Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators

Abstract. The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation–anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rain-fed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products' ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.

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The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

10.5194/hess-2016-671 ◽

2017 ◽

Cited By ~ 1

Author(s):

Clara Linés ◽

Micha Werner ◽

Wim Bastiaanssen

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Data Sets ◽

Drought Management ◽

Ebro Basin ◽

Sensing Data ◽

Drought Impacts ◽

Management Plans ◽

Drought Indicators ◽

Selection Of

Abstract. The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor their evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale, and how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origin and measurement frequency are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to some 6 months before impacts are reported in newspapers, with the best correlation-anticipation relationships for the Standard Precipitation Index (SPI), the Normalized Difference Vegetation Index (NDVI) and Evapotranspiration (ET). Soil Moisture (SM) and Land Surface Temperature (LST) offer also good anticipation, but with weaker correlations, while Gross Primary Production (GPP) presents moderate positive correlations only for some of the rainfed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rainfed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products’ ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.

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A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

Remote Sensing ◽

10.3390/rs13122333 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2333

Author(s):

Lilu Zhu ◽

Xiaolu Su ◽

Yanfeng Hu ◽

Xianqing Tai ◽

Kun Fu

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Temporal Correlation ◽

Local Association ◽

Sensing Data ◽

Multi Scale ◽

Correlation Networks ◽

Efficient Integration ◽

Spatio Temporal ◽

Query Algorithm

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

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Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

Remote Sensing ◽

10.3390/rs13061131 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1131

Author(s):

Tao Yu ◽

Pengju Liu ◽

Qiang Zhang ◽

Yi Ren ◽

Jingning Yao

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Net Primary Production ◽

Remote Sensing Data ◽

Forest Degradation ◽

Observation Data ◽

Area Index ◽

Sensing Data ◽

Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

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Assessing the impact of urbanization on net primary productivity using multi-scale remote sensing data: a case study of Xuzhou, China

Frontiers of Earth Science ◽

10.1007/s11707-014-0454-7 ◽

2014 ◽

Vol 9 (2) ◽

pp. 319-329 ◽

Cited By ~ 5

Author(s):

Kun Tan ◽

Songyang Zhou ◽

Erzhu Li ◽

Peijun Du

Keyword(s):

Remote Sensing ◽

Primary Productivity ◽

Net Primary Productivity ◽

Remote Sensing Data ◽

Sensing Data ◽

Multi Scale ◽

Impact Of Urbanization ◽

The Impact

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THE MULTI-LEVEL AND MULTI-SCALE FACTOR ANALYSIS FOR SOIL MOISTURE INFORMATION EXTRACTION BY MULTI-SOURCE REMOTE SENSING DATA

ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w1-167-2013 ◽

2013 ◽

Vol XL-7/W1 ◽

pp. 167-171 ◽

Cited By ~ 1

Author(s):

F. Yu ◽

H. T. Li ◽

Y. Jia ◽

Y. S. Han ◽

H. Y. Gu

Keyword(s):

Remote Sensing ◽

Factor Analysis ◽

Soil Moisture ◽

Information Extraction ◽

Remote Sensing Data ◽

Scale Factor ◽

Sensing Data ◽

Multi Scale ◽

Multi Level

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Spatio-temporal Cokriging method for assimilating and downscaling multi-scale remote sensing data

Remote Sensing of Environment ◽

10.1016/j.rse.2020.112190 ◽

2020 ◽

pp. 112190

Author(s):

Bo Yang ◽

Hongxing Liu ◽

Emily L. Kang ◽

Song Shu ◽

Min Xu ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Sensing Data ◽

Multi Scale ◽

Spatio Temporal

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Integrating Local and Global Error Statistics for Multi-Scale RBF Network Training: An Assessment on Remote Sensing Data

PLoS ONE ◽

10.1371/journal.pone.0040093 ◽

2012 ◽

Vol 7 (8) ◽

pp. e40093 ◽

Cited By ~ 2

Author(s):

Giorgos Mountrakis ◽

Wei Zhuang

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Global Error ◽

Error Statistics ◽

Rbf Network ◽

Sensing Data ◽

Multi Scale ◽

Network Training

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NDVI Changes of the Nansi Lake in Shandong Province of China

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.919-921.1659 ◽

2014 ◽

Vol 919-921 ◽

pp. 1659-1662

Author(s):

Chun Ling Liang

Keyword(s):

Remote Sensing ◽

Seasonal Variation ◽

Remote Sensing Data ◽

Wetland Vegetation ◽

Shandong Province ◽

Nansi Lake ◽

Sensing Data ◽

Vegetation Indexes ◽

Vegetation Activity ◽

Variation Characteristics

The normalized difference vegetation indexes (NDVI) of the Nansi Lake wetland in Shandong province is are calculated on the basis of MSS, TM and ETM+ remote sensing data collected by the Landsat satellites. The characteristics of seasonal vegetation activity of the Nansi Lake during 1973-2011 were revealed. The seasonal variation characteristics of wetland vegetation in Nansi Lake are not identical in the last 40 years. The NDVI in spring (P=0.0216) first decreased and then increased; The NDVI in summer (P=0.0007) and winter (P=0.0189) present a significant decreasing trend. The variation of the NDVI in autumn is small.

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