FLORISTIC DIVERSITY AND EQUITABILITY IN FOREST FRAGMENTS USING ARTIFICIAL NEURAL NETWORKS

Cabacinha, Christian Dias; Lafetá, Bruno Oliveira

doi:10.5902/1980509826454

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Artigos • Ciênc. Florest. 27 (1) • Mar 2017 • https://doi.org/10.5902/1980509826454 copy

FLORISTIC DIVERSITY AND EQUITABILITY IN FOREST FRAGMENTS USING ARTIFICIAL NEURAL NETWORKS

DIVERSIDADE FLORÍSTICA E EQUABILIDADE EM FRAGMENTOS FLORESTAIS USANDO REDES NEURAIS ARTIFICIAIS

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

This study aimed to evaluate the predictive efficiency of Shannon index (H') and Pielou Equitability index (J) in forest fragments from the Brazilian Cerrado biome, from the vegetation indices and landscape metrics using artificial neural networks (ANN). Feedforward networks were used and they were trained through a back propagation error algorithm. The variables used as ANN input for simultaneous estimation of indices were: the categorical (H' and J) and the numbers related to the mean and standard deviation of vegetation indices (NDVI, SAVI, EVI, and MVI5, MVI7) and landscape metrics (AREA, GYRATE, SHAPE, CONTIG, CORE and ENN). It was generated five models of ANN from the functional relationships between numerical variables inherent to vegetation indices in two seasons, a dry season (June) and a rainy season (February). The architecture of the networks was the Multilayer Perceptron (MLP), to estimate simultaneously the H' and J: 500 using vegetation indices in the wet season (100 for each vegetation index) and 500 in dry (100 for each vegetation index). The precision, accuracy and realism of biological ANN were assessed. The nets built during the rainy season and dry season that used vegetation indices MVI5 (Moisture Vegetation Index) and SAVI (Soil Adjusted Vegetation Index), respectively, were more appropriate, accurate and biologically realistic to estimate both indices H' and J. The ANN modeling demonstrated to be adequate to estimate the diversity index.

Keywords:
biological diversity; Brazilian Cerrado; MLP

Universidade Federal de Santa Maria Av. Roraima, 1.000, 97105-900 Santa Maria RS Brasil, Tel. : (55 55)3220-8444 r.37, Fax: (55 55)3220-8444 r.22 - Santa Maria - RS - Brazil
E-mail: cienciaflorestal@ufsm.br

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[TFN1] Em que: A = ÁREA. G = GYRATE. S = SHAPE. When "Índice" equal 1 and 2 refer to H' and J, respectively. n = number of observations.

ANN	Architecture	Correlation coefficient		Cycles	Activation function
ANN	Architecture	Training	Validation		Hidden	Output
1	MLP 10-6-1	0.9982^*	0.9509^*	902	Logistic	Tangential
2	MLP 10-8-1	0.9986^*	0.9454^*	385	Logistic	Logistic
3	MLP 10-5-1	0.9906^*	0.8975^*	64	Identity	Logistic
4	MLP 10-12-1	0.9999^*	0.9835^*	1029	Exponential	Logistic
5	MLP 10-6-1	0.9994^*	0.9637^*	660	Logistic	Identity
6	MLP 10-4-1	0.9970^*	0.9416^*	187	Logistic	Logistic
7	MLP 10-13-1	0.9999^*	0.9837^*	449	Exponential	Exponential
8	MLP 10-5-1	0.9742^*	0.9534^*	109	Identity	Tangential
9	MLP 10-9-1	0.9993^*	0.9610^*	671	Logistic	Exponential
10	MLP 10-4-1	0.9978^*	0.9522^*	315	Logistic	Tangential

ANN	Phases	RMSE _%	Bias _%	Relative errors (%)			t-test
ANN	Phases	RMSE _%	Bias _%	Maximum	Medium	Minimum	p
1	Training	3.2	0.3	5.2	-0.1	-5.2	0.5339
1	Validation	16.7	0.2	17.2	0.1	-16.9	0.9688
2	Training	2.9	0.3	9.1	-0.1	-6.6	0.4938
2	Validation	18.0	2.2	18.9	-1.8	-18.2	0.7402
3	Training	7.2	0.7	13.5	-0.6	-10.3	0.5866
3	Validation	25.3	4.1	21.1	-3.8	-32.2	0.6568
4	Training	0.8	0.1	4.0	-0.1	-6.3	0.5442
4	Validation	11.6	-3.5	14.7	1.6	-13.5	0.4020
5	Training	1.8	0.0	7.6	0.1	-5.0	1.0000
5	Validation	14.8	-1.7	16.8	1.1	-13.6	0.7521
6	Training	4.2	0.4	10.8	0.1	-6.9	0.5848
6	Validation	18.3	0.7	20.1	-0.3	-19.2	0.9120
7	Training	0.7	0.0	4.8	0.1	-3.8	0.8859
7	Validation	9.6	0.0	8.9	-0.4	-14.0	0.9938
8	Training	11.8	0.5	24.7	0.2	-19.1	0.8184
8	Validation	17.2	0.9	17.2	-3.2	-30.1	0.8859
9	Training	2.0	-0.3	11.9	0.6	-4.3	0.4373
9	Validation	14.9	-0.1	18.8	0.1	-14.4	0.9909
10	Training	3.5	0.4	6.5	-0.2	-5.6	0.4677
10	Validation	16.3	-0.2	17.9	-18.0	-18.0	0.9696

Season	ANN	n	--------------------------- Inputs ---------------------------
Season	ANN	n	Numerical	Categorical
Rainy (February)	1	44	NDVIM, NDVID, A, G, S, CONTIG, CORE, ENN	Índice
	2	44	SAVIM, SAVID, A, G, S, CONTIG, CORE, ENN	Índice
	3	44	EVIM, EVID, A, G, S, CONTIG, CORE, ENN	Índice
	4	44	MVI5M, MVI5D, A, G, S, CONTIG, CORE, ENN	Índice
	5	44	MVI7M, MVI7D, A, G, S, CONTIG, CORE, ENN	Índice
Dry (June)	6	44	NDVIM, NDVID, A, G, S, CONTIG, CORE, ENN	Índice
	7	44	SAVIM, SAVID, A, G, S, CONTIG, CORE, ENN	Índice
	8	44	EVIM, EVID, A, G, S, CONTIG, CORE, ENN	Índice
	9	44	MVI5M, MVI5D, A, G, S, CONTIG, CORE, ENN	Índice
	10	44	MVI7M, MVI7D, A, G, S, CONTIG, CORE, ENN	Índice