Monitoring the changes of vegetation phenological cycles in Ahvaz city using remote sensing images

Document Type : Original Article

Authors

1 1. MSc. Graduate, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

2 Assistant professor, University of Tehran, Faculty of Geography, Department of Remote Sensing and GIS, Tehran, Iran

Abstract

Introduction: Plant phenology plays an important role in plant ecosystems and serves as a key indicator of ecological changes. With the expansion of urbanization, urban green spaces have become increasingly important in residential areas. On the other hand, The use of plants in urban settings and the green space services they provide have garnered significant attention in recent studies. The value of urban green spaces has been recognized for their numerous benefits to human health and the ecological environment of cities. Therefore, it is essential to study and monitor the phenological cycles of plants in urban areas at various spatial-temporal scales, considering their pivotal role in the urban ecosystem and society health.
Materials and methods: This study utilized the widely used NDVI and EVI indices calculated from Landsat satellite OLI sensor and MOD13Q1 product of MODIS sensor images to investigate the plant phenology cycle in the Ahvaz metropolitan area from 2015 to December 2019. Satellite images were retrieved and processed using the Google Earth Engine platform. The phenological cycle of plants was obtained based on the vegetation indices, categorized according to vegetation type and compared with the phenological cycle obtained from the ground survey data. Due to probability noise and pixels with spectral mixing, a Savitzky-Golay filter was applied to smooth the phenological cycle of plants.
Results and discussion: The results indicate the increasing trend in the values of both NDVI and EVI indices annually, with a rise of 0.03 and 0.04 in the OLI sensor and 0.01 in the MOD13Q1 product, respectively. These positive changes were particularly noticeable in January, March, October, November, and December for both sensors. Variations in plant phenology phases were observed between the two sensors, with the most significant differences occurring in 2018 and 2019. This shows that under favorable weather conditions, there is an increase in plant chlorophyll content, leading to disparities between the results of the two sensors. The transition periods of the growing season identified by the OLI sensor exhibited more detail compared to the MODIS medium resolution dataset. Although the MODIS sensor indicated an earlier start to the growing season than the OLI sensor, the shape of the phenological cycle curves from both sensors appeared similar despite discrepancies in their start and end dates.
Generally, according to the MODIS product, the duration of the growing season (between mid-winter and early summer) is approximately four months. These disparities point to more changes in vegetation that can be better detected using high-resolution images compared to sensors with medium and low spatial resolution. Overall, significant changes in the phenological cycle of plants were observed at the urban area level, reflecting various ground phenomena that contribute to increased heterogeneity in satellite sensor image pixels.
Conclusion: The smallest difference between the periods of the growing season of plants observed by ground observations in OLI and MODIS sensors was 7 and 10 days for the Start of Growing Season (SOS), respectively. The largest difference was noted at the peak of the growing season, with disparities of 20 and 35 days, and for the End of Growing Season (EOS), 20 days later and 20 days earlier, respectively, based on ground observations. However, the length of the growing season (LOS) in the OLI sensor is approximately five months, indicating closer alignment with ground observations. This divergence is attributed to increased heterogeneous conditions in the target phenomena and/or the spatial resolution of MODIS sensor images.
it is evident that the results obtained from the OLI sensor enhance our understanding of human interactions with the natural environment in urban areas. Addressing these findings in future studies can help mitigate environmental challenges and provide more accurate information for planning purposes.

Keywords

References

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