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Chapter 1 Scientific Basis of Global Climate Change

vegetation indices such as the Normalized Difference Vegetation Index (NDVI) through visi-
ble and near-infrared bands, satellites are able toclearly demonstrate the growth dynamics of
vegetation in different regions. In arid regions, satellite monitoring has revealed a year-by-
year decline in vegetation coverage, which is closely correlated with the climatic trends of
reduced precipitation and rising temperatures in these regions, further revealing the impacts
of climate change on ecosystems.
As an emerging force, drone monitoring technology has demonstrated unique advan-
tages in climate change research. Compared to satellite remote sensing, drones offer higher
flexibility and resolution. They can fly at low altitudes to conduct detailed observations of
specific areas, compensating for satellite remote sensing’s limitations in monitoring localized
details. In glacier research, drones can approach glacier surfaces to capture high-resolution
images, obtaining information about glacial morphology, textures, and crack distributions.
Through comparative analysis of glacier images captured by drones during different peri-
ods, scientists can precisely calculate parameters such as glacial retreat rates and ice volume
changes, providing critical data for studying the impacts of global climate change on gla-
ciers. In wetland ecosystem research, drones can traverse wetlands at low altitudes to moni-
tor vegetation species, distributions, and water level variations. Wetlands serve as crucial in-
dicators of climate change, and the stability of their ecosystems holds significant importance
for climate regulation and biodiversity maintenance. Data acquired by drones helps scientists
gain deeper insights into the evolutionary mechanisms of wetland ecosystems under climate
change, offering scientific foundations for wetland conservation and management.
Additionally, several other emerging technologies are also playing active roles in cli-
mate change research. For instance, sensor network technology deploys numerous sensors
across different geographical locations to collect real-time meteorological data such as
temperature, humidity, air pressure, and wind speed. These sensors form a vast monitoring
network, enabling high-density environmental monitoring of localized climate conditions.
Compared to traditional weather stations, sensor networks offer higher spatial resolution and
broader coverage, capturing climate variation characteristics at small scales. In urban areas,
sensor networks monitor the specific distribution and intensity changes of urban heat island
effects, providing data support for urban planning and heat island mitigation strategies. The
application of big data and artificial intelligence technologies has also introduced novel ap-
proaches to climate change research. Big data technology integrates and processes massive
climate-related datasets, including satellite remote sensing data, ground monitoring data,
and historical climate records, enhancing data processing efficiency and analytical precision.
Artificial intelligence algorithms perform pattern recognition and predictive analysis on
these complex datasets, such as using machine learning algorithms to forecast future climate
trends and assess regional climate change impacts. These emerging technologies synergize
and complement one another, opening expansive prospects for climate change research and
significantly advancing the field.

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