Remote Sensing Indices to Measure the Seasonal Dynamics of Photosynthesis in a Southern China Subtropical Evergreen Forest

Floral trait correlations were compared between the two flower morphs of a distylous Ophiorrhiza napoensis population in a subtropical evergreen forest at the Defu Natural Animal Preserve, Guangxi, China. Common principal component analyses indicated that overall patterns in correlations among floral traits were morph specific in the study population. Strong positive correlations (r > 0.9) between anther height and corolla-tube length were found in both morphs. Stigma height correlated positively with corolla-tube length in the long-styled morph (r = 0.843), but not in the short-styled morph (r = −0.018). Flower-morph-specific correlation suggests that natural selection by pollinators has moulded trait covariance among floral traits. Because morph-specific correlations are expressed as the patterns of within-morph variation among multiple traits, putative genes responsible for the stigma-corolla tube correlation should not link to the supergene for sex-organ reciprocity between the morphs, but their expression is limited in the long-styled morph.

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Application of remote sensing indices to digital soil salt composition and ionic strength mapping in the east shore of Urmia Lake, Iran

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2021.100498 ◽

2021 ◽

pp. 100498

Author(s):

Mansour Omrani ◽

Farzin Shahbazi ◽

Bakhtiar Feizizadeh ◽

Shahin Oustan ◽

Nosratollah Najafi

Keyword(s):

Remote Sensing ◽

Ionic Strength ◽

Urmia Lake ◽

Salt Composition ◽

Soil Salt ◽

Remote Sensing Indices

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Monitoring plastic accumulation in water hyacinths using remote sensing

10.5194/egusphere-egu21-12085 ◽

2021 ◽

Author(s):

Niels Janssens ◽

Lauren Biermann ◽

Louise Schreyers ◽

Martin Herold ◽

Tim van Emmerik

Keyword(s):

Remote Sensing ◽

Seasonal Dynamics ◽

Water Hyacinth ◽

Naive Bayes ◽

Pearson Correlation ◽

Field Measurements ◽

Naïve Bayes ◽

Plastic Waste ◽

Water Hyacinths ◽

Sentinel 2

<p>While efforts to quantify plastic waste accumulation in the marine environment are rapidly increasing, the data on plastic transport in rivers are relatively scarce. Rivers are a major source of plastic waste into the oceans and understanding seasonal dynamics of macroplastic transport is necessary to develop effective mitigation measures. Macroplastic transport in rivers varies significantly throughout the year. Research shows that in the case of the Saigon river, Vietnam, these plastic transport fluxes are mainly correlated to the amount of organic debris (mostly water hyacinths). Since large water hyacinths patches can be monitored from space, this gives the opportunity for large scale monitoring using freely available remote sensing products. Remote sensing products, such as Sentinel-2, can be applied to areas where water hyacinths occur and plastic emissions are estimated to be high. In this study, we present a first method to detect and monitor water hyacinths using optical remote sensing. This was done by developing an algorithm to automatically detect and quantify water hyacinth coverage for a large section of the Saigon river in Vietnam, for the year 2018. Spectral signatures of water,&#160; infrastructure in the river, and water hyacinths were used to classify the water hyacinths coverage and dynamics using a Naive Bayes algorithm. Water hyacinths were promisingly identified with 95% accuracy by the Naive Bayes classifier. The comparison between the seasonal dynamics of classified water hyacinth and seasonal dynamics of the field measurements resulted in an overall Pearson correlation of 0.72. The comparison we attempted between seasonal dynamics of plastics from satellite and field measurements yielded a Pearson correlation of 0.48. With the next field campaign collecting in-situ data matched to satellite overpasses, we aim to improve this. In conclusion, we were able to successfully map seasonal dynamics of water hyacinth in an automated way using Sentinel-2 data. Our study provides the first step in exploring the possibilities of mapping water hyacinth from satellite as a proxy for river plastics.</p>

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Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7110418 ◽

2018 ◽

Vol 7 (11) ◽

pp. 418 ◽

Cited By ~ 12

Author(s):

Tian Jiang ◽

Xiangnan Liu ◽

Ling Wu

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Time Series ◽

Deep Learning ◽

Convolutional Neural Network ◽

Vegetation Index ◽

Southern China ◽

Series Data ◽

Support Vector ◽

Complex Landscape

Accurate and timely information about rice planting areas is essential for crop yield estimation, global climate change and agricultural resource management. In this study, we present a novel pixel-level classification approach that uses convolutional neural network (CNN) model to extract the features of enhanced vegetation index (EVI) time series curve for classification. The goal is to explore the practicability of deep learning techniques for rice recognition in complex landscape regions, where rice is easily confused with the surroundings, by using mid-resolution remote sensing images. A transfer learning strategy is utilized to fine tune a pre-trained CNN model and obtain the temporal features of the EVI curve. Support vector machine (SVM), a traditional machine learning approach, is also implemented in the experiment. Finally, we evaluate the accuracy of the two models. Results show that our model performs better than SVM, with the overall accuracies being 93.60% and 91.05%, respectively. Therefore, this technique is appropriate for estimating rice planting areas in southern China on the basis of a pre-trained CNN model by using time series data. And more opportunity and potential can be found for crop classification by remote sensing and deep learning technique in the future study.

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