Supplementary material to "Patterns of land-cover transitions from satellite imagery of the Brazilian Amazon"

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 is not yet well understood because of the complex interplay of ecological and socio-economic 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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Response to Referee Comments for the Manuscript “Patterns of land-cover transitions from satellite imagery of the Brazilian Amazon” by Finn Müller-Hansen et al.

10.5194/npg-2016-53-ac1 ◽

2017 ◽

Author(s):

Finn Müller-Hansen

Keyword(s):

Land Cover ◽

Satellite Imagery ◽

Brazilian Amazon

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Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽

10.3390/geosciences11080312 ◽

2021 ◽

Vol 11 (8) ◽

pp. 312

Author(s):

Barbara Wiatkowska ◽

Janusz Słodczyk ◽

Aleksandra Stokowska

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Satellite Imagery ◽

Urban Areas ◽

Agricultural Land ◽

Urban Expansion ◽

Landsat 8 ◽

Rate Of Increase ◽

Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

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Hydrological regime, water availability and land use/land cover change impact on the water balance in a large agriculture basin in the Southern Brazilian Amazon

Journal of South American Earth Sciences ◽

10.1016/j.jsames.2021.103224 ◽

2021 ◽

Vol 108 ◽

pp. 103224

Author(s):

Tárcio Rocha Lopes ◽

Cornélio Alberto Zolin ◽

Rafael Mingoti ◽

Laurimar Gonçalves Vendrusculo ◽

Frederico Terra de Almeida ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Water Balance ◽

Land Cover Change ◽

Water Availability ◽

Hydrological Regime ◽

Brazilian Amazon ◽

Land Use Land Cover ◽

Change Impact

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Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

Journal of Intelligent Systems ◽

10.1515/jisys-2019-0155 ◽

2020 ◽

Vol 30 (1) ◽

pp. 273-286

Author(s):

Kalyan Mahata ◽

Rajib Das ◽

Subhasish Das ◽

Anasua Sarkar

Keyword(s):

Land Use ◽

Image Segmentation ◽

Cellular Automata ◽

Land Cover ◽

Satellite Imagery ◽

Current Model ◽

Discrete Dynamical System ◽

Land Use Land Cover ◽

Data Set ◽

Validity Indices

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.

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PALSAR-2/ALOS-2 AND OLI/LANDSAT-8 DATA INTEGRATION FOR LAND USE AND LAND COVER MAPPING IN NORTHERN BRAZILIAN AMAZON

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702018000200017 ◽

2018 ◽

Vol 24 (2) ◽

pp. 250-269 ◽

Cited By ~ 5

Author(s):

João Arthur Pompeu Pavanelli ◽

João Roberto dos Santos ◽

Lênio Soares Galvão ◽

Maristela Xaud ◽

Haron Abrahim Magalhães Xaud

Keyword(s):

Land Use ◽

Land Cover ◽

Brazilian Amazon ◽

Landsat 8 ◽

Array Type ◽

Complex Landscape ◽

Hybrid Classification ◽

L Band ◽

Thematic Accuracy ◽

Better Than

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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