Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia
Remote sensing (RS) is a compulsory component in studying and monitoring ecosystems suffering from the disruption of natural balance, productivity, and degradation. The current study attempted to assess the feasibility of multisource RS for assessing and monitoring mountainous natural grasslands in...
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2024-11-01
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| author | Grigor Ayvazyan Vahagn Muradyan Andrey Medvedev Anahit Khlghatyan Shushanik Asmaryan |
| author_facet | Grigor Ayvazyan Vahagn Muradyan Andrey Medvedev Anahit Khlghatyan Shushanik Asmaryan |
| author_sort | Grigor Ayvazyan |
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| description | Remote sensing (RS) is a compulsory component in studying and monitoring ecosystems suffering from the disruption of natural balance, productivity, and degradation. The current study attempted to assess the feasibility of multisource RS for assessing and monitoring mountainous natural grasslands in Armenia. Different spatial resolution RS data (Landsat 8, Sentinel-2, Planet Scope, and multispectral UAV) were used to obtain various vegetation spectral indices: NDVI, NDWI, GNDVI, GLI, EVI, DVI, SAVI, MSAVI, and GSAVI, and the relationships among the indices were assessed via the Spearman correlation method, which showed a significant positive correlation for all cases (<i>p</i> < 0.01). A comparison of all indices showed a significant high correlation between UAV and the Planet Scope imagery. The comparisons of UAV with Sentinel and Landsat data show moderate and low significant correlation (<i>p</i> < 0.01), correspondingly. Also, trend analysis was performed to explore the spatial–temporal changes of these indices using Mann–Kendall statistical tests (MK, MKKH, MKKY, PW, TFPW), which indicated no significant trend. However, Sen’s slope as a second estimator showed a decreasing trend. Generally, it could be proved that, as opensource data, Sentinel-2 seemed to have better alignment, making it a reliable tool for the accurate monitoring of the ecological state of small mountainous grasslands. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-7846c873f1ba4752a5f175ae7cc9fc8d2024-11-26T17:47:52ZengMDPI AGApplied Sciences2076-34172024-11-0114221020510.3390/app142210205Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in ArmeniaGrigor Ayvazyan0Vahagn Muradyan1Andrey Medvedev2Anahit Khlghatyan3Shushanik Asmaryan4Center for Ecological—Noosphere Studies NAS RA, GIS & Remote Sensing Department, Abovyan Str. 68, Yerevan 0025, ArmeniaCenter for Ecological—Noosphere Studies NAS RA, GIS & Remote Sensing Department, Abovyan Str. 68, Yerevan 0025, ArmeniaCenter for Ecological—Noosphere Studies NAS RA, GIS & Remote Sensing Department, Abovyan Str. 68, Yerevan 0025, ArmeniaCenter for Ecological—Noosphere Studies NAS RA, GIS & Remote Sensing Department, Abovyan Str. 68, Yerevan 0025, ArmeniaCenter for Ecological—Noosphere Studies NAS RA, GIS & Remote Sensing Department, Abovyan Str. 68, Yerevan 0025, ArmeniaRemote sensing (RS) is a compulsory component in studying and monitoring ecosystems suffering from the disruption of natural balance, productivity, and degradation. The current study attempted to assess the feasibility of multisource RS for assessing and monitoring mountainous natural grasslands in Armenia. Different spatial resolution RS data (Landsat 8, Sentinel-2, Planet Scope, and multispectral UAV) were used to obtain various vegetation spectral indices: NDVI, NDWI, GNDVI, GLI, EVI, DVI, SAVI, MSAVI, and GSAVI, and the relationships among the indices were assessed via the Spearman correlation method, which showed a significant positive correlation for all cases (<i>p</i> < 0.01). A comparison of all indices showed a significant high correlation between UAV and the Planet Scope imagery. The comparisons of UAV with Sentinel and Landsat data show moderate and low significant correlation (<i>p</i> < 0.01), correspondingly. Also, trend analysis was performed to explore the spatial–temporal changes of these indices using Mann–Kendall statistical tests (MK, MKKH, MKKY, PW, TFPW), which indicated no significant trend. However, Sen’s slope as a second estimator showed a decreasing trend. Generally, it could be proved that, as opensource data, Sentinel-2 seemed to have better alignment, making it a reliable tool for the accurate monitoring of the ecological state of small mountainous grasslands.https://www.mdpi.com/2076-3417/14/22/10205multisource remote sensingmountainous grasslandsUAV datatrend analysisshort-term data setsvegetation indices |
| spellingShingle | Grigor Ayvazyan Vahagn Muradyan Andrey Medvedev Anahit Khlghatyan Shushanik Asmaryan Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia Applied Sciences multisource remote sensing mountainous grasslands UAV data trend analysis short-term data sets vegetation indices |
| title | Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia |
| title_full | Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia |
| title_fullStr | Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia |
| title_full_unstemmed | Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia |
| title_short | Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia |
| title_sort | exploring multisource remote sensing for assessing and monitoring the ecological state of the mountainous natural grasslands in armenia |
| topic | multisource remote sensing mountainous grasslands UAV data trend analysis short-term data sets vegetation indices |
| url | https://www.mdpi.com/2076-3417/14/22/10205 |
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