Phenological Observations on Classical Prehistoric Sites in the Middle and Lower Reaches of the Yellow River Based on Landsat NDVI Time Series

Pan, Yuqing; Nie, Yueping; Watene, Chege; Zhu, Jianfeng; Liu, Fang

doi:10.3390/rs9040374

Cited by 10 publications

(7 citation statements)

References 25 publications

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“…The results indicate that inflection points both in the σ d time series and in the slope curve could be correlated with phenological metrics. Compared with the previous studies using optical remote sensing data, there were fewer atmospheric effects (e.g., cloud, haze) on the C-band SAR data, which would lead to a better and steadier temporal resolution [10,48]. In addition, it should be noted that the phenological metrics based on the analysis of backscatter time series have shown the main changes in the canopy structure (e.g., vertical structure, vegetation coverage) which are different than those in previous research based on NDVI or other vegetation indices [49,50].…”

Section: Monitoring Of Winter Wheat Phenologymentioning

confidence: 94%

Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

Song

Wang

2019

Remote Sensing

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Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the North China Plain, East Asia, which has a stable cropping pattern and similar phenological stages across the region. Ground phenological observations acquired from a typical agro-meteorological station were used as a priori knowledge. A parallelepiped classifier processed VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving) backscatter signals in order to map the winter wheat planting area. An accuracy assessment showed that the total classification accuracy reached 84% and the Kappa coefficient was 0.77. Both the difference ( σ d ) between VH and VV and its slope were obtained to contrast with a priori knowledge and then used to extract the phenological metrics. Our findings from the analysis of the time series showed that the seedling, tillering, overwintering, jointing, and heading of winter wheat may be closely related to σ d and its slope. Overall, this study presents a generalizable methodology for mapping the winter wheat planting area and monitoring phenology using Sentinel-1 backscatter time series, especially in areas lacking optical remote sensing data. Our results suggest that the main change in Sentinel-1 backscatter is dominated by the vegetation canopy structure, which is different from the established methods using optical remote sensing data, and it is available for phenological metrics extraction.

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Section: Monitoring Of Winter Wheat Phenologymentioning

confidence: 94%

Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

Song

Wang

2019

Remote Sensing

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“…no individual image reveals the sites, the authors' diachronic, phenological analysis can recognize sites with great precision, revealing the location of thousands of both known and previously undocumented sites. Menze & Ur's (2012) methods likely work best in regions where sites and features are easily recognizable in satellite imagery, as was similarly shown to be the case in a study from China that relies on a time-series analysis of Landsat-derived NDVI (normalized difference vegetation index) images (Pan et al 2017). Even in regions where sites are less evident from space, analyses of high-resolution multispectral satellite imagery alongside drone-acquired imagery have been used to successfully document ancient settlements on the basis of how they impact vegetation, revealing sites in West Africa (Reid 2020), western Greenland (Fenger-Nielsen et al 2019, and coastal Peru (Vining 2018), and even ephemeral hunter-gatherer sites in Alaska (Keeney & Hickey 2015).…”

Section: Discovering Settlement Historiesmentioning

confidence: 80%

Rethinking the Landscape: Emerging Approaches to Archaeological Remote Sensing

Casana

2021

Annu. Rev. Anthropol.

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An emerging arena of archaeological research is beginning to deploy remote sensing technologies—including aerial and satellite imagery, digital topographic data, and drone-acquired and terrestrial geophysical data—not only in support of conventional fieldwork but also as an independent means of exploring the archaeological landscape. This article provides a critical review of recent research that relies on an ever-growing arsenal of imagery and instruments to undertake innovative investigations: mapping regional-scale settlement histories, documenting ancient land use practices, revealing the complexity of settled spaces, building nuanced pictures of environmental contexts, and monitoring at-risk cultural heritage. At the same time, the disruptive nature of these technologies is generating complex new challenges and controversies surrounding data access and preservation, approaches to a deluge of information, and issues of ethical remote sensing. As we navigate these challenges, remote sensing technologies nonetheless offer revolutionary ways of interrogating the archaeological record and transformative insights into the human past. Expected final online publication date for the Annual Review of Anthropology, Volume 50 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…Since its inception by Breiman [72], RF has been effectively used in several domains like pharmacology [73,74], medical imaging [75][76][77], and genetics [78][79][80] because of its intrinsic ability to measure variable importance and its robust predictive power. Furthermore, throughout the last decade, there was a surge in the use of RF in different remote sensing applications, such as vegetation species mapping [81][82][83], agriculture [84,85], and archaeology [20,86].…”

Section: Segmentation and Feature Selectionmentioning

confidence: 99%

Evaluating the Performance of Geographic Object-Based Image Analysis in Mapping Archaeological Landscapes Previously Occupied by Farming Communities: A Case of Shashi–Limpopo Confluence Area

Thabeng,

Adam,

Merlo

2023

Remote Sensing

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The use of pixel-based remote sensing techniques in archaeology is usually limited by spectral confusion between archaeological material and the surrounding environment because they rely on the spectral contrast between features. To deal with this problem, we investigated the possibility of using geographic object-based image analysis (GEOBIA) to predict archaeological and non-archaeological features. The chosen study area was previously occupied by farming communities and is characterised by natural soils (non-sites), vitrified dung, non-vitrified dung, and savannah woody vegetation. The study uses a three-stage GEOBIA that comprises (1) image object segmentation, (2) feature selection, and (3) object classification. The spectral mean of each band and the area extent of an object were selected as input variables for object classifications in support vector machines (SVM) and random forest (RF) classifiers. The results of this study have shown that GEOBIA approaches have the potential to map archaeological landscapes. The SVM and RF classifiers achieved high classification accuracies of 96.58% and 94.87%, respectively. Visual inspection of the classified images has demonstrated the importance of the aforementioned models in mapping archaeological and non-archaeological features because of their ability to manage the spectral confusion between non-sites and vitrified dung sites. In summary, the results have demonstrated that the GEOBIAs ability to incorporate spatial elements in the classification model ameliorates the chances of distinguishing materials with limited spectral differences.

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Phenological Observations on Classical Prehistoric Sites in the Middle and Lower Reaches of the Yellow River Based on Landsat NDVI Time Series

Cited by 10 publications

References 25 publications

Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

Rethinking the Landscape: Emerging Approaches to Archaeological Remote Sensing

Evaluating the Performance of Geographic Object-Based Image Analysis in Mapping Archaeological Landscapes Previously Occupied by Farming Communities: A Case of Shashi–Limpopo Confluence Area

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