Mapping land cover dynamics with Copernicus

Abstract The present study aimed to analyse changes in the land cover of Vilnius city and its surrounding areas and propose a scenario for their future changes using an Artificial Neural Network. The land cover dynamics modelling was based on a multilayer perceptron neural network. Landscape metrics at a class and landscape level were evaluated to determine the amount of changes in the land uses. As the results showed, the Built-up area class increased, while the forest (Semi forest and Dense forest) classes decreased during the period from 1999 to 2019. The predicted scenario showed a considerable increase of about 60 % in the Built-up area until 2039. The vegetation plant areas consist about 47 % of all the area in 2019, but it will be 36 % in 2039, if this trend (urban expansion) continues in the further. The findings further indicated the major urban expansion in the vegetation areas. However, Built-up area would expand over Semi forest land and Dense forest land, with a large part of them changed into built- up areas.

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LAND COVER DYNAMICS AND LANDSCAPE STRUCTURE IN THE AREA SURROUNDING WATER RESERVOIRS, MOUNTAINOUS REGION OF RIO DE JANEIRO

FLORESTA ◽

10.5380/rf.v50i4.65893 ◽

2020 ◽

Vol 50 (4) ◽

pp. 1808

Author(s):

Lucas De Siqueira Cardinelli ◽

José Marinaldo Gleriani ◽

Sebastião Venâncio Martins

Keyword(s):

Land Cover ◽

Landscape Structure ◽

Rio De Janeiro ◽

Forest Cover ◽

Water Area ◽

Mountainous Region ◽

Water Reservoirs ◽

Landsat Images ◽

Surrounding Water ◽

Land Cover Dynamics

The aim of this study is to evaluate land cover dynamics and landscape structure in the area surrounding two water reservoirs built-in 2009 for energy production, in the mountainous region of the State of Rio de Janeiro (Serra Fluminense). The analysis was developed through the interpretation of Landsat images from 2003, 2009, and 2013, considering the following land cover classes: early successional forest, mid successional forest, pasture, pasture with shrubs and trees, geological outcrop, urban area, and water area. We used thematic maps to determine landscape metrics of size and proximity in the reservoirs catchment area and the Permanent Preservation Area (PPA). At catchment level, pasture was predominant, a consequence of the extensive livestock production carried out in the whole watershed. During the evaluated period, the forest area remained consistent, however, fragmented in many small patches of mid successional forest. The average patch area of mid successional forest is three times the size of the early successional forest patches. For neither forest land cover classes, no significant variations through time in area or isolation were identified. On the PPA, an overall reduction of the forest cover was registered before the construction of the reservoir. However, from 2009 to 2013, after the enclosure of PPA areas, the forest cover increased 35% via assisted natural regeneration, suggesting a high potential for cost-effective restoration in the region.

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