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  • Cartography and geographic base data  (2)
  • 1
    Online Resource
    Online Resource
    GOOGLE EARTH ENGINE FOR LANDSAT IMAGE PROCESSING AND ASSESSING LULC CLASSIFICATION IN SOUTHWESTERN CÔTE D’IVOIRE
    Vilnius Gediminas Technical University ; 2023
    In:  Geodesy and cartography Vol. 49, No. 1 ( 2023-03-13), p. 37-50
    Details
    In: Geodesy and cartography, Vilnius Gediminas Technical University, Vol. 49, No. 1 ( 2023-03-13), p. 37-50
    Abstract: High-accuracy land use and land cover maps (LULC) are increasingly in demand for environmental management and decision-making. Despite the limitation, Machine learning classifiers (MLC) fill the gap in any complex issue related to LULC data accuracy. Visualizing land-cover information is critical in mitigating Côte d’Ivoire’s deforestation and land use planning using the Google Earth Engine (GEE) software. This paper estimates the probability of RF classification in South Western Côte d’Ivoire. Landsat 8 Surface Reflectance Tiers 1 (L8OLI/TIRS) data with a resolution of 30 mn for 2020 were used to classify the western and southwestern Forest areas of Côte d’Ivoire. The Random Forest (RF) learning classifier was calibrated using 80% training data and 20% testing data to assess GEE classification accuracy performance. The findings indicate that the Forest land class accounts for 39.48% of the entire study area, followed by the Bareland class, the Cultivated land class 21.28±0.90%, the Water class 1.94±0.27%, and the 0.96±0.60% Urban class respectively. The classification reliability test results show that 99.85%±1.95 is the overall training accuracy (OTA), and 99.81±1.95% for the training kappa (TK). The overall validation accuracy (VOA) is 94.02±1.90%, while 92.25±1.88% validation kappa (VK) and 92.45±1.88% RF Accuracy. The different coefficients classification accuracy results obtained from the RF confusion matrix indicate that each class has three good performances. This is due to the cultivated land samples lower spatial resolution and smaller sample numbers, resulting in a lower PA for this class than for the other classes. All had producer accuracy (PA) and user accuracy (UA) more than 90% using the L8OLI/TIRS data. Using the RF-based classification method integrated into the GEE provides an efficient and high scores accuracy for classifying land use and land cover in the study area.
    Type of Medium: Online Resource
    ISSN: 2029-6991 , 2029-7009
    URL: Article
    DOI: 10.3846/gac.2023.16805
    Language: Unknown
    Publisher: Vilnius Gediminas Technical University
    Publication Date: 2023
    detail.hit.zdb_id: 2737682-5
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  • 2
    Online Resource
    Online Resource
    Spatiotemporal Change in Livestock Population and Its Correlation with Meteorological Disasters during 2000–2020 across Inner Mongolia
    MDPI AG ; 2022
    In:  ISPRS International Journal of Geo-Information Vol. 11, No. 10 ( 2022-10-16), p. 520-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 11, No. 10 ( 2022-10-16), p. 520-
    Abstract: Inner Mongolia (IM) is one of the five major pastoral areas in China, and animal husbandry is its traditional industry. The population of livestock is an important factor affecting the sustainable development of livestock and grassland. Due to the special geographical location of IM, various meteorological disasters occur frequently, which have a significant impact on the local livestock population. In this study, principal component analysis (PCA) and geographically weighted principal component analysis (GWPCA) were used to explore the spatial and temporal patterns of small livestock and large livestock populations in county-level administrative units from 2000 to 2020, and the effects of meteorological disasters on livestock populations were also considered. We found that the cumulative proportion of total variance (CPTV) of the first two principal components of global PCA for small livestock and the first principal component for large livestock reached 94.54% and 91.98%, respectively, while the CPTV of GWPCA was in the range of 93.23–96.45% and 88.47–92.49%, respectively, which showed stronger spatial explanation; the small livestock population was significantly correlated with spring drought, summer drought, spring–summer drought and snow disaster. However, the correlation between large livestock and summer drought and spring–summer drought is greater. We conclude that GWPCA can better explain the spatial change of livestock populations; meteorological disasters have both advantages and disadvantages on the livestock population, and the drought types that have a greater impact on livestock are summer drought and spring–summer drought. There are geographical differences in the impact of meteorological disasters, with drought affecting most of IM and snow disaster mainly affecting the eastern region; large livestock were mainly affected by drought, while small livestock were affected by both drought and snow disaster.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi11100520
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2655790-3
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