An efficient knowledge-based approach for random variation interpretation in NDVI time series

Abbes, Ali Ben; Hemissi, Selim; Farah, Imed Riadh

doi:10.1007/s12665-018-7951-z

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…This method does not require previous knowledge of analyzed data or the specification of any space-related input parameters and is efficient in terms of execution time [21]. Abbes et al (2018) proposed an efficient knowledge-based approach for vegetation monitoring using the normalized difference vegetation index (NDVI) time series combined with generated association rules to mine the relationship between climate factors and vegetation coverage in Northwestern Tunisia [22].…”

Section: Introductionmentioning

confidence: 99%

Impact of Port Construction on the Spatial Pattern of Land Use in Coastal Zones Based on CLDI and LUT Models: A Case Study of Qingdao and Yantai

Yan

Xiao

et al. 2021

Remote Sensing

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Ports are an important type of land use in coastal cities, and the development of ports has a significant influence on the spatial pattern of land use in port cities. However, the research focusing on economic indicators hardly reflects the process of changes in the spatial distribution of land development in coastal port cities. This paper introduces a spatial association rule method to establish a coastline and land development intensity (CLDI) model and land use transfer (LUT) model in the vertical direction of coastal zones to mine the association rules between shoreline change and land development intensity along the sea–land gradient in the Qingdao and Yantai coastal zones and to explore the important land development sequence patterns. The results showed that, in the early stage of regional development, the land development intensity decreased from sea to land. In the later stage, as the industry transferred to nearby towns, the land units with extremely strong and strong levels started to move to the end or middle of the sequence. With the improvement of the urban construction level, the simple LUT pattern sequence that increased building land through the occupation of cultivated land and forestland was replaced gradually by complex sequences with multiple components. The relationship between land development and distance from the port showed that the areas with strong land development intensity gradually moved from coastal to inland areas over time. Port shipping has a profound influence on port city land use patterns. Industrial transfer drives the development of surrounding towns during the metaphase. This trend was used to build a second port to realize the division of transportation capacity, as the old port’s carrying capacity tended to become saturated. This paper revealed the general changes in the important land use patterns in port areas through a comparative study of the Qingdao and Yantai port areas and the differences among different geographical locations and development processes. This study provides a reference for the rational planning of coastal zone spatial layouts and provides a model basis for the analysis of the spatial structure of coastal zones. This information can be used to coordinate the relationship between ports and cities and promote the sustainable development of coastal zones.

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Section: Introductionmentioning

confidence: 99%

Impact of Port Construction on the Spatial Pattern of Land Use in Coastal Zones Based on CLDI and LUT Models: A Case Study of Qingdao and Yantai

Yan

Xiao

et al. 2021

Remote Sensing

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“…17,20,21 The application of modeling techniques based on time series of coffee plant vegetation index can be a method to reduce the confusion of classifying algorithms of agricultural crops, assisting in the decision making of protocols for preventive or corrective management. 6,22 Based on the assumption that a standard spectral-temporal signature can collaborate with the detection of coffee pest organisms, based on the coffee crop predicted and observed signatures, this work aimed to characterize the spectral dynamics of the vegetative development of coffee fields under different irrigation systems and evaluate the use of EVI to distinguish signatures and evaluate the accuracy of the prediction obtained from the temporal modeling of this index.…”

Section: Introductionmentioning

confidence: 99%

Time series analysis of the enhanced vegetation index to detect coffee crop development under different irrigation systems

Silva

Alves

Sáfadi

et al. 2021

J. Appl. Rem. Sens.

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The coffee crop spectral behavior identification throughout its cycle can contribute to its development monitoring under pest incidence. We aim to identify coffee development through time signatures of enhanced vegetation index (EVI), as well as to evaluate the use of seasonal autoregressive integrated moving average (SARIMA) models to identify coffee trees spectrum-time patterns under different irrigation management and design future scenarios. Three coffee fields were selected under different irrigation systems, whose EVI data of 8 years were obtained from the moderate resolution image spectroradiometer sensor. Each coffee crop model was subjected to residual autocorrelation test and classified according to information criteria, while its accuracy was assessed by means of prediction error measures and agreement index. The estimated and observed EVI values were similar, even for the predicted year. However, in agricultural years during which coffee diseases occurred, the crops showed vegetative vigor below the expected. We concluded that SARIMA models enabled the establishment of a reliable spectral signature expected for coffee crop, which could help with crop management defining, regardless of the irrigation system adopted. Based on the evaluation of divergence between expected and observed spectral signatures, early signs of coffee underdevelopment were detected, which could reduce economic loss risks on its commercial chain.

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A Global 250-m Downscaled NDVI Product from 1982 to 2018

Dong

Lin

et al. 2022

Remote Sensing

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Satellite-based normalized difference vegetation index (NDVI) time series data are useful for monitoring the changes in vegetation ecosystems in the context of global climate change. However, most of the current NDVI products cannot effectively reconcile high spatial resolution and continuous observations in time. Here, to produce a global-scale, long-term, and high-resolution NDVI database, we developed a simple and new data downscaling approach. The downscaling algorithm considers the pixel-wise ratios of the coefficient of variation (CV) between the coarse- and fine-resolution NDVI data and relative changes in the NDVI against a baseline period. The algorithm successfully created a worldwide monthly NDVI database with 250 m resolution from 1982 to 2018 by translating the fine spatial information from MODIS (Moderate-resolution Imaging Spectroradiometer) data and the long-term temporal information from AVHRR (Advanced Very High Resolution Radiometer) data. We employed the evaluation indices of root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (Pearson’s R) to assess the accuracy of the downscaled data against the MODIS NDVI. Both the RMSE and MAE values at the regional and global scales are typically between 0 and 0.2, whereas the Pearson’s R values are mostly above 0.7, which implies that the downscaled NDVI product is similar to the MODIS NDVI product. We then used the downscaled data to monitor the NDVI changes in different plant types and places with significant vegetation heterogeneity, as well as to investigate global vegetation trends over the last four decades. The Google Earth Engine platform was used for all the data downscaling processes, and here we provide a code for users to easily acquire data corresponding to any part of the world. The downscaled global-scale NDVI time series has high potential for the monitoring of the long-term temporal and spatial dynamics of terrestrial ecosystems under changing environments.

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An efficient knowledge-based approach for random variation interpretation in NDVI time series

Cited by 3 publications

References 39 publications

Impact of Port Construction on the Spatial Pattern of Land Use in Coastal Zones Based on CLDI and LUT Models: A Case Study of Qingdao and Yantai

Impact of Port Construction on the Spatial Pattern of Land Use in Coastal Zones Based on CLDI and LUT Models: A Case Study of Qingdao and Yantai

Time series analysis of the enhanced vegetation index to detect coffee crop development under different irrigation systems

A Global 250-m Downscaled NDVI Product from 1982 to 2018

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