IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i1p180-d1026302.html
   My bibliography  Save this article

Spatial Dynamics of the Shore Coverage within the Zone of Influence of the Chambo River, Central Ecuador

Author

Listed:
  • Julie Echeverría-Puertas

    (Escuela Superior Politécnica de Chimborazo (ESPOCH), Grupo de Investigación y Desarrollo para el Ambiente y Cambio Climático (GIDAC), 060101 Riobamba, Ecuador)

  • Magdy Echeverría

    (Escuela Superior Politécnica de Chimborazo (ESPOCH), 060101 Riobamba, Ecuador)

  • Franklin Cargua

    (Escuela Superior Politécnica de Chimborazo (ESPOCH), Grupo de Investigación y Desarrollo para el Ambiente y Cambio Climático (GIDAC), 060101 Riobamba, Ecuador)

  • Theofilos Toulkeridis

    (Departamento de Ciencias de la Tierra y de la Construcción, Universidad de las Fuerzas Armadas ESPE, Sangolquí 171103, Ecuador)

Abstract

The predominant aim of the current study was to evaluate the spatial dynamics of the riparian coverage of the area of influence of the Chambo River in the area of the river’s source (middle-high basin), between 2500 and 3000 m.a.s.l. For its execution, Landsat 7 images from the year 2000, RapidEye from the year 2009, and Spot 6 from the year 2019 were used in the time range of 2000–2009 and 2009–2019. These were subjected to supervised classification by applying the maximum likelihood algorithm, identifying five classes of soil cover, being pasture, crops, soil-remnants of paramo, forest, and anthropic. The classification results were validated by calculating the precision measures and the kappa index. With the use of cross-tabulation matrices, the gains, losses, and persistence in the two periods studied were identified. There, it was determined that, in the first study period, the soil cover-paramo remnants presented the highest percentage of loss (26.70%), the crop cover the highest percentage of gain (28.91%), and in the second period, the crop class presented the highest percentages of losses (18.94%) and gains (17.29%). The cartographic projection of the area for the year 2030 predicts that the areas anthropic category will increase by 1.27%, that of forest will decrease by 1.19%, that of soil-remnants of paramo will gain 0.79%, and crop and pasture cover will decrease by 0.45% and 0.43%, respectively. The results obtained allow for the transitions between coverages to be attributed to population growth, afforestation, reforestation, deforestation and agricultural activities, volcanic eruptions, land colonization, and expansion of agricultural activity. Complementary studies are recommended that involve livelihoods and water quality, which facilitate the identification of vulnerable areas to propose adaptation, prevention, and/or restoration measures.

Suggested Citation

  • Julie Echeverría-Puertas & Magdy Echeverría & Franklin Cargua & Theofilos Toulkeridis, 2023. "Spatial Dynamics of the Shore Coverage within the Zone of Influence of the Chambo River, Central Ecuador," Land, MDPI, vol. 12(1), pages 1-21, January.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:1:p:180-:d:1026302
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/1/180/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/12/1/180/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rizwan Muhammad & Wenyin Zhang & Zaheer Abbas & Feng Guo & Luc Gwiazdzinski, 2022. "Spatiotemporal Change Analysis and Prediction of Future Land Use and Land Cover Changes Using QGIS MOLUSCE Plugin and Remote Sensing Big Data: A Case Study of Linyi, China," Land, MDPI, vol. 11(3), pages 1-24, March.
    2. Yadira Pazmiño & José Juan de Felipe & Marc Vallbé & Franklin Cargua & Luis Quevedo, 2021. "Identification of a Set of Variables for the Classification of Páramo Soils Using a Nonparametric Model, Remote Sensing, and Organic Carbon," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. David Vinicio Carrera-Villacrés & Fabián Rodríguez-Espinosa & Theofilos Toulkeridis, 2023. "Potential Solutions for the Water Shortage Using Towers of Fog Collectors in a High Andean Community in Central Ecuador," Sustainability, MDPI, vol. 15(12), pages 1-15, June.

    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. SrinivasaPerumal Padma & Sivakumar Vidhya Lakshmi & Ramaiah Prakash & Sundaresan Srividhya & Aburpa Avanachari Sivakumar & Nagarajan Divyah & Cristian Canales & Erick I. Saavedra Flores, 2022. "Simulation of Land Use/Land Cover Dynamics Using Google Earth Data and QGIS: A Case Study on Outer Ring Road, Southern India," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    2. Ram Avtar & Apisai Vakacegu Rinamalo & Deha Agus Umarhadi & Ankita Gupta & Khaled Mohamed Khedher & Ali P. Yunus & Bhupendra P. Singh & Pankaj Kumar & Netrananda Sahu & Anjar Dimara Sakti, 2022. "Land Use Change and Prediction for Valuating Carbon Sequestration in Viti Levu Island, Fiji," Land, MDPI, vol. 11(8), pages 1-17, August.
    3. Sylwia Barwicka & Małgorzata Milecka, 2022. "The “Perfect Village” Model as a Result of Research on Transformation of Plant Cover—Case Study of the Puchaczów Commune," Sustainability, MDPI, vol. 14(21), pages 1-22, November.
    4. Chunliu Gao & Deqiang Cheng & Javed Iqbal & Shunyu Yao, 2023. "Spatiotemporal Change Analysis and Prediction of the Great Yellow River Region (GYRR) Land Cover and the Relationship Analysis with Mountain Hazards," Land, MDPI, vol. 12(2), pages 1-24, January.
    5. Jessica Strzempko & Robert Gilmore Pontius, 2023. "The Flow Matrix Offers a Straightforward Alternative to the Problematic Markov Matrix," Land, MDPI, vol. 12(7), pages 1-18, July.
    6. Md Shihab Uddin & Badal Mahalder & Debabrata Mahalder, 2023. "Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Gazipur City Corporation, Bangladesh," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    7. Saulo Folharini & António Vieira & António Bento-Gonçalves & Sara Silva & Tiago Marques & Jorge Novais, 2023. "A Framework Using Open-Source Software for Land Use Prediction and Climate Data Time Series Analysis in a Protected Area of Portugal: Alvão Natural Park," Land, MDPI, vol. 12(7), pages 1-16, June.
    8. Nivin Abdelrahim Hasan & Dongkai Yang & Fayha Al-Shibli, 2023. "A Historical–Projected Analysis in Land Use/Land Cover in Developing Arid Region Using Spatial Differences and Its Relation to the Climate," Sustainability, MDPI, vol. 15(3), pages 1-24, February.

    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:12:y:2023:i:1:p:180-:d:1026302. 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.