Assessing Landsat Images Availability and Its Effects on Phenological Metrics

Mas, Jean-François; Araújo, Francisca Soares de

doi:10.3390/f12050574

Cited by 8 publications

(5 citation statements)

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“…From the 3 tier categories available in Landsat Collection Level‐1, Landsat Tier 1 Surface Reflectance (SR) product was the most appropriate for its high‐quality characteristics, which includes atmospherically and geometrically corrected images (Mas & Soares de Araújo, 2021; Ramsey & Mavoa, 2021; Roteta et al. , 2021).…”

Section: Methodsmentioning

confidence: 99%

“…From the 3 tier categories available in Landsat Collection Level-1, Landsat Tier 1 Surface Reflectance (SR) product was the most appropriate for its high-quality characteristics, which includes atmospherically and geometrically corrected images (Mas & Soares de Araújo, 2021;Ramsey & Mavoa, 2021;Roteta et al, 2021). To minimize the effects of clouds and cloud shadows and ensure high data quality, all Landsat images were masked by mask function (FMASK) algorithm (Zhu et al, 2015;Foga et al, 2017).…”

Section: Field Methods and Experimental Designmentioning

confidence: 99%

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Context‐dependent insect predation pressure on an avian ectoparasite

et al. 2023

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Context dependence arises when ecological relationships vary with the conditions under which they are observed. Context dependence of interactions involving parasites is poorly known, even if it is key to understanding host–parasite relationships and food web dynamics. This paper investigates to which extent predation pressure on an avian ectoparasite (Carnus hemapterus) is context‐dependent. Based on a predator‐exclusion experiment, predation pressure on C. hemapterus pupae in the host's nest for 3 years, and its variation between habitat types are quantified. Variation in precipitation and normalized difference vegetation index (NDVI) is also explored as a likely cause of context dependency. We hypothesize that predation pressure should fluctuate with such surrogates of food availability, so that inter‐annual and intra‐annual differences may emerge. The number of nests with significant reduction of pupae varied widely among years ranging from 24% to 75%. However, average pupae reduction in nests where a significant reduction occurred did not vary between years. No differences in predation rates between habitat types were detected. Precipitation and NDVI varied widely between years and NDVI was consistently lower around nests on cliffs than around nests on trees and farmhouses. Parallels were found between variation in predation pressure and precipitation/NDVI at a wide scale (highest predation the driest year, and much lower the 2 rainier ones), but not at the nest scale. This paper shows clear context‐dependent insect predation pressure on an ectoparasite under natural conditions, and that such interaction changes in signs rather than magnitude between years. The causes for these variations require longer‐term studies and/or well‐designed, large‐scale experiments.

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Section: Methodsmentioning

confidence: 99%

Section: Field Methods and Experimental Designmentioning

confidence: 99%

Context‐dependent insect predation pressure on an avian ectoparasite

et al. 2023

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“…Indices such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) have been linked to LST (Guha et al, 2019). The NDVI characterizes vegetation phenology as the difference between near-infrared and red reflectance (Wessels et al, 2011;Mas and Soares de Araújo, 2021). The following procedure (Equation 7) is used to extract the NDVI value (Townshend and Justice, 1986).…”

Section: Land Surface Temperature In Relation To Spatial Indicesmentioning

confidence: 99%

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

Mehmood

Rehman²,

Sajjad³

et al. 2023

Front. Ecol. Evol.

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While urbanization puts lots of pressure on green areas, the transition of green-to-grey surfaces under land use land cover change is directly related to increased land surface temperature–compromising livability and comfort in cities due to the heat island effect. In this context, we evaluate historical and future associations between land use land cover changes and land surface temperature in Dera Ghazi Khan–one of the top cities in Pakistan–using multi-temporal Landsat data over two decades (2002–2022). After assessing current land use changes and future predictions, their impact on land surface temperature and urban heat island effect is measured using machine learning via Multi-Layer Perceptron-Markov Chain, Artificial Neural Network and Cellular Automata. Significant changes in land use land cover were observed in the last two decades. The built-up area expanded greatly (874 ha) while agriculture land (−687 ha) and barren land (−253 ha) show decreasing trend. The water bodies were found the lowest changes (57 ha) and vegetation cover got the largest proportion in all the years. This green-grey conversion in the last two decades (8.7%) and prospect along the main corridors show the gravity of unplanned urban growth at the cost of vegetation and agricultural land (−6.8%). The land surface temperature and urban heat island effect shows a strong positive correlation between urbanization and vegetation removal. The simulation results presented in this study confirm that by 2032, the city will face a 5° C high mean temperature based on historical patterns, which could potentially lead to more challenges associated with urban heat island if no appropriate measures are taken. It is expected that due to land cover changes by 2032, ~60% of urban and peri-urban areas will experience very hot to hot temperatures (> 31.5°C). Our results provide baseline information to urban managers and planners to understand the increasing trends of land surface temperature in response to land cover changes. The study is important for urban resource management, sustainable development policies, and actions to mitigate the heat island effect. It will further asset the broader audience to understand the impact of land use land cover changes on the land surface temperature and urban heat island effect in the light of historic pattern and machine learning approach.

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“…Landsat-7/8 and Sentinel-2A/B Data Landsat-7 and 8 were successfully launched in 1999 and 2013, respectively, as extensions and continuations of the previous generation of Landsat satellites [57], carrying the Enhanced Thematic Mapper Plus (ETM+), the Operational Land Imager (OLI), and the Thermal Infrared Sensor (TIS). They rotate around the Earth in a Sun-synchronous nearpolar orbit and repeat observations every 16 days, providing a wide range of continuous land surface observations at a spatial resolution of 30 m [58], which can further contribute to understanding the Earth's system and its response to natural and anthropogenic changes and improve our ability to predict climate and natural disasters [59]. Sentinel-2A/B was successfully launched in 2015 and 2017, respectively, offering improved spatial, temporal, and spectral resolution [60].…”

Section: Data 221 Near-surface Remote Sensing Data-phenocam Datasetmentioning

confidence: 99%

Mapping Phenology of Complicated Wetland Landscapes through Harmonizing Landsat and Sentinel-2 Imagery

et al. 2023

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Wetlands are important CO2 sinks and methane sources, and their seasonality and phenological cycle play an essential role in understanding the carbon budget. However, given the spatial heterogeneity of wetland landscapes and the coarser spatial resolution of satellites, the phenological retrievals of wetlands are challenging. Here we examined the phenology of wetlands from 30 m harmonized Landsat/Sentinel-2 (LandSent30) and 500 m MODIS satellite observations using the ground phenology network PhenoCam as a benchmark. This study used all 11 available wetland PhenoCam sites (about 30 site years), covering diverse wetland types from different climate zones. We found that the LandSent30-based phenology results were in overall higher consistency with the PhenoCam results compared to MODIS, which could be related to the better explanation capacity of LandSent30 data in the heterogeneous landscapes of wetlands. This also means that the LandSent30 has an advantage over the 500 m MODIS regarding wetland vegetation phenological retrievals. It should be noted that the LandSent30 did not show a greatly improved performance, which could be related to the specificity and complexity of the wetlands landscape. We also illustrated the potential effects of the location and observation direction of PhenoCam cameras, the selection of Region of Interest (ROI), as well as the landscape composition of the site. Overall, this study highlights the complexity of wetland phenology from both ground and remote sensing observations at different scales, which paves the road for understanding the role of wetlands in global climate change and provides a basis for understanding the real phenological changes of wetland surfaces.

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Assessing Landsat Images Availability and Its Effects on Phenological Metrics

Cited by 8 publications

References 50 publications

Context‐dependent insect predation pressure on an avian ectoparasite

Context‐dependent insect predation pressure on an avian ectoparasite

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

Mapping Phenology of Complicated Wetland Landscapes through Harmonizing Landsat and Sentinel-2 Imagery

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