Relationship between remote sensing data and field-observed interril erosion

The objective of this work was to evaluate the relationship between different remote sensing data, derived from satellite images, and interrill soil losses obtained in the field by using a portable rainfall simulator. The study was carried out in an area of a hydrographic basin, located in Médio Paraíba do Sul, in the state of Rio de Janeiro - one of the regions most affected by water erosion in Brazil. Evaluations were performed for different vegetation indices (NDVI, Savi, EVI, and EVI2) and fraction images (FI), derived from linear spectral mixture analysis (LSMA), obtained from RapidEye, Sentinel2A, and Landsat 8 OLI images. Vegetation indices are more adequate to predict soil loss than FI, highlighting EVI2, whose exponential model showed R² of 0.74. The best prediction models are generated from the RapidEye image, which shows the highest spatial resolution among the sensors evaluated.

Index terms:
linear spectral mixing analysis; rainfall simulator; vegetation indices

Authorship

André Geraldo de Lima Moraes

Universidade Federal Rural do Rio de Janeiro (UFRRJ), Instituto de Agronomia, Departamento de Solos, Rodovia BR-465, Km 7, Campus Universitário, CEP 23851-970 Seropédica, RJ, Brazil. E-mail: andrehmuz@hotmail.com, marcosceddia@gmail.comUniversidade Federal Rural do Rio de JaneiroBrazilSeropédica, RJ, BrazilUniversidade Federal Rural do Rio de Janeiro (UFRRJ), Instituto de Agronomia, Departamento de Solos, Rodovia BR-465, Km 7, Campus Universitário, CEP 23851-970 Seropédica, RJ, Brazil. E-mail: andrehmuz@hotmail.com, marcosceddia@gmail.com

Daniel Fonseca de Carvalho

Universidade Federal Rural do Rio de Janeiro, Instituto de Tecnologia, Departamento de Engenharia, Rodovia BR-465, Km 7, Campus Universitário, CEP 23851-970 Seropédica, RJ, Brazil. E-mail: carvalho@ufrrj.br, homemantunes@gmail.comUniversidade Federal Rural do Rio de JaneiroBrazilSeropédica, RJ, BrazilUniversidade Federal Rural do Rio de Janeiro, Instituto de Tecnologia, Departamento de Engenharia, Rodovia BR-465, Km 7, Campus Universitário, CEP 23851-970 Seropédica, RJ, Brazil. E-mail: carvalho@ufrrj.br, homemantunes@gmail.com

Mauro Antonio Homem Antunes

Marcos Bacis Ceddia

SCIMAGO INSTITUTIONS RANKINGS

Figures | Tables | Formulas

Figures (4)
Tables (4)
Formulas (5)

Thumbnail

Figure 1.
Study area and simulated rainfall sampling points, in the Cachimbal River basin, in the state of Rio de Janeiro, Brazil.

Thumbnail

Figure 2.
Correlation between soil loss and NDVI (A), Savi (B), EVI (C), EVI2 (D), FI_sh (E), FI_veg (F), and FI_bs (G), derived from RapidEye image.

Thumbnail

Figure 3.
Correlation between NDVI derived from RapidEye image and soil loss (SL), for the different soil cover classes: UMP, unmanaged pasture; FA, fallow area; DP, degraded pasture.

Thumbnail

Figure 4.
EVI2 derived from three different images: RapidEye (A), Sentinel 2A (B), and Landsat 8 OLI (C) of the Cachimbal River basin, in the state of Rio de Janeiro, Brazil.

Thumbnail

Table 1.
Characteristics of tested satellite images.

Thumbnail

Table 2.
Average, minimum, and maximum soil losses, and number of tests per soil cover class.

Thumbnail

Table 3.
Average, minimum, and maximum values of remote sensing data derived from RapidEye image of the main soil cover classes in the study area.

Thumbnail

Table 4.
Soil loss prediction models based on vegetation indices and fraction images derived from RapidEye, Sentinel 2A, and Landsat 8 OLI images.

Thumbnail

Figure 1. Study area and simulated rainfall sampling points, in the Cachimbal River basin, in the state of Rio de Janeiro, Brazil.

Figure 2. Correlation between soil loss and NDVI (A), Savi (B), EVI (C), EVI2 (D), FI_sh (E), FI_veg (F), and FI_bs (G), derived from RapidEye image.

Figure 3. Correlation between NDVI derived from RapidEye image and soil loss (SL), for the different soil cover classes: UMP, unmanaged pasture; FA, fallow area; DP, degraded pasture.

Figure 4. EVI2 derived from three different images: RapidEye (A), Sentinel 2A (B), and Landsat 8 OLI (C) of the Cachimbal River basin, in the state of Rio de Janeiro, Brazil.

Table 1. Characteristics of tested satellite images.

Image	Data	Number of bands	Spatial resolution⁽¹⁾ (m)	License
RapidEye	07/12/2015	5	6.5 resampled for 5	Private⁽²⁾
Sentinel 2A	08/01/2015	13	10	Free
Landsat 8 OLI	07/14/2015	13	30	Free

⁽¹⁾
Spatial resolution of the bands of interest used for vegetation monitoring. ⁽²⁾RapidEye images until 2014 can be obtained for free, for research purposes, through the Ministério do Meio Ambiente (MMA) of Brazil.

Table 2. Average, minimum, and maximum soil losses, and number of tests per soil cover class.

Soil cover class	Soil loss (kg ha^-1)			Tests per soil cover class
Soil cover class	Average	Maximum	Minimum	Tests per soil cover class
Forest	15.2	41.2	0.0	13
Unmanaged pasture	34.8	97.8	0.0	12
Fallow area	47.4	102.0	0.0	11
Pasture	66.7	158.8	20.88	26
Degraded pasture	267.0	618.0	99.5	9
Total	74.4	618.0	0.00	71

Table 3. Average, minimum, and maximum values of remote sensing data derived from RapidEye image of the main soil cover classes in the study area.

Data	Statistic	Soil cover classes
Data	Statistic	Forest	UMP	FA	Pasture	DP
NDVI	Average	0.84	0.75	0.69	0.69	0.49
	Maximum	0.90	0.85	0.84	0.85	0.63
	Minimum	0.80	0.62	0.61	0.44	0.33
Savi	Average	0.43	0.39	0.39	0.38	0.28
	Maximum	0.52	0.47	0.56	0.61	0.38
	Minimum	0.35	0.31	0.30	0.29	0.20
EVI	Average	0.41	0.36	0.36	0.35	0.25
	Maximum	0.52	0.45	0.57	0.55	0.35
	Minimum	0.32	0.28	0.28	0.24	0.17
EVI 2	Average	0.42	0.38	0.38	0.37	0.27
	Maximum	0.51	0.48	0.59	0.51	0.37
	Minimum	0.35	0.30	0.29	0.27	0.19
FI_sh	Average	0.47	0.47	0.33	0.43	0.43
	Maximum	0.65	0.65	0.60	0.58	0.56
	Minimum	0.29	0.17	0.10	0.06	0.26
FI_veg	Average	0.52	0.46	0.49	0.45	0.30
	Maximum	0.71	0.60	0.87	0.85	0.47
	Minimum	0.38	0.32	0.31	0.27	0.16
FI_bs	Average	0.02	0.07	0.16	0.17	0.26
	Maximum	0.06	0.19	0.26	0.32	0.39
	Minimum	0.00	0.02	0.01	0.07	0.14

UMP, unmanaged pasture; FA, fallow area; DP, degraded pasture.

Table 4. Soil loss prediction models based on vegetation indices and fraction images derived from RapidEye, Sentinel 2A, and Landsat 8 OLI images.

Remote sensing data	Model	R²	RMSE (kg ha^-1)
	RapidEye
NDVI	SL = 14.473NDVI^-3.335	0.66	62.41
Savi	SL = 0.762Savi^-4.201	0.70	57.63
EVI	SL = 0.869EVI^-3.739	0.72	56.37
EVI 2	SL = 0.590EVI2^-4.229	0.74	54.15
FI_sh	SL = 99.675FI_sh^0.309	0.01	105.16
FI_veg	SL = 6.527FI_veg^-2.503	0.64	62.72
FI_bs	SL = 1192.513FI_bs^1.468	0.41	82.16
	Sentinel 2A
NDVI	SL = 177.473 - 182.573NDVI	0.07	102.17
Savi	SL = -8.280 - 69.708lnSavi	0.03	104.07
EVI	SL = 7.580 - 55.441lnEVI	0.03	104.41
EVI2	SL = -65.944 - 6.595lnEVI2	0.03	103.94
FI_sh	SL = 93.185 - 45.270FI_sh	0.00	105.58
FI_veg	SL = 28.262 - 48.419FI_veg	0.03	104.31
FI_bs	SL = 37.251 - 209.527FI_bs	0.05	102.87
	Landsat8 OLI
NDVI	SL = 211.447 - 163.303NDVI	0.03	103.94
Savi	SL = 67.597 + 17.897Savi	0.00	105.75
EVI	SL = 73.110 + 3.510EVI	0.00	105.76
EVI2	SL = 72.1771 + 6.115EVI2	0.00	105.76
FI_sh	SL = 98.271 - 68.557FI_sh	0.01	105.19
FI_veg	SL = 75.959 - 2.687FI_veg	0.00	105.76
FI_bs	SL = 133.355FI_bs^-19.711	0.04	103.72

RMSE, root mean square error.

N D V I = ρ_{N I R} - ρ_{R E D} / ρ_{N I R} + ρ_{R E D}

S a v i = (1 + L) (ρ_{N I R} - ρ_{R E D}) / ρ_{N I R} + ρ_{R E D} + L

E V I = 2.5 (ρ_{N I R} - ρ_{R E D} / ρ_{N I R} - C_{1} ρ_{R E D} + C_{2} ρ_{B L U E} + L)

E V I = 2.5 (ρ_{N I R} - ρ_{R E D} / ρ_{N I R} + 2.4 ρ_{R E D} + 1)

R i = \sum_{j = 1}^{N} f_{j} r_{i j} + ε_{i} a n d \sum_{j = 1}^{N} f_{j} = 1; 0 \leq f_{j} \leq 1,

How to cite

copy

Embrapa Secretaria de Pesquisa e Desenvolvimento; Pesquisa Agropecuária Brasileira Caixa Postal 040315, 70770-901 Brasília DF Brazil, Tel. +55 61 3448-1813, Fax +55 61 3340-5483 - Brasília - DF - Brazil
E-mail: pab@embrapa.br

Acompanhe os números deste periódico no seu leitor de RSS

Versão para download de PDF

PDF

Inglês

Versões e tradução automática

Versão original do texto

English

Tradução automática

Google Translator
Microsoft Translator

Como citar

RIS

BIBTEX

Outros formatos de citação e exportação:

SciELO - Scientific Electronic Library Online
Rua Dr. Diogo de Faria, 1087 – 9º andar – Vila Clementino 04037-003 São Paulo/SP - Brasil
E-mail: scielo@scielo.org

Leia a Declaração de Acesso Aberto

Métricas

Relationship between remote sensing data and field-observed interril erosion

PlumX

Mensagem

[TFN1] ⁽¹⁾
Spatial resolution of the bands of interest used for vegetation monitoring. ⁽²⁾RapidEye images until 2014 can be obtained for free, for research purposes, through the Ministério do Meio Ambiente (MMA) of Brazil.