A Simple Phenology-Based Vegetation Index for Mapping Invasive Spartina Alterniflora Using Google Earth Engine

Xu, Ronglong; Zhao, Siqing; Ke, Yinghai

doi:10.1109/jstars.2020.3038648

Cited by 34 publications

(22 citation statements)

References 51 publications

(49 reference statements)

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“…In another work [2], the authors propose an approach that exploits multivariate geostatistical data fusion techniques to fuse multi-temporal data from a multi-band radiometer and a geophysical sensor, to delineate homogeneous zones to be assigned to differential agricultural management. In [3], the authors developed the Phenological Vegetation Index (PVI), that is synergistically used with the well-known NDVI index to thoroughly extract Spartina Alterniflora from images.…”

Section: Related Workmentioning

confidence: 99%

“…For what concern burn severity evaluation, the Area monitoring module interprets the dNBR values provided by the VI calculation module by using a classification table developed by USGS 3 and reported in Table II. It reports various ranges of the dNBR values and the corresponding class of burn severity; the positive values evidence increasing levels of severity, while values around 0 and negative values denote unburned and post-fire regrowth scenarios.…”

Section: Area Monitoringmentioning

confidence: 99%

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Incremental Knowledge Extraction From IoT-Based System for Anomaly Detection in Vegetation Crops

Cavaliere

Senatore

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Precision agriculture systems collect spectral images from satellites, from which vegetation indices (VIs) can be assessed to monitor vegetation and soil condition. It requires a neardaily data acquisition to perform robust crop monitoring and data analysis. Satellites provide a periodic data acquisition that need a further data integration using multiple satellite sources along with camera-equipped drones to achieve an accurate data collection on a selected area. Moreover, VIs are not enough for a proper vegetation evaluation of the monitored areas due to differences among cultivars, the phenological season in which the vegetation is evaluated, the latitude of the areas, etc. This paper introduces a system model to detect anomalies regarding the vegetation and soil conditions according to the area phenology and the historical vegetation trends. The system collects spectral images of the regions of interest (ROIs) from satellites and drones, harmonized to calculate VIs and feeds a dataset of neardaily high-resolution integrated images. The harmonic analysis allows phenological data extraction about the ROIs, hence the Territorial Observation Model (TOM) has been extended to represent phenological stages and build knowledge on the ROIs and their phenology that is stored on a triple store. The system selects the VI values, calculated during the learned growing seasons of the ROIs, and classifies them to detect vegetation anomalies affecting those ROIs. The collected knowledge can be used by end-users (e.g. agronomists, experts, etc.) to analyze the anomalies correlated to historical results and vegetation trends.

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Section: Related Workmentioning

confidence: 99%

Section: Area Monitoringmentioning

confidence: 99%

Incremental Knowledge Extraction From IoT-Based System for Anomaly Detection in Vegetation Crops

Cavaliere

Senatore

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The climates in these areas vary from tropical monsoon to subtropical monsoon climate with high temperature and rainfall in summer, whereas low temperature and little rainfall in winter. Port development, rapid economic growth as well as abundant habitats of microbial communities have made mangrove ecosystems the priorities of the coastal management programs [38], [43].…”

Section: Study Area and Data A Study Areamentioning

confidence: 99%

Generalized Composite Mangrove Index for Mapping Mangroves Using Sentinel-2 Time Series Data

Xue

Qian

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Monitoring mangroves is critical to protect the coastal ecosystems. Some studies resorted to remote sensing for constructing mangrove indices (MIs). However, there are still some drawbacks in existing MIs. On the one hand, difficulty still persists in distinguishing mangroves from non-mangrove vegetation and non-vegetated areas at the same time. On the other hand, the existing MIs have not fully utilized the phenological trajectories, which can greatly help to distinguish mangroves from other land covers. To overcome these issues, we built a novel mangrove index, namely generalized composite mangrove index (GCMI) by compositing vegetation indices (VIs) and water indices (WIs) based on Sentinel-2 time series data. Firstly, to determine the optimal indices, a similarity trend distance (ST distance) measure was proposed based on Pearson correlation coefficient and dynamic time warping (DTW). Secondly, in order to optimize the weights of selected indices, a population reconstruction genetic algorithm (PRGA) was designed. Finally, mangroves were mapped by feeding the time series of GCMI into random forest (RF) classifier. Experiments conducted over three areas along the southern coast of China demonstrate that: 1) GCMI enhances the separability between mangroves and other land covers compared to the existing VIs, WIs, and MIs, with an averaged OA of 91.45%; 2) ST distance outperforms Euclidean distance, Cosine distance, Pearson correlation coefficient, and DTW in optimizing the weights of GCMI; and 3) PRGA greatly improves the probability of attaining global optimal result. The innovation lies in the presented GCMI considering both the vegetation trajectory information and water inundation using time series.

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“…In coastal areas, the densely turned cloud cover disturbs the process of phonological information deriving. Scene-based methods are less applicable in these areas [1]. Vegetation is crucial for global changes and plays a significant role in the carbon cycle, land-atmosphere interactions, and ecosystem maintenance.…”

Section: Introductionmentioning

confidence: 99%

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Vijayalakshmi¹,

Kumar²

2023

Intelligent Automation &Amp; Soft Computing

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Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions. It is challenging to determine vegetation using traditional map classification approaches. The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties. It is more demandable to determine the multiple spectral analyses for improving the accuracy of vegetation mapping through remotely sensed images. The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping. The architecture comprises three approaches, feature-based approach, region-based approach, and texture-based approach for classifying the vegetation area. The novel Deep Meta fusion model (DMFM) is created with a unique fusion framework of residual stacking of convolution layers with Unique covariate features (UCF), Intensity features (IF), and Colour features (CF). The overhead issues in GPU utilization during Convolution neural network (CNN) models are reduced here with a lightweight architecture. The system considers detailing feature areas to improve classification accuracy and reduce processing time. The proposed DMFM model achieved 99% accuracy, with a maximum processing time of 130 s. The training, testing, and validation losses are degraded to a significant level that shows the performance quality with the DMFM model. The system acts as a standard analysis platform for dynamic datasets since all three different features, such as Unique covariate features (UCF), Intensity features (IF), and Colour features (CF), are considered very well.

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A Simple Phenology-Based Vegetation Index for Mapping Invasive Spartina Alterniflora Using Google Earth Engine

Cited by 34 publications

References 51 publications

Incremental Knowledge Extraction From IoT-Based System for Anomaly Detection in Vegetation Crops

Incremental Knowledge Extraction From IoT-Based System for Anomaly Detection in Vegetation Crops

Generalized Composite Mangrove Index for Mapping Mangroves Using Sentinel-2 Time Series Data

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

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