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  • Open Access

    ARTICLE

    Monitoring Vegetation Cover Changes in a Rapidly Urbanizing Region: A Case Study in Da Nang City, Vietnam

    Vu Thi Phuong1, Bui Bao Thien2,*

    Revue Internationale de Géomatique, Vol.34, pp. 151-168, 2025, DOI:10.32604/rig.2025.062829 - 21 March 2025

    Abstract Vegetation is crucial to ecosystems, thus, detecting and assessing changes in vegetation cover are receiving increasing attention. In this study, we combine remote sensing data and geographic information systems to assess vegetation cover changes in Da Nang city, Vietnam, between 1988 and 2022. Remote sensing images for the years 1988, 2000, and 2010 were obtained from Landsat 5-TM satellite data, and imagery for 2022 was obtained from Landsat 9-OLI/TIRS satellite data. In each satellite scene, we used supervised classification and spectral indices (NDWI—Normalized Difference Water Index, NDVI—Normalized Difference Vegetation Index, and SAVI—Soil Adjusted Vegetation Index) More >

  • Open Access

    ARTICLE

    Fuzzy Machine Learning-Based Algorithms for Mapping Cumin and Fennel Spices Crop Fields Using Sentinel-2 Satellite Data

    Shilpa Suman1, Abhishek Rawat2,*, Anil Kumar3, S. K. Tiwari4

    Revue Internationale de Géomatique, Vol.33, pp. 363-381, 2024, DOI:10.32604/rig.2024.053981 - 18 September 2024

    Abstract In this study, the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means (PCM) and Noise Clustering (NC) classifiers were examined and mapped the cumin and fennel rabi crop. Two training sample selection approaches that have been investigated in this study are “mean” and “individual sample as mean”. Both training sample techniques were applied to the PCM and NC classifiers to classify the two indices approach. Both approaches have been studied to decrease spectral information in temporal data processing. The Modified Soil Adjusted Vegetation Index 2 (MSAVI-2) and Class-Based Sensor… More >

  • Open Access

    ARTICLE

    Improving the Accuracy of Vegetation Index Retrieval for Biomass by Combining Ground-UAV Hyperspectral Data–A New Method for Inner Mongolia Typical Grasslands

    Ruochen Wang1,#, Jianjun Dong2,#, Lishan Jin3, Yuyan Sun3, Taogetao Baoyin2, Xiumei Wang*

    Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 387-411, 2024, DOI:10.32604/phyton.2024.047573 - 27 February 2024

    Abstract Grassland biomass is an important parameter of grassland ecosystems. The complexity of the grassland canopy vegetation spectrum makes the long-term assessment of grassland growth a challenge. Few studies have explored the original spectral information of typical grasslands in Inner Mongolia and examined the influence of spectral information on aboveground biomass (AGB) estimation. In order to improve the accuracy of vegetation index inversion of grassland AGB, this study combined ground and Unmanned Aerial Vehicle (UAV) remote sensing technology and screened sensitive bands through ground hyperspectral data transformation and correlation analysis. The narrow band vegetation indices were… More >

  • Open Access

    ARTICLE

    A Hybrid Approach of TLBO and EBPNN for Crop Yield Prediction Using Spatial Feature Vectors

    Preeti Tiwari1, *, Piyush Shukla1

    Journal on Artificial Intelligence, Vol.1, No.2, pp. 45-58, 2019, DOI:10.32604/jai.2019.04444

    Abstract The prediction of crop yield is one of the important factor and also challenging, to predict the future crop yield based on various criteria’s. Many advanced technologies are incorporated in the agricultural processes, which enhances the crop yield production efficiency. The process of predicting the crop yield can be done by taking agriculture data, which helps to analyze and make important decisions before and during cultivation. This paper focuses on the prediction of crop yield, where two models of machine learning are developed for this work. One is Modified Convolutional Neural Network (MCNN), and the… More >

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