scholarly journals Estimation of Spatial Distribution of Vegetation Amount in a Watershed Using Landsat-5 TM Image Data

1994 ◽  
Vol 38 ◽  
pp. 777-782
Author(s):  
Masahiro SEGUCHI
Keyword(s):  
Spatial Distribution ◽  
Image Data ◽  
Tm Image ◽  
Landsat 5 Tm

scholarly journals Identificação de Cicatrizes de Movimentos de Massa na Borda Oriental da Bacia do Paraná com base em Imagens Landsat 5-TM (Identification of Landslides Scars in the Eastern Edge of the Paraná Basin)

2015 ◽  
Vol 8 (1) ◽  
pp. 056
Author(s):  
Mariana Madruga De Brito
Keyword(s):  
Image Processing ◽  
Mass Movement ◽  
Google Earth ◽  
Economic Losses ◽  
Mass Movements ◽  
Tm Image ◽  
Paraná Basin ◽  
Landsat 5 Tm ◽  
Parana Basin ◽  
Eastern Edge

Movimentos de massa são fenômenos naturais caracterizados pelo deslocamento de solo e rocha vertente abaixo. Quando esses processos ocorrem em áreas urbanizadas, podem causar perdas econômicas, impactos sociais e, em casos extremos, perda de vidas humanas. Na tentativa de mitigar tais desastres, torna-se necessário mapear os locais já afetados pelos mesmos, uma vez que escorregamentos recentes podem sugerir futuros padrões de instabilidade. Nesse sentido, este trabalho teve por objetivo mapear as cicatrizes de movimentos de massa em um segmento da Borda Oriental da Bacia do Paraná, por meio do processamento de uma imagem Landsat 5-TM, órbita/ponto 220/80, data de passagem 28/08/2009. Para facilitar a discriminação destas feições, elaboraram-se composições coloridas RGB e processamentos tais como Ampliação Linear de Contraste (ALC), razão entre bandas e Análise por Componentes Principais (PCA). A fim de validar o inventário elaborado, utilizaram-se imagens de alta resolução disponíveis no software Google Earth®. Ao total, foram identificadas 63 cicatrizes com a imagem Landsat 5-TM e 121 cicatrizes com as imagens do Google Earth®. Os principais tipos de movimentos de massa mapeados são escorregamentos translacionais rasos e corrida de detritos. A identificação das cicatrizes foi possível devido às diferenças de tonalidade, cor, matiz e textura nas imagens orbitais após a ocorrência desses processos. Os resultados obtidos representam um passo inicial para a análise da suscetibilidade da área.     A B S T R A C T Mass movements are natural phenomena characterized by the downslope movement of soil and rock. When these processes occur in urban areas they can cause economic losses, social impacts and, in extreme cases, loss of human lives. In an attempt to mitigate such disasters, it is necessary to map sites affected by them, since recent landslides may suggest future patterns of instability. In this sense, this study aimed to map the mass movement scars in a segment of the Eastern Edge of the Paraná Basin, through the processing of a Landsat 5-TM image, 220/80 orbit-point and date of passage 08/28/2009. To facilitate the recognition of these features, RGB color compositions and image processing techniques such as contrast stretching, ratio between bands and Principal Component Analysis (PCA) were applied. In order to validate the inventory, high-resolution images available at Google Earth® software were used. Totally, 63 scars were identified with the Landsat 5-TM image and 121 with Google Earth® images. The main types of mass movements mapped are translational landslides and debris flows. The identification of the scars was possible due to differences in tone, color, hue and texture at the orbital images after the occurrence of such processes. Results obtained represent an initial step towards the susceptibility analysis of the area. Keywords: Landslide inventory; Digital image processing; Satellite images; Geoprocessing.  

scholarly journals Measuring Community Greening Merging Multi-Source Geo-Data

2019 ◽  
Vol 11 (4) ◽  
pp. 1104 ◽  
Author(s):  
Weiying Gu ◽  
Yiyong Chen ◽  
Muye Dai
Keyword(s):  
Spatial Distribution ◽  
Spatial Relationship ◽  
Image Data ◽  
Urban Land Use ◽  
Residential Areas ◽  
Residential Community ◽  
Shenzhen City ◽  
Spatial Distribution Characteristics ◽  
Residential Units ◽  
Green Land

Urban residential greening provides opportunities for social integration and physical exercise. These activities are beneficial to promoting citizens’ mental health, relieving stress, and reducing obesity and violent crimes. However, how to measure the distribution and spatial difference of green resources in urban residential areas have been controversial. This study takes the greening of urban residential units in Shenzhen City as its research object, measures the various greening index values of each residential unit, and analyses the spatial distribution characteristics of residential greening, regional differences, and influencing factors. A large sample of street view pictures, urban land use and high-resolution remote sensing image data are employed to establish an urban residential greening database containing 14,196 residential units. This study proposes three greening indicators, namely, green coverage index, green view index, and accessible public green land index, for measuring the green coverage of residential units, the visible greening of surrounding street space and the public green land around, respectively. Results show that (1) the greening level of residential units in Shenzhen City is generally high, with the three indicators averaging 32.7%, 30.5%, and 15.1%, respectively; (2) the types of residential greening differ per area; and (3) the level of residential greening is affected by development intensity, location, elevation and residential type. Such findings can serve as a reference for improving the greening level of residential units. This study argues that one indicator alone cannot measure the greenness of a residential community. It proposes an accessible public green land index as a measure for the spatial relationship between residential units and green lands. It suggests that future green space planning should pay more attention to the spatial distribution of green land, and introduce quantitative indicators to ensure sufficient green lands around the walking range of residential areas.

Estimation and Study on Forest Water Conservation Ecological Benefit

2013 ◽  
Vol 295-298 ◽  
pp. 2351-2354
Author(s):  
Lin Meng ◽  
Xue Gang Mao ◽  
Chang Zhai ◽  
Li Na Wang
Keyword(s):  
Water Conservation ◽  
Image Data ◽  
Remote Sensing Image ◽  
Test Accuracy ◽  
Structure Model ◽  
Multivariate Statistical ◽  
Ecological Benefit ◽  
Multivariate Statistical Model ◽  
Landsat 5 Tm ◽  
Coefficient Of Regression

By using Ecological Observatory Station survey data and Landsat-5 TM remote sensing image data in 2008 as the basic data to construct the conventional multivariate statistical model. The determination coefficient of regression model was 0.788 and the average test accuracy was 81.1%. By using the parameters of this model as the input data to Geographic Information System (GIS) to reveal the spatial distribution of quantity and value, then estimated the total forest water conservation quantity of Laoshan in 2008, I = 263.78 tons, established the currency structure model of the study area to estimate the total value quantity of water conservation effective, E = 1.3189 million yuan. And then analyzed the variation feature of quantity change with topographical, also, the variation fits the truth.

A wavelet theory based remote sensing inversion of chlorophyll a concentrations for inland lakes in arid areas using TM image data

2017 ◽  
Vol 37 (3) ◽  
Author(s):  
史锐 SHI Rui ◽  
张红 ZHANG Hong ◽  
岳荣 YUE Rong ◽  
张霄羽 ZHANG Xiaoyu ◽  
王美萍 WANG Meiping ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Image Data ◽  
Arid Areas ◽  
Wavelet Theory ◽  
Inland Lakes ◽  
Tm Image

scholarly journals Exploring the Distribution Patterns of Flickr Photos

2019 ◽  
Vol 8 (9) ◽  
pp. 418 ◽  
Author(s):  
Ding ◽  
Fan
Keyword(s):  
Spatial Distribution ◽  
Image Data ◽  
Data Retrieval ◽  
Distribution Patterns ◽  
Quality Analysis ◽  
Distribution Analysis ◽  
Data Filtering ◽  
Data Source ◽  
Quality Assessments

In recent years, volunteered-geographic-information (VGI) image data have served as a data source for various geographic applications, attracting researchers to assess the quality of these images. However, these applications and quality assessments are generally focused on images associated with geolocation through textual annotations, which is only part of valid images to them. In this paper, we explore the distribution pattern for most relevant VGI images of specific landmarks to extend the current quality analysis, and to provide guidance for improving the data-retrieval process of geographic applications. Distribution is explored in terms of two aspects, namely, semantic distribution and spatial distribution. In this paper, the term semantic distribution is used to describe the matching of building-image tags and content with each other. There are three kinds of images (semantic-relevant and content-relevant, semantic-relevant but content-irrelevant, and semantic-irrelevant but content-relevant). Spatial distribution shows how relevant images are distributed around a landmark. The process of this work can be divided into three parts: data filtering, retrieval of relevant landmark images, and distribution analysis. For semantic distribution, statistical results show that an average of 60% of images tagged with the building’s name actually represents the building, while 69% of images depicting the building are not annotated with the building’s name. There was also an observation that for most landmarks, 97% of relevant building images were located within 300 m around the building in terms of spatial distribution.

Tree species distribution and community structure of central Amazonian várzea forests by remote-sensing techniques

2002 ◽  
Vol 18 (6) ◽  
pp. 805-820 ◽  
Author(s):  
Florian Wittmann ◽  
Dieter Anhuf ◽  
Wolfgang J. Funk
Keyword(s):  
Species Diversity ◽  
Scale Up ◽  
Image Data ◽  
Behaviour Pattern ◽  
Forest Types ◽  
Successional Stage ◽  
Landsat Tm Image ◽  
Tm Image ◽  
Flood Level ◽  
Study Sites

In central Amazonian white-water floodplains (várzea), different forest types become established in relation to the flood-level gradient. The formations are characterized by typical patterns of species composition, and their architecture results in different light reflectance patterns, which can be detected by Landsat TM image data. Ground checking comprised a detailed forest inventory of 4 ha, with Digital Elevation Models (DEM) being generated for all sites. The results indicate that, at the average flood level of 3 m, species diversity and architecture of the forests changes, thus justifying the classification into the categories of low várzea (várzea baixa) and high várzea (várzea alta). In a first step to scale up, the study sites were observed by aerial photography. Tree heights, crown sizes, the projected crown area coverage and the gap frequencies provide information, which confirms a remotely sensed classification into three different forest types. The structure of low várzea depends on the successional stage, and species diversity increases with increasing age of the formations. In high várzea, only one successional stage was found and species diversity is higher than in all low-várzea formations. The more complex architecture of the high-várzea forest results in a more diffuse behaviour pattern in pixel distribution, when scanned by TM image data.

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