Analysis of airborne SAR data (L-band) for discrimination land use/land cover types in the Brazilian Amazon region

Abstract: In northern Brazilian Amazon, the crops, savannahs and rainforests form a complex landscape where land use and land cover (LULC) mapping is difficult. Here, data from the Operational Land Imager (OLI)/Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)/ALOS-2 were combined for mapping 17 LULC classes using Random Forest (RF) during the dry season. The potential thematic accuracy of each dataset was assessed and compared with results of the hybrid classification from both datasets. The results showed that the combination of PALSAR-2 HH/HV amplitudes with the reflectance of the six OLI bands produced an overall accuracy of 83% and a Kappa of 0.81, which represented an improvement of 6% in relation to the RF classification derived solely from OLI data. The RF models using OLI multispectral metrics performed better than RF models using PALSAR-2 L-band dual polarization attributes. However, the major contribution of PALSAR-2 in the savannahs was to discriminate low biomass classes such as savannah grassland and wooded savannah.

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Land Use and Land Cover Mapping in the Brazilian Amazon Using Polarimetric Airborne P-Band SAR Data

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2008.2000630 ◽

2008 ◽

Vol 46 (10) ◽

pp. 2956-2970 ◽

Cited By ~ 39

Author(s):

Corina da Costa Freitas ◽

Luciana de Souza Soler ◽

Sidnei JoÃo Siqueira Sant'Anna ◽

Luciano Vieira Dutra ◽

JoÃo Roberto dos Santos ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Brazilian Amazon ◽

Land Cover Mapping ◽

Sar Data

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REMOTE-SENSING EXPERT CLASSIFICATION OF LAND USE/LAND COVER TYPES USING FRACTAL DIMENSIONS OVER A SUBTROPICAL HILLY REGION IN CHINA

Fractals ◽

10.1142/s0218348x11005452 ◽

2011 ◽

Vol 19 (04) ◽

pp. 407-421

Author(s):

JI ZHU ◽

ZIYU LIN ◽

XIAOZHOU LI

Keyword(s):

Remote Sensing ◽

Land Use ◽

Fractal Dimension ◽

Land Cover ◽

Fractal Dimensions ◽

Land Use Land Cover ◽

Surface Features ◽

Land Cover Types ◽

Hilly Region ◽

Expert Classification

In the work, a simple and reliable algorithm is presented to calculate the fractal dimension of single pixel for the remote sensing images, and the fractal dimension values obtained by the algorithm proposed in this work have positive correlation with the complexity of surface features. On the basis of a scene of Landsat7 ETM+ (i.e., Enhanced Thematic Mapper Plus) data and the proposed algorithm, expert classification models and fractal technique were introduced to identify the ground objects in a Chinese subtropical hilly region, where surface features are very diverse and complex. In the work, the different land use/land cover types, especially the different vegetation categories were successfully identified using the ETM+ image, and this classification has an overall accuracy of 80.25% and a K hat of 0.7738, which are higher than those of the traditional supervised classification.

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Total carbon and nitrogen stocks under different land use/land cover types in the Southwestern region of Nigeria

Geoderma Regional ◽

10.1016/j.geodrs.2020.e00320 ◽

2020 ◽

Vol 22 ◽

pp. e00320

Author(s):

Idowu Ezekiel Olorunfemi ◽

Johnson Toyin Fasinmirin ◽

Ayorinde Akinlabi Olufayo ◽

Akinola Adesuji Komolafe

Keyword(s):

Land Use ◽

Land Cover ◽

Total Carbon ◽

Land Use Land Cover ◽

Land Cover Types ◽

Carbon And Nitrogen ◽

Carbon And Nitrogen Stocks

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Land Use, Land Cover and Land Use Change in the Brazilian Amazon (1960–2013)

Ecological Studies - Interactions Between Biosphere, Atmosphere and Human Land Use in the Amazon Basin ◽

10.1007/978-3-662-49902-3_15 ◽

2016 ◽

pp. 369-383 ◽

Cited By ~ 3

Author(s):

Jean P. Ometto ◽

Eráclito R. Sousa-Neto ◽

Graciela Tejada

Keyword(s):

Land Use ◽

Land Use Change ◽

Land Cover ◽

Brazilian Amazon ◽

Land Use Land Cover

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Analysis of L-band SAR backscatter and coherence for delineation of land-use/land-cover

International Journal of Remote Sensing ◽

10.1080/01431161.2014.965282 ◽

2014 ◽

Vol 35 (18) ◽

pp. 6781-6798 ◽

Cited By ~ 8

Author(s):

N. Parihar ◽

A. Das ◽

V.S. Rathore ◽

M.S. Nathawat ◽

S. Mohan

Keyword(s):

Land Use ◽

Land Cover ◽

Land Use Land Cover ◽

L Band

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A matrix clustering method to explore patterns of land-cover transitions in satellite-derived maps of the Brazilian Amazon

Nonlinear Processes in Geophysics ◽

10.5194/npg-24-113-2017 ◽

2017 ◽

Vol 24 (1) ◽

pp. 113-123 ◽

Cited By ~ 7

Author(s):

Finn Müller-Hansen ◽

Manoel F. Cardoso ◽

Eloi L. Dalla-Nora ◽

Jonathan F. Donges ◽

Jobst Heitzig ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Transition Probabilities ◽

Brazilian Amazon ◽

Regional Patterns ◽

Local Climate ◽

Tropical Regions ◽

Land Cover Types ◽

Socioeconomic Drivers ◽

Land Cover Dynamics

Abstract. Changes in land-use systems in tropical regions, including deforestation, are a key challenge for global sustainability because of their huge impacts on green-house gas emissions, local climate and biodiversity. However, the dynamics of land-use and land-cover change in regions of frontier expansion such as the Brazilian Amazon are not yet well understood because of the complex interplay of ecological and socioeconomic drivers. In this paper, we combine Markov chain analysis and complex network methods to identify regimes of land-cover dynamics from land-cover maps (TerraClass) derived from high-resolution (30 m) satellite imagery. We estimate regional transition probabilities between different land-cover types and use clustering analysis and community detection algorithms on similarity networks to explore patterns of dominant land-cover transitions. We find that land-cover transition probabilities in the Brazilian Amazon are heterogeneous in space, and adjacent subregions tend to be assigned to the same clusters. When focusing on transitions from single land-cover types, we uncover patterns that reflect major regional differences in land-cover dynamics. Our method is able to summarize regional patterns and thus complements studies performed at the local scale.

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