Version 1
: Received: 7 September 2024 / Approved: 9 September 2024 / Online: 9 September 2024 (13:48:58 CEST)
How to cite:
Ayvazyan, G.; Muradyan, V.; Medvedev, A.; Khlghatyan, A.; Asmaryan, S. Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia. Preprints2024, 2024090674. https://doi.org/10.20944/preprints202409.0674.v1
Ayvazyan, G.; Muradyan, V.; Medvedev, A.; Khlghatyan, A.; Asmaryan, S. Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia. Preprints 2024, 2024090674. https://doi.org/10.20944/preprints202409.0674.v1
Ayvazyan, G.; Muradyan, V.; Medvedev, A.; Khlghatyan, A.; Asmaryan, S. Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia. Preprints2024, 2024090674. https://doi.org/10.20944/preprints202409.0674.v1
APA Style
Ayvazyan, G., Muradyan, V., Medvedev, A., Khlghatyan, A., & Asmaryan, S. (2024). Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia. Preprints. https://doi.org/10.20944/preprints202409.0674.v1
Chicago/Turabian Style
Ayvazyan, G., Anahit Khlghatyan and Shushanik Asmaryan. 2024 "Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia" Preprints. https://doi.org/10.20944/preprints202409.0674.v1
Abstract
Remote sensing (RS) is a compulsory component in studying and monitoring ecosystems suffering from the disruption of natural balance, productivity, and degradation. Current study attempted to assess the feasibility of multisource RS for assessing and monitoring mountainous natural grasslands in Armenia. Series of the different spatial resolution RS data (Landsat 8, Sentinel 2, Planet Scope, and multispectral UAV data) were used to obtain various vegetation spectral indices: NDVI, NDWI, GNDVI, GLI, EVI, DVI, SAVI, MSAVI, and GSAVI, and the relationship among the indices were assessed via Spearman correlation method, which shows a significant positive correlation for all cases (p < 0.01). A comparison of all indices shows a significant high correlation between UAV and the PlanetScope imagery. The comparison of UAV, Sentinel and Landsat data show moderate and low significant correlation (p < 0.01) correspondingly. Also, trend analysis was performed to explore their spatial-temporal changes using Mann-Kendall statistical tests (MK, MKKH, MKKY, PW, TFPW), which indicated no significant trend. However, Sen’s slope as second estimator shows decreasing trend. Though the best result received for PlanetScope and Sentinel 2, Sentinel-2 data seem to have better alignment making it a reliable tool for an accurate monitoring of small mountainous grasslands in Armenia.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
