A spatially explicit index for mapping Forest Restoration Vocation (FRV) at the landscape scale: Application in the Rio Doce basin, Brazil.

Agroforestry Forest restoration as Nature based Solution (NbS) Socio economic and institutional indicators Spatially explicit modelling

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
20 Nov 2020
Historique:
received: 17 04 2020
revised: 29 06 2020
accepted: 29 06 2020
pubmed: 28 7 2020
medline: 28 7 2020
entrez: 28 7 2020
Statut: ppublish

Résumé

Effectively implementing landscape-scale forest restoration on the ground is particularly challenging. Available decision-support tools particularly lack the ability to comprehensively incorporate biophysical, social and institutional dimensions in a spatially explicit manner from the pixel to the whole landscape. In order to contribute to fulfilling this gap, this paper has two major objectives. The first is to present a spatially explicit decision-support tool for mapping Forest Restoration Vocation (FRV) that includes socio-economic and institutional aspects in forest landscape restoration. The second is to discuss the ways in which the FRV has been applied in the Brazilian decision-making context. The FRV was used to prioritize areas for three different restoration modalities: assisted natural regeneration (passive restoration), forest plantation with native trees to conserve biodiversity and forest plantation for agroforestry systems (active restoration). The FRV is already being adopted as a planning tool to invest R$ 1.2 billion (approx. US$ 300 million) to restore 40,000 ha in the Rio Doce, Brazil-an area corresponding to 0.05% of the area of watershed. Due to the high level of degradation of the basin, there is a need to restore 1.6 Mha via forest plantations in riparian Areas of Permanent Preservation (APPs) while 30% of APPs can be effectively restored using natural regeneration. The FRV can be effective for gauging progress and monitoring forest restoration implementation metrics across the landscape and through time. There are however still problems in effectively assessing if the investment in forest restoration will generate impact in the long term and deliver the ecosystem services society depends on.

Identifiants

pubmed: 32717460
pii: S0048-9697(20)34169-3
doi: 10.1016/j.scitotenv.2020.140647
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

140647

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Sónia M Carvalho Ribeiro (SM)

Universidade Federal de Minas Gerais, Programa de Pós Graduação em Análise e Modelagem de Sistemas Ambientais (PPG-AMSA), Centro de Sensoriamento Remoto. Instituto de Geociencias. Av. Antônio Carlos, 6627, Belo Horizonte, - MG, CEP, 31270-900, Brazil. Electronic address: soniacarvalhoribeiro@cart.igc.ufmg.br.

Raoni Rajão (R)

Universidade Federal de Minas Gerais, Programa de Pós Graduação em Análise e Modelagem de Sistemas Ambientais (PPG-AMSA), School of Engeneering. Av. Antônio Carlos, 6627, Belo Horizonte, - MG, CEP, 31270-900, Brazil.

Felipe Nunes (F)

Universidade Federal de Minas Gerais, Centro de Inteligênciua Territorial(CIT), Belo Horizonte -, MG, CEP, 31270-900, Brazil.

Débora Assis (D)

Universidade Federal de Minas Gerais, Instituto Geociências Av. Antônio Carlos, 6627, Belo Horizonte, - MG, CEP, 31270-900, Brazil.

José Ambrósio Neto (JA)

Universidade Federal de Viçosa. Departamento Economia Rural. Viçosa., Brazil.

Camilla Marcolino (C)

Universidade Federal de Minas Gerais, School of Engeneering Av. Antônio Carlos, 6627, Belo Horizonte, - MG, CEP, 31270-900, Brazil. Electronic address: millamarcolino@yahoo.com.br.

Leticia Lima (L)

Universidade Federal de Minas Gerais, School of Engeneering Av. Antônio Carlos, 6627, Belo Horizonte, - MG, CEP, 31270-900, Brazil.

Thomas Rickard (T)

Universidade Federal de Minas Gerais, Programa de Pós Graduação em Análise e Modelagem de Sistemas Ambientais (PPG-AMSA , Av. Antônio Carlos, 6627, Belo Horizonte, - MG, CEP, 31270-900, Brazil.

Caroline Salomão (C)

Universidade Federal de Minas Gerais, Programa de Pós Graduação em Análise e Modelagem de Sistemas Ambientais (PPG-AMSA), Instituto Geociências, Av. Antônio Carlos, 6627, Belo Horizonte, - MG, CEP, 31270-900, Brazil.

Britaldo Soares Filho (BS)

Universidade Federal de Minas Gerais, Programa de Pós Graduação em Análise e Modelagem de Sistemas Ambientais (PPG-AMSA), Centro de Sensoriamento Remoto. Instituto Geociências, Av. Antônio Carlos, 6627, Belo Horizonte, - MG, CEP, 31270-900, Brazil. Electronic address: britaldo@csr.ufmg.br.

Classifications MeSH