scholarly journals USO DOS FRACTAIS NA ANÁLISE DA FRAGMENTAÇÃO DE UMA FLORESTA ATRAVÉS DE IMAGENS DE SATÉLITE

FLORESTA ◽  
2002 ◽  
Vol 32 (1) ◽  
Author(s):  
Fábio Minoru Yamaji ◽  
Christel Lingnau ◽  
Carlos Roberto Sanquetta
Keyword(s):  
Fractal Dimension ◽  
Land Cover ◽  
Landscape Pattern ◽  
Fractal Analysis ◽  
Satellite Images ◽  
Landscape Fragmentation ◽  
Araucaria Forest ◽  
Tm Image ◽  
Landsat 5 Tm ◽  
Cover Pattern

Esta pesquisa utilizou a geometria fractal para analisar o padrão da paisagem de uma Floresta Ombrófila Mista alterada. A partir da imagem classificada do Landsat-5 TM, onde foram definidas 8 classes, determinou-se os índices da dimensão fractal "D" e do parâmetro de Pareto "a" para cada tipologia. Com a análise fractal foi possível associar o padrão de cobertura com os índices calculados. Os resultados mostraram que os fragmentos de araucária são os maiores (apresentando um parâmetro de Pareto "a"@16,5) e têm as bordas mais sinuosas (onde o índice da dimensão fractal "D"@1,42). Os menores fragmentos são de capoeira ("a"@13,3) e os fragmentos com as bordas mais simples são da classe água ("D"@1,25). Este estudo mostrou que a análise fractal fornece índices que diminuem o grau de subjetividade na avaliação da fragmentação da paisagem. Use of Fractals in Fragmentation Analysis of Mixed Araucária Forest by Satellite Images Abstract This research aimed the use of fractals to analyze the landscape pattern of a Mixed Araucaria Forest. From the classified Landsat-5 TM image 8 classes were defined and then the fractal dimension "D" and the Pareto parameter "a", for each class, were determined. By using the fractal analysis it was possible to relate land cover pattern and estimated indices. The results showed that araucaria has the largest fragments (with Pareto parameter "a"@16,5) and more complicated perimeters (where fractal dimension "D"@1,42). The smaller fragments ("a"@13,3) are brush, and water has smoother perimeter ("D"@1,25). This study demonstrated that fractal analysis provide indices to reduce the subjectivity level in the evaluation of the landscape fragmentation.

Detecting hotspots of changes in spatial pattern of forest fragmentation in the Romanian Carpathian Mountains

2020 ◽  
Author(s):  
Vasilică-Dănuț Horodnic ◽  
Vasile Efros ◽  
Dumitru Mihăilă ◽  
Luminița-Mirela Lăzărescu ◽  
Petruț-Ionel Bistricean
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Cover ◽  
Regional Scale ◽  
Landscape Fragmentation ◽  
Forest Landscape ◽  
Forest Area Change ◽  
Forest Cover Changes ◽  
Land Restitution ◽  
Cover Pattern

<p>Landscape fragmentation is the expression of patchiness and spatial heterogeneity of land cover pattern. After the breakdown of the socialism regime in 1989, Romania has undergone significant changes at the level of political, institutional and socio-economic profile, which determined researchers to consider this country an experimental territory for land use and landscape research.</p><p>The aim of present study is to detect hotspots of changes of forests landscape fragmentation patterns in the Romanian Carpathian Mountains over the last 28 years. In order to meet our demand we applied a holistic approach to assess the multiple teleconnections between forest cover changes and the degree of fragmentation at regional scale for two distinct periods that make up the 1990-2018 period: (1) 1990-2006 (land restitution period or transition period to the market economy) and (2) 2006-2018 (post-accession period to the European Union).</p><p>The analysis were carried out using freely available time series CORINE Land Cover data of 1990, 2006 and 2018 provided by Copernicus Land Monitoring Services. The initial spatial datasets were processed with the help of Geographic Information Systems (GIS), while GUIDOS, a free software toolbox dedicated to quantitative analysis of digital landscape images, was used to generate spatial and statistics data of the degree of forest landscape fragmentation.</p><p>Our findings indicate that the first period of analysis was more dynamic regarding forest cover changes with a gross area gain of 316 304 ha (7.59%) and a gross area loss of 147 496 ha (3.54%) leading to a net forest area change of 168 808 ha (4.05%) which reflects the level of forest recovery. The change pattern of fragmentation classes showed that 332 045 ha (71.47%) of fragmentation decrease is found for the transition of dominant forest in 1990 into the less fragmented class interior in 2006, while 67 418 ha (65.10%) of all fragmentation increase is found for transition from interior in 1990 to dominant in 2006. The other side, for the period from 2006 to 2018 we found a gross area gain of 127 146 ha (2.93%) and a gross area loss of 212 933 ha (4.91%) leading to a net forest area change of -85 787 ha (-1.98%) which emphasizes the level of forest disturbance. In the same time frame, the high values of fragmentation pattern have been registered for the same classes, 56.82% for fragmentation decrease and 70.60% for fragmentation increase, respectively. The results highlight the reversible impact of land use change on land cover pattern, spatially shaped through afforestation in the first period of analysis and through deforestation in the second period. The afforestation process were determined by high rate of external migration, while deforestation process is a consequence of land restitution laws (Law no. 247/2005), which caused considerable mutations in the ownership of land.</p><p>The study emphasizes the impacts of land use policies and land management practices on the pattern of forest landscape and the usefulness of Guidos Toolbox, a universal digital image object analysis, to detect hotspots of changes at regional scale.</p>

scholarly journals Multiscale Fourier Landscape Pattern Indices For Landscape Ecology

2018 ◽  
Vol 11 (2) ◽  
pp. 5-30
Author(s):  
Alvin Spivey ◽  
Anthony Vodacek
Keyword(s):  
Factor Analysis ◽  
Land Cover ◽  
Landscape Pattern ◽  
Explanatory Power ◽  
United States Geological Survey ◽  
Landscape Pattern Metrics ◽  
Pattern Metrics ◽  
Pattern Information ◽  
Pattern Variability ◽  
Cover Pattern

Abstract A factor analysis of 67 landscape pattern metrics was performed to quantify the ability of landscape pattern metrics to explain land cover pattern, and to report individual landscape pattern metric values that are statistically independent. This land cover pattern is measured from 7.68 x 7.68 [km] GeoTiff image tiles of the conterminous United States Geological Survey (USGS) 1992 National Land Cover Dataset (NCLD). Using factor analysis to rank independent landscape pattern information, each landscape pattern metric produces the explanatory power of that landscape pattern metric amongst the other 66 landscape pattern metrics—any landscape pattern metrics that report similar values contribute redundant information. The metrics that contribute the most information are Jackson’s Contagion statistic (P005), typically contributing to 97 % of the explained variability; the Fourier Metric of Fragmentation (FMF), typically contributing to 65 % of the explained variability; and average LCLU class lacunarity (TLAC), typically contributing to 62 % of the explained variability. Two other Fourier-based landscape pattern metrics we tested, the Least Squares Fourier Transform Fractal Dimension Estimation (LsFT) and the Fourier Metric of Proportion (FMP), contributed 50 % and 12 % to the explained variability, respectively. In addition, the values reported by each of the Fourier metrics are revealed to be relatively independent amongst commonly used landscape pattern metrics and are thus demonstrated to be appropriate for explaining general landscape pattern variability.

scholarly journals Analyzing land cover change using remote sensing and gis methods in the Carsamba Plain and its surroundings (1985-2013)Çarşamba Ovası ve yakın çevresinde arazi örtüsü değişiminin uzaktan algılama ve coğrafi bilgi sistemleriyle analizi (1985-2013)

2016 ◽  
Vol 13 (3) ◽  
pp. 5551
Author(s):  
Ayse Atalay Dutucu ◽  
Cercis İkiel
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Land Cover ◽  
Satellite Image ◽  
Geographic Information ◽  
Population Increase ◽  
Tm Image ◽  
Landsat 5 Tm ◽  
Gis Methods

The aim of this study is to analyze the change of land cover of Çarşamba Plain and its surroundings by remote sensing and geographic information systems. For this purpose, Landsat 5 TM image at a resolution of 30 km for the year 1985, and the RapidEye satellite image at 6.5 m. resolution,for the year 2013 is used. These images were analyzed by geographic information systems via on-screen digitization method. According to the results obtained, significant changes in the land cover of the survey area between 1985-2013 have been determined. These findings were compared with the Land Surveillance System, the National Land Cover Database, which was created by the T.C Forest and Water Affairs Ministry and similar results were obtained. It has ben determined that there ise a change in decreasing direction in the forest and pasture areas in the examined period. Also beach areas is getting smaller because of coastal eroision. However, settlement areas expanded due to population increase in the same period. ÖzetBu çalışmanın amacı Çarşamba Ovası ve yakın çevresinde arazi örtüsü değişimini uzaktan algılama ve coğrafi bilgi sistemleri kullanılarak analiz etmektir. Bu amaç doğrultusunda 1985 yılı için 30 km çözünürlüğünde Landsat 5 TM, 2013 yılı için 6.5 m. çözünürlüğünde RapidEye uydu görüntüsü kullanılmıştır. Bu görüntüler coğrafi bilgi sistemleri ile ekran üzerinden sayısallaştırma yöntemiyle analiz edilmiştir. Elde edilen sonuçlara göre, 1985-2013 yılları arasında araştırma alanının arazi örtüsünde önemli değişiklikler tespit edilmiştir. Bu bulgular, T.C Orman ve Su İşleri Bakanlığı tarafından oluşturulan Arazi İzleme Sistemi, Ulusal Arazi Örtüsü veri tabanı ile karşılaştırılmıştır.

Heterogeneity of Cerebral Blood Flow: a Fractal Approach

2000 ◽  
Vol 39 (02) ◽  
pp. 37-42 ◽  
Author(s):  
P. Hartikainen ◽  
J. T. Kuikka
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Fractal Dimension ◽  
Frontal Lobe ◽  
Fractal Analysis ◽  
Relative Dispersion ◽  
Emission Computed Tomography ◽  
Brain Blood Flow ◽  
Healthy Controls ◽  
Fractal Approach

Summary Aim: We demonstrate the heterogeneity of regional cerebral blood flow using a fractal approach and singlephoton emission computed tomography (SPECT). Method: Tc-99m-labelled ethylcysteine dimer was injected intravenously in 10 healthy controls and in 10 patients with dementia of frontal lobe type. The head was imaged with a gamma camera and transaxial, sagittal and coronal slices were reconstructed. Two hundred fifty-six symmetrical regions of interest (ROIs) were drawn onto each hemisphere of functioning brain matter. Fractal analysis was used to examine the spatial heterogeneity of blood flow as a function of the number of ROIs. Results: Relative dispersion (= coefficient of variation of the regional flows) was fractal-like in healthy subjects and could be characterized by a fractal dimension of 1.17 ± 0.05 (mean ± SD) for the left hemisphere and 1.15 ± 0.04 for the right hemisphere, respectively. The fractal dimension of 1.0 reflects completely homogeneous blood flow and 1.5 indicates a random blood flow distribution. Patients with dementia of frontal lobe type had a significantly lower fractal dimension of 1.04 ± 0.03 than in healthy controls. Conclusion: Within the limits of spatial resolution of SPECT, the heterogeneity of brain blood flow is well characterized by a fractal dimension. Fractal analysis may help brain scientists to assess age-, sex- and laterality-related anatomic and physiological changes of brain blood flow and possibly to improve precision of diagnostic information available for patient care.

Optimal Density Determination of Bouguer Anomaly Using Fractal Analysis (Case of study: Charak area,IRAN)

2005 ◽  
Vol 1 (1) ◽  
pp. 21-24
Author(s):  
Hamid Reza Samadi
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Fractal Analysis ◽  
Fractal Geometry ◽  
Host Rock ◽  
Bouguer Anomaly ◽  
Research Area ◽  
Density Difference ◽  
Optimal Density

In exploration geophysics the main and initial aim is to determine density of under-research goals which have certain density difference with the host rock. Therefore, we state a method in this paper to determine the density of bouguer plate, the so-called variogram method based on fractal geometry. This method is based on minimizing surface roughness of bouguer anomaly. The fractal dimension of surface has been used as surface roughness of bouguer anomaly. Using this method, the optimal density of Charak area insouth of Hormozgan province can be determined which is 2/7 g/cfor the under-research area. This determined density has been used to correct and investigate its results about the isostasy of the studied area and results well-coincided with the geology of the area and dug exploratory holes in the text area

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

SateLoc: A Virtual Fingerprinting Approach to Outdoor LoRa Localization Using Satellite Images

2021 ◽  
Vol 17 (4) ◽  
pp. 1-28
Author(s):  
Yuxiang Lin ◽  
Wei Dong ◽  
Yi Gao ◽  
Tao Gu
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Images ◽  
Low Cost ◽  
Tracking Error ◽  
Location Based Services ◽  
Distance Constraint ◽  
Combination Strategy ◽  
Land Cover Types ◽  
High Resolution Satellite Images

With the increasing relevance of the Internet of Things and large-scale location-based services, LoRa localization has been attractive due to its low-cost, low-power, and long-range properties. However, existing localization approaches based on received signal strength indicators are either easily affected by signal fading of different land-cover types or labor intensive. In this work, we propose SateLoc, a LoRa localization system that utilizes satellite images to generate virtual fingerprints. Specifically, SateLoc first uses high-resolution satellite images to identify land-cover types. With the path loss parameters of each land-cover type, SateLoc can automatically generate a virtual fingerprinting map for each gateway. We then propose a novel multi-gateway combination strategy, which is weighted by the environmental interference of each gateway, to produce a joint likelihood distribution for localization and tracking. We implement SateLoc with commercial LoRa devices without any hardware modification, and evaluate its performance in a 227,500-m urban area. Experimental results show that SateLoc achieves a median localization error of 43.5 m, improving more than 50% compared to state-of-the-art model-based approaches. Moreover, SateLoc can achieve a median tracking error of 37.9 m with the distance constraint of adjacent estimated locations. More importantly, compared to fingerprinting-based approaches, SateLoc does not require the labor-intensive fingerprint acquisition process.

scholarly journals Fractal Dimension and Retinal Pathology: A Meta-Analysis

2021 ◽  
Vol 11 (5) ◽  
pp. 2376
Author(s):  
Sam Yu ◽  
Vasudevan Lakshminarayanan
Keyword(s):  
Fractal Dimension ◽  
Fractal Analysis ◽  
Meta Analysis ◽  
Scientific Evidence ◽  
Retinal Disease ◽  
Clinical Value ◽  
Disease Category ◽  
Normal Individuals ◽  
Retinal Pathology ◽  
Meta Analyses

Due to the fractal nature of retinal blood vessels, the retinal fractal dimension is a natural parameter for researchers to explore and has garnered interest as a potential diagnostic tool. This review aims to summarize the current scientific evidence regarding the relationship between fractal dimension and retinal pathology and thus assess the clinical value of retinal fractal dimension. Following the PRISMA guidelines, a literature search for research articles was conducted in several internet databases (EMBASE, MEDLINE, Web of Science, Scopus). This led to a result of 28 studies included in the final review, which were analyzed via meta-analysis to determine whether the fractal dimension changes significantly in retinal disease versus normal individuals. From the meta-analysis, summary effect sizes and 95% confidence intervals were derived for each disease category. The results for diabetic retinopathy and myopia suggest decreased retinal fractal dimension for those pathologies with the association for other diseases such as diabetes mellitus, hypertension, and glaucoma remaining uncertain. Due to heterogeneity in imaging/fractal analysis setups used between studies, it is recommended that standardized retinal fractal analysis procedures be implemented in order to facilitate future meta-analyses.

Sign in / Sign up

Export Citation Format

Share Document