PREDICTION OF THE FUTURE SCENARIO OF THE EXPANSION OF AREAS OF FORESTRY INTO THE BRAZILIAN CERRADO USING A CELLULAR AUTOMATA MARKOV CHAIN MODEL

Vick, Erivelton Pereira; Bacani, Vitor Matheus

doi:10.4215/rm2019.e18026

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Articles • Mercator (Fortaleza) 18 • Dec 2019 • https://doi.org/10.4215/rm2019.e18026 linkcopy

PREDICTION OF THE FUTURE SCENARIO OF THE EXPANSION OF AREAS OF FORESTRY INTO THE BRAZILIAN CERRADO USING A CELLULAR AUTOMATA MARKOV CHAIN MODEL

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Predictive models for the detection landscape change have been widely-used as an important tool for environmental planning and analysis. The approach that combines Markov Chains with cellular automata has been shown to produce results with excellent calibration and validation for the simulation of changes in land use and cover. The present study focused on the hydrographic basin of the Pântano River, located in the Brazilian Cerrado savanna, which has been impacted by a number of major projects, including a hydroelectric dam, and the world’s largest paper and pulp industries. The study simulated the changes in land cover and use for the year 205. Evaluating the spatial effects of the expansion of forestry into the Cerrado in the context of its different degrees of natural potential fragility. To predict the future scenario, the cellular automata Markov Chain method was combined with a multicriteria analysis using AHP logic. The model validation indicated excellent results (Kappa index greater than 0.9) for the predictions of the 2050 scenario. The model revealed a strong overall tendency for the substitution of livestock by forestry by 2050, with a predominance of expansion into areas of medium environmental fragility.

Keywords: Dynamic modeling; Watershed; GIS; Geotechnologies

YEAR	SCALE	SHAPE	COMPACTNESS
1984	0.4	0.003	0.5
2007	0.3	0.0004	0.5
2013	0.3	0.0002	0.2
2017	0.4	0.0002	0.2

CHARACTERISTICS OF THE OBJECT	CHARACTERISTICS USED
Customized	NDVI and NDWI
Layer Values	Mean/standard deviation of the spectral parameters of the bands
Geometry	Extension of the object (Area; Perimeter; Length/Thickness; Length/Width; Number of pixels); Shape

1984	2007	2013	2017
ABSENT	ABSENT	Farmland	Farmland
Water	Water	Water	Water
ABSENT	Sugarcane	Sugarcane	Sugarcane
ABSENT	Eucalyptus	Eucalyptus	Eucalyptus
Pasture	Pasture	Pasture	Pasture
Arboreal savanna	Arboreal savanna	Arboreal savanna	Arboreal savanna
Grassland/shrubby savanna	Grassland/shrubby savanna	Grassland/shrubby savanna	Grassland/shrubby savanna
Savanna woodland	Savanna woodland	Savanna woodland	Savanna woodland
ABSENT	ABSENT	Rubber	Rubber
Exposed soil	Exposed soil	Exposed soil	Exposed soil
Riparian vegetation	Riparian vegetation	Riparian vegetation	Riparian vegetation

Class used to map the changes in land cover and use	Class used in the simulation
Water	Water
Farmland	Farmland
Sugarcane	Farmland
Pasture	Pasture
Arboreal savanna	Natural Vegetation
Grassland/shrubby savanna
Savanna woodland
Riparian vegetation
Eucalyptus	Forestry
Rubber	Forestry
Exposed Soil	Exposed soil

Soil type	Weight
Body of water	-
LEa11 - Dark-Red Latosol - medium alic texture	1
LEa22 - Dark-Red Latosol - medium alic texture	1
LEa4 - Dark-Red Latosol - medium alic and dystrophic texture	1
LEa9 - Dark-Red Latosol - medium alic texture	1
LRd4 - Purple alic Latosol	1
PEd1 - Dystrophic Dark-Red Acrisol	3
PEd3 - Dystrophic Dark-Red Acrisol	3
PVa2-Alic Red-Yellow Acrisol	3
PLa3 - Eutrophic Planosol	4

SLOPE
Class	Weight	Fragility
0-6%	1	Very Weak
6-12%	2	Weak
12-20%	3	Medium
20-30%	4	Strong
> 30%	5	Very Strong

Precipitation-HBPR	Rainfall Intensity: Minimum/Maximum	Weight
1478.24-1515.89 mm	164.2/168.4	3*

	1984		2007		2013		2017
Class	Producer Accuracy	User Accuracy	Producer Accuracy	User Accuracy	Producer Accuracy	User Accuracy	Producer Accuracy	User Accuracy
Farmland	-	-	-	-	100	100	50	100
Water	72.73	100	100	69.23	66.67	100	42.86	100
Sugarcane	-	-	100	100	100	100	100	100
Eucalyptus	-	-	75	100	100	98.41	0	0
Pasture	98.82	90.32	100	88.99	100	85.54	97.22	79.55
Arboreal savanna	89.86	83.78	87.04	94	92.68	97.44	89.47	91.89
Savanna woodland	80	84.21	94.44	77.27	87.5	100	81.25	86.67
Grassland/shrubby savanna	73.91	89.47	75	88.24	66.67	92.31	77.78	100
Rubber	-	-	-	-	0	0	50	100
Exposed Soil	0	0	14.29	100	0	0	98.28	86.93
Riparian Vegetation	94.32	95.4	92.86	100	100	88.24	96.88	98.41
Global Accuracy (%)	90		91.33		92		89.33
Kappa index	0.8685		0.8874		0.9024		0.8697

Parameter of the Kappa statistic	Value	Agreement
Kstandard	0.9085	Excellent
Kno	0.9290	Excellent
KlocationStrata	0.9200	Excellent

	Water	Farmland	NaturalVegetation	Pasture	Forestry	Exposed soil
Water	0.5998	0.0035	0.1711	0.1483	0.0771	0.0002
Farmland	0.0006	0.6408	0.0702	0.1811	0.1069	0.0002
Natural Vegetation	0.0088	0.0081	0.3753	0.3417	0.2656	0.0005
Pasture	0.0017	0.0112	0.1157	0.3416	0.5292	0.0006
Forestry	0.0010	0.0044	0.1104	0.1690	0.7149	0.0002
Exposed soil	0.0013	0.0099	0.1025	0.4024	0.4825	0.0013

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