0	8.8	91.2
	17	39.29	−0.372	4.7	10	85.3	57.1	42.9	0	2.1	8.9	76.1	12.9
	18	61.39	−0.062	50.6	22.4	27	4.6	55	40.4	2.4	8.9	54.4	34.3
	19	87.01	−0.005	87	10.1	2.9	31	65.2	3.8	2.3	7.5	50	40.2
	20	2.01	−0.023	100	0	0	33.3	66.7	0	3.1	15.6	81.3	0
	21	450.55	−0.001	90.3	5.2	4.5	44.3	51.1	4.6	2	4.8	21.4	71.8
	22	3.86	−0.001	100	0	0	28.6	71.4	0	0	0	0	100
	23	41.17	−0.001	86.5	10.7	2.8	13.7	64.4	21.9	21.9	47.7	30.4	0
	24	2.19	−0.001	50.9	12.7	36.4	0	66.7	33.3	14.7	50	35.3	0
	25	1.78	−0.001	44.3	19.9	35.8	0	66.7	33.3	14.3	35.7	50	0
	26	24.2	−0.320	0	1.2	98.8	53.5	41.8	4.7	0	0	48.1	51.9

DOI: https://doi.org/10.22630/srees.9939 | Journal eISSN: 2543-7496 | Journal ISSN: 1732-9353

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Language: English

Page range: 329 - 351

Submitted on: Sep 11, 2024

Accepted on: Oct 1, 2024

Published on: Oct 29, 2024

Published by: Warsaw University of Life Sciences - SGGW Press

In partnership with: Paradigm Publishing Services

Keywords:

SAR,

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© 2024 Marta Stachowicz, Piotr Banaszuk, Pouya Ghezelayagh, Andrzej Kamocki, Dorota Mirosław-Świątek, Mateusz Grygoruk, published by Warsaw University of Life Sciences - SGGW Press
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.

Volume 33 (2024): Issue 4 (December 2024)Next article

Estimating mean groundwater levels in peatlands using a Bayesian belief network approach with remote sensing data

Figures & Tables

FIGURE 1.

FIGURE 2.

FIGURE 3.

FIGURE 4.

FIGURE 5.

FIGURE 6.

Classification of parameters used in the Bayesian network modela

Spearman’s rank correlation results between model variables

Piezometers within the Biebrza National Park used in the study

Conditional probabilities of groundwater level classes generated from the Bayesian belief network

Area percentage contributions of each model variables class in polygons and plots used for validation

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