Mapping the Extent of Mangrove Ecosystem Degradation by Integrating an Ecological Conceptual Model with Satellite Data

Anthropogenic and natural disturbances can cause degradation of ecosystems, reducing their capacity to sustain biodiversity and provide ecosystem services. Understanding the extent of ecosystem degradation is critical for estimating risks to ecosystems, yet there are few existing methods to map degradation at the ecosystem scale and none using freely available satellite data for mangrove ecosystems. In this study, we developed a quantitative classification model of mangrove ecosystem degradation using freely available earth observation data. Crucially, a conceptual model of mangrove ecosystem degradation was established to identify suitable remote sensing variables that support the quantitative classification model, bridging the gap between satellite-derived variables and ecosystem degradation with explicit ecological links. We applied our degradation model to two case-studies, the mangroves of Rakhine State, Myanmar, which are severely threatened by anthropogenic disturbances, and Shark River within the Everglades National Park, USA, which is periodically disturbed by severe tropical storms. Our model suggested that 40% (597 km2) of the extent of mangroves in Rakhine showed evidence of degradation. In the Everglades, the model suggested that the extent of degraded mangrove forest increased from 5.1% to 97.4% following the Category 4 Hurricane Irma in 2017. Quantitative accuracy assessments indicated the model achieved overall accuracies of 77.6% and 79.1% for the Rakhine and the Everglades, respectively. We highlight that using an ecological conceptual model as the basis for building quantitative classification models to estimate the extent of ecosystem degradation ensures the ecological relevance of the classification models. Our developed method enables researchers to move beyond only mapping ecosystem distribution to condition and degradation as well. These results can help support ecosystem risk assessments, natural capital accounting, and restoration planning and provide quantitative estimates of ecosystem degradation for new global biodiversity targets.

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A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

Remote Sensing ◽

10.3390/rs13132442 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2442

Author(s):

Jichao Lv ◽

Rui Zhang ◽

Jinsheng Tu ◽

Mingjie Liao ◽

Jiatai Pang ◽

...

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Satellite Data ◽

Vegetation Index ◽

Soil Moisture Content ◽

Support Vector ◽

Observation Data ◽

Multipath Effect ◽

Mars Model ◽

The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

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Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

Mathematical Problems in Engineering ◽

10.1155/2020/5795976 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6

Author(s):

Jian-ye Yuan ◽

Xin-yuan Nan ◽

Cheng-rong Li ◽

Le-le Sun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Environmental Science ◽

Fine Tuning ◽

Classification Model ◽

Classification Models ◽

Data Set ◽

Classification Tasks ◽

Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

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PEMANFAATAN DATA SATELIT TROPICAL RAINFALL MEASURING MISSION (TRMM) UNTUK PEMETAAN ZONA AGROKLIMAT OLDEMAN DI KALIMANTAN SELATAN

EnviroScienteae ◽

10.20527/es.v12i3.2452 ◽

2016 ◽

Vol 12 (3) ◽

pp. 267 ◽

Cited By ~ 1

Author(s):

Riza Arian Noor ◽

Muhammad Ruslan ◽

Gusti Rusmayadi ◽

Badaruddin Badaruddin

Keyword(s):

Satellite Data ◽

Tropical Rainfall Measuring Mission ◽

Data Availability ◽

Observation Data ◽

Resources Management ◽

Agricultural Resources ◽

Monthly Average ◽

Surface Rainfall ◽

Calculation Results ◽

Arc Gis

The irregularity of observation sites distribution and network density, lack data availability and discontinuity are the obstacles to analyzing and producing the information of agroclimate zone in South Kalimantan. TRMM satellite needs to be researched to overcome the limitations of surface observation data. This study intended to validate TRMM 3B43 satellite data with surface rainfall, to produce Oldeman agroclimate zone based on TRMM satellite data and to analyze the agroclimate zone for agricultural resources management. Data validation is done using the statistical method by analyzing the correlation value (r) and RMSE (Root Mean Square Error). The agroclimate zone is classified based on Oldeman climate classification type. The calculation results are mapped spatially using Arc GIS 10.2 software. The validation result of the TRMM satellite and surface rainfall data shows a high correlation value for the monthly average. The value of correlation coefficient is 0,97 and 25 mm for RMSE value. Oldeman agroclimate zone based on TRMM satellite data in south Kalimantan is divided into five climate zones, such as B1, B2, C1, C2, and D1.

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MicroRNA Profiling as a Methodology to Diagnose Ménière’s Disease: Potential Application of Machine Learning

Otolaryngology ◽

10.1177/0194599820940649 ◽

2020 ◽

pp. 019459982094064

Author(s):

Matthew Shew ◽

Helena Wichova ◽

Andres Bur ◽

Devin C. Koestler ◽

Madeleine St Peter ◽

...

Keyword(s):

Machine Learning ◽

Hearing Loss ◽

Meniere’S Disease ◽

Menière’S Disease ◽

Classification Model ◽

Meniere's Disease ◽

Ménière’S Disease ◽

Classification Models ◽

Menière's Disease ◽

Ménière's Disease

Objective Diagnosis and treatment of Ménière’s disease remains a significant challenge because of our inability to understand what is occurring on a molecular level. MicroRNA (miRNA) perilymph profiling is a safe methodology and may serve as a “liquid biopsy” equivalent. We used machine learning (ML) to evaluate miRNA expression profiles of various inner ear pathologies to predict diagnosis of Ménière’s disease. Study Design Prospective cohort study. Setting Tertiary academic hospital. Subjects and Methods Perilymph was collected during labyrinthectomy (Ménière’s disease, n = 5), stapedotomy (otosclerosis, n = 5), and cochlear implantation (sensorineural hearing loss [SNHL], n = 9). miRNA was isolated and analyzed with the Affymetrix miRNA 4.0 array. Various ML classification models were evaluated with an 80/20 train/test split and cross-validation. Permutation feature importance was performed to understand miRNAs that were critical to the classification models. Results In terms of miRNA profiles for conductive hearing loss versus Ménière’s, 4 models were able to differentiate and identify the 2 disease classes with 100% accuracy. The top-performing models used the same miRNAs in their decision classification model but with different weighted values. All candidate models for SNHL versus Ménière’s performed significantly worse, with the best models achieving 66% accuracy. Ménière’s models showed unique features distinct from SNHL. Conclusions We can use ML to build Ménière’s-specific prediction models using miRNA profile alone. However, ML models were less accurate in predicting SNHL from Ménière’s, likely from overlap of miRNA biomarkers. The power of this technique is that it identifies biomarkers without knowledge of the pathophysiology, potentially leading to identification of novel biomarkers and diagnostic tests.

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Forecasting mangrove ecosystem degradation utilizing quantifiable eco-physiological resilience -A study from Indian Sundarbans

Scientific Reports ◽

10.1038/s41598-020-63586-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Mst Momtaj Begam ◽

Rajojit Chowdhury ◽

Tapan Sutradhar ◽

Chandan Mukherjee ◽

Kiranmoy Chatterjee ◽

...

Keyword(s):

Mangrove Ecosystem ◽

Ecosystem Degradation ◽

Indian Sundarbans

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Visual exploration of classification models for various data types in risk assessment

Information Visualization ◽

10.1177/1473871611433715 ◽

2012 ◽

Vol 11 (3) ◽

pp. 237-251 ◽

Cited By ~ 6

Author(s):

Malgorzata Migut ◽

Marcel Worring

Keyword(s):

Risk Assessment ◽

Visual Analytics ◽

Ordinal Data ◽

Visual Exploration ◽

Classification Model ◽

Classification Models ◽

Data Types ◽

Suggested Approach ◽

Wrong Decision ◽

Data Elements

In risk assessment applications well-informed decisions need to be made based on large amounts of multi-dimensional data. In many domains, not only the risk of a wrong decision, but also of the trade-off between the costs of possible decisions are of utmost importance. In this paper we describe a framework to support the decision-making process, which tightly integrates interactive visual exploration with machine learning. The proposed approach uses a series of interactive 2D visualizations of numerical and ordinal data combined with visualization of classification models. These series of visual elements are linked to the classifier’s performance, which is visualized using an interactive performance curve. This interaction allows the decision-maker to steer the classification model and instantly identify the critical, cost-changing data elements in the various linked visualizations. The critical data elements are represented as images in order to trigger associations related to the knowledge of the expert. In this way the data visualization and classification results are not only linked together, but are also linked back to the classification model. Such a visual analytics framework allows the user to interactively explore the costs of his decisions for different settings of the model and, accordingly, use the most suitable classification model. More informed and reliable decisions result. A case study in the forensic psychiatry domain reveals the usefulness of the suggested approach.

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Validation of Daily Rainfall Based on Global Satellite Mapping of Precipitation (GSMAP) Data of Bali and Nusa Tenggara Region

BULETIN FISIKA ◽

10.24843/bf.2018.v19.i01.p03 ◽

2018 ◽

Vol 19 (1) ◽

pp. 12

Author(s):

Sanjaya Natadiredja ◽

I Ketut Sukarasa ◽

Gusti Ngurah Sutapa

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Satellite Data ◽

Daily Rainfall ◽

Rainfall Data ◽

Observation Data ◽

Similar Data ◽

Strong Positive Correlation ◽

Cause Analysis ◽

Satellite Rainfall

Limitations of observation data cause analysis and prediction of precipitation is difficult. One way to overcome such limitations is the use of satellite data such as GSMaP, but satellite data needs to be validated before use. This study aims to validate GSMaP rainfall data on observation data in Bali and Nusa Tenggara. Through monthly time series analysis, GSMaP rainfall data tend to have smaller value than observation data, but it has similar data pattern in each region with rain pattern that occurs in November to March (NDJFM). While validation between GSMaP satellite rainfall data and observation using Pearson and RMSE correlation and MBE at each location showed strong positive correlation value (> 0.5), correlation value obtained from each location from 0.82 to 0.93 with RMSE value from 2.08 to 5.51 and MBE values ??from 0.23 to 0.89, this indicates that GSMaP satellite data is valid and can be used to fill in empty data especially in 5 observation areas ie Denpasar, Ampenan, Sumbawa Besar, Bima and Kupang.

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Global warming projections derived from an observation-based minimal model

Earth System Dynamics ◽

10.5194/esd-7-51-2016 ◽

2016 ◽

Vol 7 (1) ◽

pp. 51-70 ◽

Cited By ~ 6

Author(s):

K. Rypdal

Keyword(s):

Global Warming ◽

Conceptual Model ◽

Radiative Forcing ◽

Climate Model ◽

Co2 Concentration ◽

Observation Data ◽

Policy Makers ◽

Global Emission ◽

Inertia Parameters ◽

Intercomparison Project

Abstract. A simple conceptual model for the global mean surface temperature (GMST) response to CO2 emissions is presented and analysed. It consists of linear long-memory models for the GMST anomaly response ΔT to radiative forcing and the atmospheric CO2-concentration response ΔC to emission rate. The responses are connected by the standard logarithmic relation between CO2 concentration and its radiative forcing. The model depends on two sensitivity parameters, αT and αC, and two "inertia parameters," the memory exponents βT and βC. Based on observation data, and constrained by results from the Climate Model Intercomparison Project Phase 5 (CMIP5), the likely values and range of these parameters are estimated, and projections of future warming for the parameters in this range are computed for various idealised, but instructive, emission scenarios. It is concluded that delays in the initiation of an effective global emission reduction regime is the single most important factor that influences the magnitude of global warming over the next 2 centuries. The most important aspect of this study is the simplicity and transparency of the conceptual model, which makes it a useful tool for communicating the issue to non-climatologists, students, policy makers, and the general public.

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LoRAS: an oversampling approach for imbalanced datasets

Machine Learning ◽

10.1007/s10994-020-05913-4 ◽

2020 ◽

Author(s):

Saptarshi Bej ◽

Narek Davtyan ◽

Markus Wolfien ◽

Mariam Nassar ◽

Olaf Wolkenhauer

Keyword(s):

Data Distribution ◽

Classification Model ◽

Mathematical Framework ◽

Classification Models ◽

Local Data ◽

Imbalanced Datasets ◽

Minority Class ◽

Data Space ◽

Data Points ◽

The Mean

AbstractThe Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the minority class, leading to misclassifications for the majority class, and effecting the overall balance of the model. In this article, we present an approach that overcomes this limitation of SMOTE, employing Localized Random Affine Shadowsampling (LoRAS) to oversample from an approximated data manifold of the minority class. We benchmarked our algorithm with 14 publicly available imbalanced datasets using three different Machine Learning (ML) algorithms and compared the performance of LoRAS, SMOTE and several SMOTE extensions that share the concept of using convex combinations of minority class data points for oversampling with LoRAS. We observed that LoRAS, on average generates better ML models in terms of F1-Score and Balanced accuracy. Another key observation is that while most of the extensions of SMOTE we have tested, improve the F1-Score with respect to SMOTE on an average, they compromise on the Balanced accuracy of a classification model. LoRAS on the contrary, improves both F1 Score and the Balanced accuracy thus produces better classification models. Moreover, to explain the success of the algorithm, we have constructed a mathematical framework to prove that LoRAS oversampling technique provides a better estimate for the mean of the underlying local data distribution of the minority class data space.

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Experiments on Machine Learning Techniques for Soil Classification Using Sentinel-2 Products

10.5194/egusphere-egu2020-11080 ◽

2020 ◽

Author(s):

Victor Bacu ◽

Teodor Stefanut ◽

Dorian Gorgan

Keyword(s):

Machine Learning ◽

High Resolution ◽

Satellite Data ◽

Field Measurements ◽

Soil Classification ◽

Machine Learning Techniques ◽

Classification Models ◽

Spatial Features ◽

Learning Techniques ◽

Sentinel 2

Agricultural management relies on good, comprehensive and reliable information on the environment and, in particular, the characteristics of the soil. The soil composition, humidity and temperature can fluctuate over time, leading to migration of plant crops, changes in the schedule of agricultural work, and the treatment of soil by chemicals. Various techniques are used to monitor soil conditions and agricultural activities but most of them are based on field measurements. Satellite data opens up a wide range of solutions based on higher resolution images (i.e. spatial, spectral and temporal resolution). Due to this high resolution, satellite data requires powerful computing resources and complex algorithms. The need for up-to-date and high-resolution soil maps and direct access to this information in a versatile and convenient manner is essential for pedology and agriculture experts, farmers and soil monitoring organizations.Unfortunately, the satellite image processing and interpretation are very particular to each area, time and season, and must be calibrated by the real field measurements that are collected periodically. In order to obtain a fairly good accuracy of soil classification at a very high resolution, without using interpolation methods of an insufficient number of measurements, the prediction based on artificial intelligence techniques could be used. The use of machine learning techniques is still largely unexplored, and one of the major challenges is the scalability of the soil classification models toward three main directions: (a) adding new spatial features (i.e. satellite wavelength bands, geospatial parameters, spatial features); (b) scaling from local to global geographical areas; (c) temporal complementarity (i.e. build up the soil description by samples of satellite data acquired along the time, on spring, on summer, in another year, etc.).The presentation analysis some experiments and highlights the main issues on developing a soil classification model based on Sentinel-2 satellite data, machine learning techniques and high-performance computing infrastructures. The experiments concern mainly on the features and temporal scalability of the soil classification models. The research is carried out using the HORUS platform [1] and the HorusApp application [2], [3], which allows experts to scale the computation over cloud infrastructure.&#160;References:[1] Gorgan D., Rusu T., Bacu V., Stefanut T., Nandra N., &#8220;Soil Classification Techniques in Transylvania Area Based on Satellite Data&#8221;. World Soils 2019 Conference, 2 - 3 July 2019, ESA-ESRIN, Frascati, Italy (2019).[2] Bacu V., Stefanut T., Gorgan D., &#8220;Building soil classification maps using HorusApp and Sentinel-2 Products&#8221;, Proceedings of the Intelligent Computer Communication and Processing Conference &#8211; ICCP, in IEEE press (2019).[3] Bacu V., Stefanut T., Nandra N., Rusu T., Gorgan D., &#8220;Soil classification based on Sentinel-2 Products using HorusApp application&#8221;, Geophysical Research Abstracts, Vol. 21, EGU2019-15746, 2019, EGU General Assembly (2019).

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