IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v9y2020i5p139-d353288.html
   My bibliography  Save this article

Maximum Fraction Images Derived from Year-Based Project for On-Board Autonomy-Vegetation (PROBA-V) Data for the Rapid Assessment of Land Use and Land Cover Areas in Mato Grosso State, Brazil

Author

Listed:
  • Henrique Luis Godinho Cassol

    (National Institute for Space Research (INPE), Av. dos Astronautas, 1758, CEP: 12.227-010 São José dos Campos, SP, Brazil)

  • Egidio Arai

    (National Institute for Space Research (INPE), Av. dos Astronautas, 1758, CEP: 12.227-010 São José dos Campos, SP, Brazil)

  • Edson Eyji Sano

    (Embrapa Cerrados, BR-020, km 18, CEP: 73301-970 Planaltina, DF, Brazil)

  • Andeise Cerqueira Dutra

    (National Institute for Space Research (INPE), Av. dos Astronautas, 1758, CEP: 12.227-010 São José dos Campos, SP, Brazil)

  • Tânia Beatriz Hoffmann

    (National Institute for Space Research (INPE), Av. dos Astronautas, 1758, CEP: 12.227-010 São José dos Campos, SP, Brazil)

  • Yosio Edemir Shimabukuro

    (National Institute for Space Research (INPE), Av. dos Astronautas, 1758, CEP: 12.227-010 São José dos Campos, SP, Brazil)

Abstract

This paper presents a new approach for rapidly assessing the extent of land use and land cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of fraction images derived from the linear spectral mixing model (LSMM) instead of original bands. The LSMM was applied to the Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data composites from 2015 (~73 scenes/year, cloud-free images, in theory), generating vegetation, soil, and shade fraction images. These fraction images highlight the LULC components inside the pixels. The other new idea is to reduce these time series to only six single bands representing the maximum and standard deviation values of these fraction images in an annual composite, reducing the volume of data to classify the main LULC classes. The whole image classification process was conducted in the Google Earth Engine platform using the pixel-based random forest algorithm. A set of 622 samples of each LULC class was collected by visual inspection of PROBA-V and Landsat-8 Operational Land Imager (OLI) images and divided into training and validation datasets. The performance of the method was evaluated by the overall accuracy and confusion matrix. The overall accuracy was 92.4%, with the lowest misclassification found for cropland and forestland (<9% error). The same validation data set showed 88% agreement with the LULC map made available by the Landsat-based MapBiomas project. This proposed method has the potential to be used operationally to accurately map the main LULC areas and to rapidly use the PROBA-V dataset at regional or national levels.

Suggested Citation

  • Henrique Luis Godinho Cassol & Egidio Arai & Edson Eyji Sano & Andeise Cerqueira Dutra & Tânia Beatriz Hoffmann & Yosio Edemir Shimabukuro, 2020. "Maximum Fraction Images Derived from Year-Based Project for On-Board Autonomy-Vegetation (PROBA-V) Data for the Rapid Assessment of Land Use and Land Cover Areas in Mato Grosso State, Brazil," Land, MDPI, vol. 9(5), pages 1-20, May.
    • Handle: RePEc:gam:jlands:v:9:y:2020:i:5:p:139-:d:353288
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/9/5/139/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/9/5/139/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Avery S. Cohn & Juliana Gil & Thomas Berger & Heitor Pellegrina & Chantal Toledo, "undated". "Patterns and Processes of Pasture to Crop Conversion in Brazil: Evidence from Mato Grosso State," Mathematica Policy Research Reports 8ce99775615f42b98ff43f530, Mathematica Policy Research.
    2. Luiz E. O. C. Aragão & Liana O. Anderson & Marisa G. Fonseca & Thais M. Rosan & Laura B. Vedovato & Fabien H. Wagner & Camila V. J. Silva & Celso H. L. Silva Junior & Egidio Arai & Ana P. Aguiar & Jos, 2018. "21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    3. Gollnow, Florian & Hissa, Leticia de Barros Viana & Rufin, Philippe & Lakes, Tobia, 2018. "Property-level direct and indirect deforestation for soybean production in the Amazon region of Mato Grosso, Brazil," Land Use Policy, Elsevier, vol. 78(C), pages 377-385.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carmenta, Rachel & Cammelli, Federico & Dressler, Wolfram & Verbicaro, Camila & Zaehringer, Julie G., 2021. "Between a rock and a hard place: The burdens of uncontrolled fire for smallholders across the tropics," World Development, Elsevier, vol. 145(C).
    2. Nelson Villoria & Rachael Garrett & Florian Gollnow & Kimberly Carlson, 2022. "Leakage does not fully offset soy supply-chain efforts to reduce deforestation in Brazil," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Ananda Santa Rosa de Andrade & Rossano Marchetti Ramos & Edson Eyji Sano & Renata Libonati & Filippe Lemos Maia Santos & Julia Abrantes Rodrigues & Marcos Giongo & Rafael Rodrigues da Franca & Ruth El, 2021. "Implementation of Fire Policies in Brazil: An Assessment of Fire Dynamics in Brazilian Savanna," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    4. Aidt, Toke S. & Albornoz, Facundo & Hauk, Esther, 2024. "To cut or not to cut: Deforestation policy under the shadow of foreign influence," Journal of Economic Behavior & Organization, Elsevier, vol. 227(C).
    5. Michelle C. A. Picoli & Ana Rorato & Pedro Leitão & Gilberto Camara & Adeline Maciel & Patrick Hostert & Ieda Del’Arco Sanches, 2020. "Impacts of Public and Private Sector Policies on Soybean and Pasture Expansion in Mato Grosso—Brazil from 2001 to 2017," Land, MDPI, vol. 9(1), pages 1-15, January.
    6. Miqueias Lima Duarte & Tatiana Acácio Silva & Jocy Ana Paixão Sousa & Amazonino Lemos Castro & Roberto Wagner Lourenço, 2025. "Application of a hybrid fuzzy inference system to map the susceptibility to fires," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(1), pages 1117-1141, January.
    7. Matamala, Yolanda & Flores, Francisco & Arriet, Andrea & Khan, Zarrar & Feijoo, Felipe, 2023. "Probabilistic feasibility assessment of sequestration reliance for climate targets," Energy, Elsevier, vol. 272(C).
    8. Teresa Rodríguez-Espinosa & Irene Voukkali & Ana Pérez-Gimeno & María Belén Almendro Candel & J. David Hernández-Martich & Antonis A. Zorpas & Ignacio Gómez Lucas & Jose Navarro-Pedreño, 2024. "Waste as a Sustainable Source of Nutrients for Plants and Humans: A Strategy to Reduce Hidden Hunger," Sustainability, MDPI, vol. 16(16), pages 1-23, August.
    9. Feng, Jing-Chun & Sun, Liwei & Yan, Jinyue, 2023. "Carbon sequestration via shellfish farming: A potential negative emissions technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    10. Hou, Dawei & Meng, Fanhao & Ji, Chao & Xie, Li & Zhu, Wenjuan & Wang, Shizhong & Sun, Hua, 2022. "Linking food production and environmental outcomes: An application of a modified relative risk model to prioritize land-management practices," Agricultural Systems, Elsevier, vol. 196(C).
    11. Hissa, Leticia de Barros Viana & Aguiar, Ana Paula Dutra & Camargo, Rafael Rodrigues & Lima, Leticia Santos de & Gollnow, Florian & Lakes, Tobia, 2019. "Regrowing forests contribution to law compliance and carbon storage in private properties of the Brazilian Amazon," Land Use Policy, Elsevier, vol. 88(C).
    12. Fonseca Morello, Thiago, 2022. "Subsidization of mechanized tillage as an alternative to fire-based land preparation by smallholders: An economic appraisal of the case of southwestern Brazilian Amazon," Land Use Policy, Elsevier, vol. 123(C).
    13. Araujo, Rafael & Costa, Francisco J M & Garg, Teevrat, 2022. "Public Attention and Environmental Action: Evidence from Fires in the Amazon," SocArXiv xj3f6, Center for Open Science.
    14. Hugo Tameirão Seixas & Hilton Luís Ferraz da Silveira & Alan Pereira da Silva Falcão Mendes & Fabiana Da Silva Soares & Ramon Felipe Bicudo da Silva, 2025. "Conversion from Forest to Agriculture in the Brazilian Amazon from 1985 to 2021," Land, MDPI, vol. 14(2), pages 1-17, January.
    15. Pessôa, Ana Carolina M. & Morello R.S., Thiago F. & Silva-Junior, Celso H.L. & Doblas, Juan & Carvalho, Nathália S. & Aragão, Luiz E.O.C. & Anderson, Liana O., 2023. "Protected areas are effective on curbing fires in the Amazon," Ecological Economics, Elsevier, vol. 214(C).
    16. Kauano, Érico Emed & Silva, José Maria Cardoso & Diniz Filho, José Alexandre Felizola & Michalski, Fernanda, 2020. "Do protected areas hamper economic development of the Amazon region? An analysis of the relationship between protected areas and the economic growth of Brazilian Amazon municipalities," Land Use Policy, Elsevier, vol. 92(C).
    17. Adeline M. Maciel & Michelle C. A. Picoli & Lubia Vinhas & Gilberto Camara, 2020. "Identifying Land Use Change Trajectories in Brazil’s Agricultural Frontier," Land, MDPI, vol. 9(12), pages 1-16, December.
    18. repec:osf:socarx:xj3f6_v1 is not listed on IDEAS
    19. Maristela Volpato & Caio F. Andrade & Elton L. Silva & Maria L. Barbosa & Melina D. Andrade & Pedro. V. Rocha & Rafael C. Delgado & Paulo E. Teodoro & Carlos A. Silva & Marcos G. Pereira, 2023. "Fire foci and their spatiotemporal relations to weather variables and land uses in the state of Mato Grosso," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12419-12438, November.
    20. Favero, Alice & Sohngen, Brent & Hamilton, W. Parker, 2022. "Climate change and timber in Latin America: Will the forestry sector flourish under climate change?," Forest Policy and Economics, Elsevier, vol. 135(C).
    21. Zhang, Yajuan & Zhang, Lijin & Wang, Huan & Wang, Yueyao & Ding, Jiaqi & Shen, Jiashu & Wang, Zheng & Liu, Yinglu & Liang, Chenyu & Li, Shuangcheng, 2022. "Reconstructing deforestation patterns in China from 2000 to 2019," Ecological Modelling, Elsevier, vol. 465(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:9:y:2020:i:5:p:139-:d:353288. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.