Retraction Note: Agricultural climate change based on remote sensing images and fashion design innovation

The integration of multi-source, multi-temporal, multi-band optical, and radar remote sensing images to accurately detect, extract, and monitor the long-term dynamic change of coastline is critical for a better understanding of how the coastal environment responds to climate change and human activities. In this study, we present a combination method to produce the spatiotemporal changes of the coastline in the Yellow River Delta (YRD) in 1980–2020 with both optical and Synthetic Aperture Radar (SAR) satellite remote sensing images. According to the measurement results of GPS RTK, this method can obtain a high accuracy of shoreline extraction, with an observation error of 71.4% within one pixel of the image. Then, the influence of annual water discharge and sediment load on the changes of the coastline is investigated. The results show that there are two significant accretion areas in the Qing 8 and Qingshuigou course. The relative high correlation illustrates that the sediment discharge has a great contribution to the change of estuary area. Human activities, climate change, and sea level rise that affect waves and storm surges are also important drivers of coastal morphology to be investigated in the future, in addition to the sediment transport.

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Mapping present-day mountain treeline pattern based on high-resolution remote sensing images

10.5194/egusphere-egu21-15731 ◽

2021 ◽

Author(s):

Xinyue He ◽

Zhenzhong Zeng ◽

Dominick Spracklen ◽

Joseph Holden ◽

Alan D. Ziegler

Keyword(s):

Climate Change ◽

Remote Sensing ◽

High Resolution ◽

Vegetation Index ◽

River Flow ◽

Google Earth ◽

Tree Line ◽

Mountain Forests ◽

Remote Sensing Images ◽

The World

<p>Mountain forests, widely distributed around the world, are hotspots of biodiversity and provide important environmental services by conserving water and soil, regulating river flow and storing carbon. The upper altitudinal limits of trees is defined as the treeline. Some field investigations indicate that treelines around the world are moving upward as a response to global climate change. However, to date, a high-resolution spatial map of global mountain treeline position is still lacking. In this study, we develop an algorithm to detect the present-day tree line positions in mountain regions globally, via integrating a high-resolution tree distribution dataset with a high-resolution digital elevation model. The results are validated with even finer resolution remote sensing images in Google Earth. We analyse a range of climate datasets to understand important climate drivers of the present-day tree line position. Further, we explore the change in Normalized Difference Vegetation Index (NDVI) within the buffer zone of the treeline to determine how the treeline position has shifted in the last three decades. By providing the first global mountain treeline distribution, our analysis will help to reveal how mountain forests are responding to climate change globally, and to detect how the responses vary regionally.</p>

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