scholarly journals An Automated Model to Evaluate Landscape Patches with Analysis of the Neighborhood Relations

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 15
Author(s):  
Serdar Selim ◽  
Nusret Demir
Keyword(s):  
Landscape Structure ◽  
Function Analysis ◽  
Satellite Image ◽  
Test Site ◽  
Specific Class ◽  
Random Forest Classification ◽  
Automated Method ◽  
Forest Classification ◽  
Sentinel 2A ◽  
Landscape Patches

The landscape should be analyzed in segments to understand its texture, structure, function, and changes. These segments can be used to evaluate landscape structure and for function analysis. In this context, the most important segments which form the landscape are landscape patches. Analysis and understanding of the landscape structure and ecological progress needs measurement of the landscape patches and evaluation. Therefore, the neighborhood ratio between the patches should be known. In this study, we propose an automated method, which is based on Python language, to compute this ratio with consideration of neighborhood degrees between the patches. The test site was Mugla-Koycegiz, a town in Turkey, where there is a huge population of Sweetgum (Liquidambar orientalis) trees, and the town is important for shoreline tourism. Urban area, water surface, agricultural areas, marsh, and forest classes were defined. Sentinel 2A multispectral satellite image was used and the Random Forest classification method applied. The derived patches were produced from the classification, and then converted to the vector form. All vector boundaries were converted to point features with 10 m intervals. The ratio of the number of points neighboring the specific class to all points along the boundary was computed automatically with developed script. Three different patches were analyzed, and the results are reported.

scholarly journals Fully Automated Detection of Paramagnetic Rims in Multiple Sclerosis Lesions on 3T Susceptibility-Based MR Imaging

2020 ◽  
Author(s):  
Carolyn Lou ◽  
Pascal Sati ◽  
Martina Absinta ◽  
Kelly Clark ◽  
Jordan D. Dworkin ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Severe Disease ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
List Type ◽  
Training Set ◽  
Random Forest Classification ◽  
Automated Method ◽  
Forest Classification

AbstractBackground and PurposeThe presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis (MS) lesion. Increased prevalence of these paramagnetic rim lesions (PRLs) is associated with a more severe disease course in MS. The identification of these lesions is time-consuming to perform manually. We present a method to automatically detect PRLs on 3T T2*-phase images.MethodsT1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 19 subjects with MS. The images were then processed with lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 877 lesions were identified, 118 (13%) of which contained a paramagnetic rim. We divided our data into a training set (15 patients, 673 lesions) and a testing set (4 patients, 204 lesions). We fit a random forest classification model on the training set and assessed our ability to classify lesions as PRL on the test set.ResultsThe number of PRLs per subject identified via our automated lesion labelling method was highly correlated with the gold standard count of PRLs per subject, r = 0.91 (95% CI [0.79, 0.97]). The classification algorithm using radiomic features can classify a lesion as PRL or not with an area under the curve of 0.80 (95% CI [0.67, 0.86]).ConclusionThis study develops a fully automated technique for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.HighlightsA fully automated method for both the identification and classification of paramagnetic rim lesions is proposed.Radiomic features in conjunction with machine learning algorithms can accurately classify paramagnetic rim lesions.Challenges for classification are largely driven by heterogeneity between lesions, including equivocal rim signatures and lesion location.

scholarly journals COMPARISON OF DIFFERENT CLASSIFICATION TECHNIQUES TO IDENTIFY COCONUT GROWING AREAS IN KOZIKHODE DISTRICT, KERALA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 321-324
Author(s):  
N. Singh ◽  
S. Roy ◽  
P. Kumar ◽  
M. M. Kimothi ◽  
S. Mamatha
Keyword(s):  
Random Forest ◽  
Google Earth ◽  
Random Forest Classification ◽  
Classification Technique ◽  
Forest Classification ◽  
High Resolution Imagery ◽  
The Government ◽  
Kerala State ◽  
Sentinel 2A ◽  
Government Statistics

<p><strong>Abstract.</strong> This study was envisaged to map the coconut growing areas in Kerala state of India, using multidate NDVI obtained from sentinel 2A MSI data, having spatial resolution as 10 m. 95% Cloud free satellite images were taken for classification and date of pass considered for the study were 16th February, 2017 and 18th December, 2017 for Kozhikode district of Kerala. In this study bio-window of coconut plantation was identified using NDVI images of two dates. It was observed that interclass variations were more prominent in February image. Forest, dense and moderately dense coconut plantations have significantly different NDVI values in February image whereas in December image all three features have similar values. Hence, February image was classified using three classification methods i.e. ISODATA, maximum likelihood and random forest classification to assess which method is better to distinguish coconut plantation from other classes. Random Forest classification technique was found to be more accurate in identifying coconut plantation. Area was also estimated for Kozhikode district and compared with the government statistics. Google Earth was taken as reference to identify coconut plantation as it has a unique star shaped canopy, which is clearly visible in high-resolution imagery.</p>

Step-by-Step Processing of Sentinel-1 data for Estimation of Rice Area.

2019 ◽  
Author(s):  
Awais Karamat ◽  
Muhammad Nawaz ◽  
Ali Imam Mirza ◽  
Muhammad Rahat Jamil ◽  
Ali Asghar ◽  
...  
Keyword(s):  
Satellite Image ◽  
European Space Agency ◽  
Kappa Statistics ◽  
Random Forest Classification ◽  
Rice Farmers ◽  
Space Agency ◽  
Forest Classification ◽  
Rice Area ◽  
Local Farmers ◽  
Strong Agreement

Rice has become an essential part of four pillars of food security, especially in Asia, where it is produced over large spatial extents and also consumed widely. About 89 % of the global rice production is targeted and achieved from Asian countries. We downloaded Sentinel-1 datasets from official website of European Space Agency (ESA) for identification of rice patterns in the study site. The data was selected in Ground Range Detection (GRD) format and applied the toolbox in Sentinel Application Platform (SNAP) for further processing. We applied the orbit file for geometric and radiometric corrections, LEE filter for removal of spackles, resampling to convert 20*20m2 to 10*10m2 pixel size and finally the Random Forest Classification (RFC) to classify the satellite image. The classification results of Sentinel image for the year 2018, show that the total area of the study site was 360021 ha, including 144991 ha as rice area, 130598 as other vegetation, 19339 ha as water body and the built-up area was estimated as 5693 ha. Kappa statistics resulted the overall accuracy of 85% which is in strong agreement to ground reality. We observed that the rice area was increased from 140403 ha in 2017 to 144991 ha in 2018. The main reason of this increase in rice area was observed as the preference of local farmers to grow rice in comparison to other crops because the local government was offering high subsidy to rice farmers. Moreover, district Nankana-Sahib produces rice of expert quality which is famous throughout the world therefore, it is considered as cash crop.

scholarly journals MRI-based radiomic feature analysis of end-stage liver disease for severity stratification

2021 ◽  
Author(s):  
Jennifer Nitsch ◽  
Jordan Sack ◽  
Michael W. Halle ◽  
Jan H. Moltz ◽  
April Wall ◽  
...  
Keyword(s):  
Disease Severity ◽  
Prediction Models ◽  
Blood Parameters ◽  
Meld Score ◽  
Poor Correlation ◽  
Discrimination Ability ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Academic Center ◽  
End Stage

Abstract Purpose We aimed to develop a predictive model of disease severity for cirrhosis using MRI-derived radiomic features of the liver and spleen and compared it to the existing disease severity metrics of MELD score and clinical decompensation. The MELD score is compiled solely by blood parameters, and so far, it was not investigated if extracted image-based features have the potential to reflect severity to potentially complement the calculated score. Methods This was a retrospective study of eligible patients with cirrhosis ($$n=90$$ n = 90 ) who underwent a contrast-enhanced MR screening protocol for hepatocellular carcinoma (HCC) screening at a tertiary academic center from 2015 to 2018. Radiomic feature analyses were used to train four prediction models for assessing the patient’s condition at time of scan: MELD score, MELD score $$\ge $$ ≥ 9 (median score of the cohort), MELD score $$\ge $$ ≥ 15 (the inflection between the risk and benefit of transplant), and clinical decompensation. Liver and spleen segmentations were used for feature extraction, followed by cross-validated random forest classification. Results Radiomic features of the liver and spleen were most predictive of clinical decompensation (AUC 0.84), which the MELD score could predict with an AUC of 0.78. Using liver or spleen features alone had slightly lower discrimination ability (AUC of 0.82 for liver and AUC of 0.78 for spleen features only), although this was not statistically significant on our cohort. When radiomic prediction models were trained to predict continuous MELD scores, there was poor correlation. When stratifying risk by splitting our cohort at the median MELD 9 or at MELD 15, our models achieved AUCs of 0.78 or 0.66, respectively. Conclusions We demonstrated that MRI-based radiomic features of the liver and spleen have the potential to predict the severity of liver cirrhosis, using decompensation or MELD status as imperfect surrogate measures for disease severity.

scholarly journals Changes in Landscape Structure in the Municipalities of the Nitra District (Slovak Republic) Due to Expanding Suburbanization

2021 ◽  
Vol 13 (3) ◽  
pp. 1205
Author(s):  
Zuzana Pucherová ◽  
Regina Mišovičová ◽  
Gabriel Bugár ◽  
Henrich Grežo
Keyword(s):  
Landscape Structure ◽  
Slovak Republic ◽  
Satellite Image ◽  
Analytical Data ◽  
European Space Agency ◽  
Rural Landscape ◽  
Landscape Features ◽  
Main Research ◽  
Spatial Changes ◽  
The Impact

Suburbanization, as a set of several factors, influences and changes the landscape structure of smaller municipalities in the hinterland of larger cities. The purpose of this paper is to evaluate the built-up areas related to suburbanization within three time horizons—in 2002, 2005, and 2020—in 62 municipalities of the district (including two cities, Nitra and Vráble). This study examines the process of spatial changes in landscape features (residential, industrial, agricultural, transport) related to suburbanization between 2002 and 2005 and between 2002 and 2020. The input analytical data were digital orthophotomaps from 2002 and 2005 and the current orthophotomosaics of the Slovak Republic from 2017 (GKÚ, Bratislava), updated for the year 2020 using Sentinel 2 satellite image data (European Space Agency). The impact of suburbanization processes between 2002 and 2005 did not reach the dimensions of the changes that occurred due to suburbanization processes between 2002 and 2020 or 2005 and 2020. The main research objective of the article is the identification and assessment of these changes. We determined which landscape features related to suburbanization affected spatial changes in municipalities of the district Nitra. The total area affected by one of the suburbanization processes monitored by us reached 92.52 ha in the period between 2002 and 2005. Between the years 2002 and 2020, the area reached a total of 2272.82 ha, which is an increase of 2180.30 ha in 2020 compared to 2002. This included mainly the expansion of settlements or housing (60.15%), industrial areas (29.31%), transport facilities (4.35%), agricultural areas (0.73%), and other areas (5.46%). These results show expanding suburbanization for the period from 2002 to 2020 and that this process has been gaining momentum in the municipalities of the Nitra district, especially in recent years, which changes the look of rural municipalities and the character of a typical rural landscape.

scholarly journals Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification

2016 ◽  
Vol 146 ◽  
pp. 370-385 ◽  
Author(s):  
Adam Hedberg-Buenz ◽  
Mark A. Christopher ◽  
Carly J. Lewis ◽  
Kimberly A. Fernandes ◽  
Laura M. Dutca ◽  
...  
Keyword(s):  
Random Forest ◽  
Ganglion Cell ◽  
Retinal Ganglion Cell ◽  
Quantitative Measurement ◽  
Cell Populations ◽  
Retinal Ganglion ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery

2021 ◽  
Vol 11 (9) ◽  
pp. 4258
Author(s):  
Jordan R. Cissell ◽  
Steven W. J. Canty ◽  
Michael K. Steinberg ◽  
Loraé T. Simpson
Keyword(s):  
Google Earth ◽  
Mangrove Forests ◽  
Random Forest Classification ◽  
High Resolution Mapping ◽  
Mangrove Ecosystems ◽  
Forest Classification ◽  
Resolution Mapping ◽  
Google Earth Engine ◽  
Terrestrial Biodiversity ◽  
Sentinel 2

In this paper, we present the highest-resolution-available (10 m) national map of the mangrove ecosystems of Belize. These important ecosystems are increasingly threatened by human activities and climate change, support both marine and terrestrial biodiversity, and provide critical ecosystem services to coastal communities in Belize and throughout the Mesoamerican Reef ecoregion. Previous national- and international-level inventories document Belizean mangrove forests at spatial resolutions of 30 m or coarser, but many mangrove patches and loss events may be too small to be accurately mapped at these resolutions. Our 10 m map addresses this need for a finer-scale national mangrove inventory. We mapped mangrove ecosystems in Belize as of 2020 by performing a random forest classification of Sentinel-2 Multispectral Instrument imagery in Google Earth Engine. We mapped a total mangrove area of 578.54 km2 in 2020, with 372.04 km2 located on the mainland and 206.50 km2 distributed throughout the country’s islands and cayes. Our findings are substantially different from previous, coarser-resolution national mangrove inventories of Belize, which emphasizes the importance of high-resolution mapping efforts for ongoing conservation efforts.

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