Missing Burns in the High Northern Latitudes: The Case for Regionally Focused Burned Area Products

Global estimates of burned areas, enabled by the wide-open access to the standard data products from the Moderate Resolution Imaging Spectroradiometer (MODIS), are heavily relied on by scientists and managers studying issues related to wildfire occurrence and its worldwide consequences. While these datasets, particularly the MODIS MCD64A1 product, have fundamentally improved our understanding of wildfire regimes at the global scale, their performance may be less reliable in certain regions due to a series of region- or ecosystem-specific challenges. Previous studies have indicated that global burned area products tend to underestimate the extent of the burned area within some parts of the boreal domain. Despite this, global products are still being regularly used by research activities and management efforts in the northern regions, likely due to a lack of understanding of the spatial scale of their Arctic-specific limitations, as well as an absence of more reliable alternative products. In this study, we evaluated the performance of two widely used global burned area products, MCD64A1 and FireCCI51, in the circumpolar boreal forests and tundra between 2001 and 2015. Our two-step evaluation shows that MCD64A1 has high commission and omission errors in mapping burned areas in the boreal forests and tundra regions in North America. The omission error overshadows the commission error, leading to MCD64A1 considerably underestimating burned areas in these high northern latitude domains. Based on our estimation, MCD64A1 missed nearly half the total burned areas in the Alaskan and Canadian boreal forests and the tundra during the 15-year period, amounting to an area (74,768 km2) that is equivalent to the land area of the United States state of South Carolina. While the FireCCI51 product performs much better than MCD64A1 in terms of commission error, we found that it also missed about 40% of burned areas in North America north of 60° N between 2001 and 2015. Our intercomparison of MCD64A1 and FireCCI51 with a regionally adapted MODIS-based Arctic Boreal Burned Area (ABBA) shows that the latter outperforms both MCD64A1 and FireCCI51 by a large margin, particularly in terms of omission error, and thus delivers a considerably more accurate and consistent estimate of fire activity in the high northern latitudes. Considering the fact that boreal forests and tundra represent the largest carbon pool on Earth and that wildfire is the dominant disturbance agent in these ecosystems, our study presents a strong case for regional burned area products like ABBA to be included in future Earth system models as the critical input for understanding wildfires’ impacts on global carbon cycling and energy budget.

ERRORS MADE BY THE TENTH GRADE STUDENTS OF SMA NEGERI 1 ULU MORO’O IN THEIR WRITING RECOUNT TEXT COMPOSITION IN ACADEMIC YEAR 2020/2021

This study is about errors made by the tenth grade students of SMA Negeri 1 Ulu Moro’o in writing recount text composition in academic year 2019/2020. The objectives of this study is to find out types of errors made by the tenth grade students in their writing recount text composition and to find out the most dominant errors made by the tenth grade students in writing recount text composition. The instrument of the research is test and documentation. The research methodology is descriptive qualitative. The subject of this study is the tenth grade students of SMA Negeri 1 Ulu Moro’o, which is consist of 30 students. The result of this study showed that there are four types of errors that occur; they are error of omission, error of addition, error of misformation and error of misordering. The data was taken from the test: it was written text. The findings showed that errors made by the students were 19,80% omission, 17,82% addition, 53,46% misformation, and 8,91% misordering. The writer observed 101 total errors. From the frequency of each error types, misformation was the error which most frequently produced by the students. The writer concluded that misformation was the dominant kind of errors made by the tenth grade students of SMA Negeri 1 Ulu Moro’o in writing recount text composition.

Cloud Detection Using an Ensemble of Pixel-Based Machine Learning Models Incorporating Unsupervised Classification

Remote sensing imagery, such as that provided by the United States Geological Survey (USGS) Landsat satellites, has been widely used to study environmental protection, hazard analysis, and urban planning for decades. Clouds are a constant challenge for such imagery and, if not handled correctly, can cause a variety of issues for a wide range of remote sensing analyses. Typically, cloud mask algorithms use the entire image; in this study we present an ensemble of different pixel-based approaches to cloud pixel modeling. Based on four training subsets with a selection of different input features, 12 machine learning models were created. We evaluated these models using the cropped LC8-Biome cloud validation dataset. As a comparison, Fmask was also applied to the cropped scene Biome dataset. One goal of this research is to explore a machine learning modeling approach that uses as small a training data sample as possible but still provides an accurate model. Overall, the model trained on the sample subset (1.3% of the total training samples) that includes unsupervised Self-Organizing Map classification results as an input feature has the best performance. The approach achieves 98.57% overall accuracy, 1.18% cloud omission error, and 0.93% cloud commission error on the 88 cropped test images. By comparison to Fmask 4.0, this model improves the accuracy by 10.12% and reduces the cloud omission error by 6.39%. Furthermore, using an additional eight independent validation images that were not sampled in model training, the model trained on the second largest subset with an additional five features has the highest overall accuracy at 86.35%, with 12.48% cloud omission error and 7.96% cloud commission error. This model’s overall correctness increased by 3.26%, and the cloud omission error decreased by 1.28% compared to Fmask 4.0. The machine learning cloud classification models discussed in this paper could achieve very good performance utilizing only a small portion of the total training pixels available. We showed that a pixel-based cloud classification model, and that as each scene obviously has unique spectral characteristics, and having a small portion of example pixels from each of the sub-regions in a scene can improve the model accuracy significantly.

A Burned Area Mapping Algorithm for Sentinel-2 Data Based on Approximate Reasoning and Region Growing

Sentinel-2 (S2) multi-spectral instrument (MSI) images are used in an automated approach built on fuzzy set theory and a region growing (RG) algorithm to identify areas affected by fires in Mediterranean regions. S2 spectral bands and their post- and pre-fire date (Dpost-pre) difference are interpreted as evidence of burn through soft constraints of membership functions defined from statistics of burned/unburned training regions; evidence of burn brought by the S2 spectral bands (partial evidence) is integrated using ordered weighted averaging (OWA) operators that provide synthetic score layers of likelihood of burn (global evidence of burn) that are combined in an RG algorithm. The algorithm is defined over a training site located in Italy, Vesuvius National Park, where membership functions are defined and OWA and RG algorithms are first tested. Over this site, validation is carried out by comparison with reference fire perimeters derived from supervised classification of very high-resolution (VHR) PlanetScope images leading to more than satisfactory results with Dice coefficient >0.84, commission error <0.22 and omission error <0.15. The algorithm is tested for exportability over five sites in Portugal (1), Spain (2) and Greece (2) to evaluate the performance by comparison with fire reference perimeters derived from the Copernicus Emergency Management Service (EMS) database. In these sites, we estimate commission error <0.15, omission error <0.1 and Dice coefficient >0.9 with accuracy in some cases greater than values obtained in the training site. Regression analysis confirmed the satisfactory accuracy levels achieved over all sites. The algorithm proposed offers the advantages of being least dependent on a priori/supervised selection for input bands (by building on the integration of redundant partial burn evidence) and for criteria/threshold to obtain segmentation into burned/unburned areas.

Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang

Underground coal fires have become a worldwide disaster, which brings serious environmental pollution and massive energy waste. Xinjiang is one of the regions that is seriously affected by the underground coal fires. After years of extinguishing, the underground coal fire areas in Xinjiang have not been significantly reduced yet. To extinguish underground coal fires, it is critical to identify and monitor them. Recently, remote sensing technologies have been showing great potential in coal fires’ identification and monitoring. The thermal infrared technology is usually used to detect thermal anomalies in coal fire areas, and the Differential Synthetic Aperture Radar Interferometry (DInSAR) technology for the detection of coal fires related to ground subsidence. However, non-coal fire thermal anomalies caused by ground objects with low specific heat capacity, and surface subsidence caused by mining and crustal activities have seriously affected the detection accuracy of coal fire areas. To improve coal fires’ detection accuracy by using remote sensing technologies, this study firstly obtains temperature, normalized difference vegetation index (NDVI), and subsidence information based on Landsat8 and Sentinel-1 data, respectively. Then, a multi-source information strength and weakness constraint method (SWCM) is proposed for coal fire identification and analysis. The results show that the proposed SWCM has the highest coal fire identification accuracy among the employed methods. Moreover, it can significantly reduce the commission and omission error caused by non-coal fire-related thermal anomalies and subsidence. Specifically, the commission error is reduced by 70.4% on average, and the omission error is reduced by 30.6%. Based on the results, the spatio-temporal change characteristics of the coal fire areas have been obtained. In addition, it is found that there is a significant negative correlation between the time-series temperature and the subsidence rate of the coal fire areas (R2 reaches 0.82), which indicates the feasibility of using both temperature and subsidence to identify and monitor underground coal fires.

AN ERROR ANALYSIS ON SUBJECT-VERB AGREEMENT IN CONSTRUCTING SIMPLE PRESENT TENSE MADE BY THE SECOND SEMESTER STUDENTS OF LAW FACULTY

This research was aimed to find out the subject-verb agreement error made by the second semester students of Law Faculty UNIKA Santo Thomas. From the findings, 27% of the instruments answered by the students were identified error. There were 4 kinds of error made by the students in constructing subject-verb agreement. They were omission error, addition error, misformation error, and misordering error. In omission error, there were 26 errors occurred. In addition error, there were 5 errors occurred. In misformation error, there were 54 errors occurred. In misordering error, there was 1 error occurred. Among all of the errors that had been identified, misformation error was the most dominan error faced by the students. Based on the analysis, the possible cause of error was because of the students did not understand well about the construction of the simple present tense mastery. Although only 27% instrument was identified error, it was considered that the students’ lack of knowledge in structure could give serious impact to their writing. It was suggested that the students need to enrich their knowledge at least in the mastery of the simple present tense, specially in the construction of subject-verb agreement. In other hand, the faculty need to make extra class for the English subject to give the students more chance to enhance their English knowledge. This would support the vision of UNIKA Santo Thomas in achieving International Level University.

An Analysis of Lexical and Syntactical Errors in English Abstract Made by Students of the Accounting Department Polmed

The main purpose of the study is to find out the kinds of lexical and syntactical errors in writing the student’s paper of English Abstract. The data collecting technique used the field research method from 25 student’s papers of English abstract of Accounting Department Politeknik Negeri Medan. The method used in the research is a qualitative approach. The findings of the research revealed that there were so many errors found in the student’s papers of English abstract. There were two kinds of lexical errors relate to Semantic Error in Lexis found in the student’s papers of English abstract which are Confusion of Sense Relations and Collocational Errors. The number of errors in Confusion of Sense was 20 errors with percentage 62.50 % while the collocational errors were also found as many as 12 errors that constituted 37,50% of the total errors collected in the data analysis process. The errors in the syntactical level are also found in the student’s paper of English abstract based on surface structure taxonomy. There were five kinds of errors found in the analysis process. The most frequent errors made by students are misformation 62 (40,79%) of error followed by omission error reach 54 (35,53%) of errors. The addition errors reach 22 (14,47%) of error. then, misordering errors are found 13 (8,55%) of errors. finally, the blend is only found 1 (0,66%) of errors in student’s papers of English abstract in Accounting Department Politeknik Negeri Medan.

Estimation of Vertical Datum Parameters Using the GBVP Approach Based on the Combined Global Geopotential Models

Unification of the global vertical datum has been a key problem to be solved for geodesy over a long period, and the main challenge for a unified vertical datum system is to determine the vertical offset between the local vertical datum and the global vertical datum. For this purpose, the geodetic boundary value problem (GBVP) approach based on the remove-compute-restore (RCR) technique is used to determine the vertical datum parameters in this paper. In the RCR technique, a global geopotential model (GGM) is required to remove and restore the long wavelengths of the gravity field. The satellite missions of the GRACE (Gravity Recovery and Climate Experiment) and GOCE (Gravity field and steady-state Ocean Circulation Exploration) offer high accuracy medium–long gravity filed information, but GRACE/GOCE-based GGMs are restricted to medium–long wavelengths because the maximum degree of their spherical harmonic representation is limited, which is known as an omission error. To compensate for the omission error of GRACE/GOCE-based GGM, a weighting method is used to determine the combined GGM by combining the high-resolution EGM2008 model (Earth Gravitational Model 2008) and GRACE/GOCE-based GGM to effectively bridge the spectral gap between satellite and terrestrial data. An additional consideration for the high-frequency gravity signals is induced by the topography, and the residual terrain model (RTM) is used to recover the omission errors effect of the combined GGM. In addition, to facilitate practical implementation of the GBVP approach, the effects of the indirect bias term, the spectral accuracy of the GGM, and the systematic levelling errors and distortions in estimations of the vertical datum parameters are investigated in this study. Finally, as a result of the GBVP solution based on the combined DIR_R6/EGM2008 model, RTM, and residual gravity, the geopotential values of the North American Vertical Datum of 1988 (NAVD88), the Australian Height Datum (AHD), and the Hong Kong Principal Datum (HKPD) are estimated to be equal to 62636861.31 ± 0.96, 62653852.60 ± 0.95 and 62636860.55 ± 0.29 m2s−2, respectively. The vertical offsets of NAVD88, AHD, and HKPD with respect to the global geoid are estimated as −0.809 ± 0.090, 0.082 ± 0.093, and −0.731 ± 0.030 m, respectively.

KESALAHAN PENGUCAPAN BAHASA INGGRIS PADA MAHASISWA (ERROR PRONUNCIATION)

<p align="center"><strong>Abstrak</strong></p><p>Penelitian ini merupakan penelitian </p><p align="center"><strong>Abstrak</strong></p><p>Penelitian ini merupakan penelitian deskriptif kualitatif yang bertujuan untuk mendeskripsikan kesalahan dalam pengucapan vokal Bahasa Inggris, penyebab terjadinya kesalahan dan jenis kesalahan yang terjadi. Hasil penelitian ini menunjukkan 224 kata yang sering salah diucapkan oleh mahasiswa. Hal ini terjadi karena perbedaan vokal dalam Bahasa Indonesia dan Bahasa Inggris, ada beberapa vokal dalam Bahasa Inggris yang tidak dimilliki oleh Bahasa Indonesia sehingga mahasiswa seringkali menggantinya dengan suara yang mirip dengan Bahasa pertama mereka. Jenis kesalahan yang terjadi dalam pengucapan vokal Bahasa Inggris adalah kesalahan substitusi, kesalahan sisipan, dan kesalahan penghilangan. Kesalahan substitusi terjadi hampir 90% dari seluruh pengucapan yang dilakukan oleh responden.</p><p> </p><p>Kata kunci: kesalahan pengucapan, substitusi, sisipan, penghilangan</p><p align="center"><strong>Abstract</strong></p><p>This is a descriptive qualitative research which aims to describe the error pronunciation of English vowel, the cause of the error, and the type of the error. The result of this research shows there are 224 words which is frequently mispronounce by the students. This is caused by the discrepancies of the vowel words in English and in Indonesian language. Some of the English vowel words do not exist in Indonesian language as a result most of the students replace those words which have similar sounds to their native language. The types of error often happened are the substitution error, inserting error, and omission error. The substitutions error is the highest error made by the students. Nearly 90% of the mispronounce words is caused by the substitution error.</p><p> </p><p>Keywords: error pronunciation; substitution; inserting; omission</p>