Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options

Abstract

For food crises early warning purposes, coarse spatial resolution NDVI data are widely used to monitor vegetation conditions in near real-time (NRT). Different types of NDVI anomalies are typically employed to assess the current state of crops and rangelands as compared to previous years. Timeliness and accuracy of such anomalies are critical factors to an effective monitoring. Temporal smoothing can efficiently reduce noise and cloud contamination in the time series of historical observations, where data points are available before and after each observation to be smoothed. With NRT data, smoothing methods are adapted to cope with the unbalanced availability of data before and after the most recent data points. These NRT approaches provide successive updates of the estimation of the same data point as more observations become available. Anomalies compare the current NDVI value with some statistics (e.g. indicators of central tendency and dispersion) extracted from the historical archive of observations. With multiple updates of the same datasets being available, two options can be selected to compute anomalies, i.e. using the same update level for the NRT data and the statistics or using the most reliable update for the latter. In this study we assess the accuracy of three commonly employed 1 km MODIS NDVI anomalies (standard scores, non-exceedance probability and vegetation condition index) with respect to (1) delay with which they become available and (2) option selected for their computation. We show that a large estimation error affects the earlier estimates and that this error is efficiently reduced in subsequent updates. In addition, with regards to the preferable option to compute anomalies, we empirically observe that it depends on the type of application (e.g. averaging anomalies value over an area of interest vs. detecting “drought” conditions by setting a threshold on the anomaly value) and the employed anomaly type. Finally, we map the spatial pattern in the magnitude of NRT anomaly estimation errors over the globe and relate it to average cloudiness.

An Improved GrabCut Method Based on a Visual Attention Model for Rare-Earth Ore Mining Area Recognition with High-Resolution Remote Sensing Images

“…NDVI may be the most frequently used vegetation index in vegetative remote sensing analysis and applications. It has been proven to be a good indicator to distinguish vegetative surfaces from none vegetative surfaces, and also a highly sensitive parameter to represent vegetation growth status [25,26]. The experimental results indicate that the improved GrabCut model based on visual attention model can extract precise REO mining area information from high spatial resolution remote sensing image, and the whole process of REO mining area extraction was fully automatic, not relied on manual intervention.…”

Using 1st Derivative Reflectance Signatures within a Remote Sensing Framework to Identify Macroalgae in Marine Environments

“…We also took all our readings in contact with the ASD measurement wand, which uses a controlled light source. By recording our spectra in this manner, we avoided the large sources of noise that the work by [72][73][74] had to overcome. Examples of such sources of noise are temporal variation, small instantaneous field of view imaging, undetected clouds, and poor atmospheric conditions.…”

Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks

“…In general, these studies are focused on specific elements that can generate significant financial losses or the physical integrity of the people. More recent work addresses the identification of anomalous behaviors using artificial intelligence to identify behaviors in vegetation [20], authorization logs [21], computer systems [22] and finally in modern industry [23].…”