Earthquakes often cause destructive and unpredictable changes that can affect local hydrology (e.g. groundwater elevation or reduction) and thus disrupt land uses and human activities. Prolific agricultural regions overlie seismically active areas, emphasizing the importance to improve our understanding and monitoring of hydrologic and agricultural systems following a seismic event. A thorough data collection is necessary for adequate post-earthquake crop management response; however, the large spatial extent of earthquake's impact makes challenging the collection of robust data sets for identifying locations and magnitude of these impacts. Observing hydrologic responses to earthquakes is not a novel concept, yet there is a lack of methods and tools for assessing earthquake's impacts upon the regional hydrology and agricultural systems. The objective of this paper is to describe how remote sensing imagery, methods and tools allow detecting crop responses and damage incurred after earthquakes because a change in the regional hydrology. Many remote sensing datasets are long archived with extensive coverage and with well-documented methods to assess plant-water relations. We thus connect remote sensing of plant water relations to its utility in agriculture using a post-earthquake agrohydrologic remote sensing (PEARS) framework; specifically in agro-hydrologic relationships associated with recent earthquake events that will lead to improved water management.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.scitotenv.2016.05.146 | DOI Listing |
Ying Yong Sheng Tai Xue Bao
October 2024
Ningxia Helan Mountain National Nature Reserve Administration, Yinchuan 750021, China.
subsp. is an important resource plant with considerable medicinal, economic, and ecological value, and an indicator species in the transition zones between forests and grasslands. Predicting the potential geographic distribution of subsp.
View Article and Find Full Text PDFYing Yong Sheng Tai Xue Bao
October 2024
School of Resources and Environmental Engineering, Ludong University, Yantai 264025, Shandong, China.
Accurately capturing the spatiotemporal dynamics of regional forest cover and its response to climate change is of great significance for forest resource management and ecological environment protection. We used statistical methods such us linear regression and correlation analysis, as well as remote sensing change monitoring to investigate the spatiotemporal dynamics of forest cover and its response to climate change from 2000 to 2022 in Shandong Province based on MODIS VCF products and meteorological data. The results showed that the forest co-verage and forest area in Shandong Province increased from 43.
View Article and Find Full Text PDFYing Yong Sheng Tai Xue Bao
October 2024
Research Center for UAV Remote Sensing, College of Life Sciences, Shaanxi Normal University, Xi'an 710119, China.
Niche conservatism is essential for predicting the risk of alien species invasions. Currently, the changes of climate niche of during its invasion in China are still not clear. Using principal component analysis, we examined the climate niche shifts of during its invasion and analyzed its potential distribution in China.
View Article and Find Full Text PDFGlob Chang Biol
December 2024
Faculdade de Ciências, Instituto Dom Luiz, Universidade de Lisboa, Lisbon, Portugal.
In fire-prone regions such as the Mediterranean biome, fire seasons are becoming longer, and fires are becoming more frequent and severe. Post-fire recovery dynamics is a key component of ecosystem resilience and stability. Even though Mediterranean ecosystems can tolerate high exposure to extreme temperatures and recover from fire, changes in climate conditions and fire intensity or frequency might contribute to loss of ecosystem resilience and increase the potential for irreversible changes in vegetation communities.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
Department of Forest, Environment, and Climate Change, Chilika Development Authority, Barkul, Odisha, India.
Chlorophyll-a (Chla) is recognized as a key indicator of water quality and ecological health in aquatic ecosystems, offering valuable insights into ecosystem dynamics and changes over time. This study aimed to to develop and validate a robust ML model for estimating Chla using Landsat data, produce a time series of Chl a maps, and analyze the spatiotemporal variability of Chla in Chilika Lagoon, Asia's largest brackish water lagoon. Nine ML regression models, including Extreme Gradient Boost, Support Vector Regression, Random Forest, and Bagging Regression, were evaluated using Landsat imagery and field data.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!