scholarly journals COMPARISON BETWEEN SPECTRAL, SPATIAL AND POLARIMETRIC CLASSIFICATION OF URBAN AND PERIURBAN LANDCOVER USING TEMPORAL SENTINEL – 1 IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 789-796 ◽  
Author(s):  
K. Roychowdhury
Keyword(s):  
Classification Accuracy ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Unsupervised Classification ◽  
Landsat 8 ◽  
Open Spaces ◽  
Suburban Areas ◽  
Medium Resolution

Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

scholarly journals COMPARISON BETWEEN SPECTRAL, SPATIAL AND POLARIMETRIC CLASSIFICATION OF URBAN AND PERIURBAN LANDCOVER USING TEMPORAL SENTINEL – 1 IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 789-796 ◽  
Author(s):  
K. Roychowdhury
Keyword(s):  
Classification Accuracy ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Unsupervised Classification ◽  
Landsat 8 ◽  
Open Spaces ◽  
Suburban Areas ◽  
Medium Resolution

Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

scholarly journals NDVI Based Assessment of Land Cover Changes Using Remote Sensing and GIS (A case study of Srinagar district, Kashmir)

2020 ◽  
Author(s):  
Perminder Singh ◽  
Ovais Javeed
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Spectral Signature ◽  
Landsat 8 ◽  
Thematic Mapping

Normalized Difference Vegetation Index (NDVI) is an index of greenness or photosynthetic activity in a plant. It is a technique of obtaining  various features based upon their spectral signature  such as vegetation index, land cover classification, urban areas and remaining areas presented in the image. The NDVI differencing method using Landsat thematic mapping images and Landsat oli  was implemented to assess the chane in vegetation cover from 2001to 2017. In the present study, Landsat TM images of 2001 and landsat 8 of 2017 were used to extract NDVI values. The NDVI values calculated from the satellite image of the year 2001 ranges from 0.62 to -0.41 and that of the year 2017 shows a significant change across the whole region and its value ranges from 0.53 to -0.10 based upon their spectral signature .This technique is also  used for the mapping of changes in land use  and land cover.  NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as -0.1, -0.09, 0.14, 0.06, 0.28, 0.35, and 0.5. The NDVI values were initially computed using the Natural Breaks (Jenks) method to classify NDVI map. Results confirmed that the area without vegetation, such as water bodies, as well as built up areas and barren lands, increased from 35 % in 2001 to 39.67 % in 2017.Key words: Normalized Difference Vegetation Index,land use/landcover, spectral signature 

scholarly journals Influence of the use and the land cover of the catchment in the water quality of the semiarid tropical reservoirs

2018 ◽  
Vol 7 (7) ◽  
pp. 389
Author(s):  
Herika Cavalcante ◽  
Patrícia Silva Cruz ◽  
Leandro Gomes Viana ◽  
Daniely De Lucena Silva ◽  
José Etham De Lucena Barbosa
Keyword(s):  
Water Quality ◽  
Supervised Classification ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Dissolved Phosphorus ◽  
Sparse Vegetation ◽  
Tropical Reservoirs

The aim of this study was to evaluate some parameters of water quality of semiarid reservoirs under different uses and occupation of the catchment’s soil. For this, the reservoirs Acauã and Boqueirão, belonging to the Paraíba do Norte river watershed and Middle and Upper course sub catchments, respectively, were studied. For this, water samples were collected in August, September and October 2016. From these samples, total and dissolved phosphorus, nitrate, nitrite, ammonia, chlorophyll, dissolved and suspended solids were analyzed. In addition, images of the Landsat 8 satellite were acquired for the calculation of the Normalized Difference Vegetation Index (NDVI), and for the supervised classification of the use and occupation of the sub catchments. Thus, it was observed that, in general, the Acauã reservoir presented values of phosphorus and nitrogen and solids larger than the Boqueirão reservoir, due to the greater urban area, even though it had a smaller total area of the basin. Both reservoirs presented low vegetation rates and high areas of sparse vegetation and exposed soil, increasing the propensity to soil erosion and the transport of nutrients from the basin to the reservoirs, making water quality worse or impossible.

scholarly journals The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 139 ◽  
Author(s):  
Yingying Yang ◽  
Taixia Wu ◽  
Shudong Wang ◽  
Jing Li ◽  
Farhan Muhanmmad
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Distribution Data ◽  
Landsat 8 ◽  
Oxygen Balance

Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVIann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVIann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVIann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability.

scholarly journals ANALISIS VEGETASI DI TAMAN WISATA ALAM (TWA) SUNGAI DUMAI, RIAU

2021 ◽  
Vol 16 (1) ◽  
pp. 25-36
Author(s):  
Hanifah Ikhsani
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Vegetation Density ◽  
Vegetation Analysis ◽  
Landsat 8 Oli ◽  
Density Class ◽  
Vegetation Class ◽  
Do So

TWA Sungai Dumai is a tourist forest area and ensuring the preservation of natural potential. However, there are problems that can disrupt the sustainability of it, including forest and land fires and conversion of land use to agriculture and oil palm plantations. Until now, there is no vegetation analysis using satellite imagery in TWA Sungai Dumai, so it is important to do so that can be managed sustainably. This study  classification of vegetation density classes which are presented in the form of a vegetation density class map in it. This research uses Landsat-8 OLI / TIRS images from October 2017 and October 2020 which are processed to determine density class using Normalized Difference Vegetation Index algorithm. The vegetation density class with the highest area in 2017 was the vegetation density class (2380,832 ha or 66,819% of the total area), while the lowest area was the non-vegetation class (75,737 ha or 2,126% of the total area). The vegetation density class with the highest area in 2020 in TWA Sungai Dumai is dense vegetation density class (3205,039 ha or 89,950% of the total area), while the lowest area is non-vegetation class (1,637 ha or 0.046% of the total area)

scholarly journals Identification of croplands of winter cereals in Rio Grande do Sul state, Brazil, through unsupervised classification of normalized difference vegetation index images

2013 ◽  
Vol 33 (4) ◽  
pp. 883-895 ◽  
Author(s):  
Amanda H. Junges ◽  
Denise C. Fontana ◽  
Daniele G. Pinto
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rio Grande ◽  
Unsupervised Classification ◽  
Grain Production ◽  
Rio Grande Do Sul ◽  
Winter Cereal ◽  
Winter Cereals ◽  
Temporal Profiles

This study aimed to propose methods to identify croplands cultivated with winter cereals in the northern region of Rio Grande do Sul State, Brazil. Thus, temporal profiles of Normalized Difference Vegetation Index (NDVI) from MODIS sensor, from April to December of the 2000 to 2008, were analyzed. Firstly, crop masks were elaborated by subtracting the minimum NDVI image (April to May) from the maximum NDVI image (June to October). Then, an unsupervised classification of NDVI images was carried out (Isodata), considering the crop mask areas. According to the results, crop masks allowed the identification of pixels with greatest green biomass variation. This variation might be associated or not with winter cereals areas established to grain production. The unsupervised classification generated classes in which NDVI temporal profiles were associated with water bodies, pastures, winter cereals for grain production and for soil cover. Temporal NDVI profiles of the class winter cereals for grain production were in agree with crop patterns in the region (developmental stage, management standard and sowing dates). Therefore, unsupervised classification based on crop masks allows distinguishing and monitoring winter cereal crops, which were similar in terms of morphology and phenology.

scholarly journals Remote Sensing of the Earth as a Method of Assessing Environmental Quality of Urban Areas

2020 ◽  
pp. 5-12
Author(s):  
S.A. Yeprintsev ◽  
O.V. Klepikov ◽  
S.V. Shekoyan
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
Urban Areas ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Space Images ◽  
The Earth ◽  
Voronezh Region

Introduction: Spatial zoning of an urban area by the level of anthropogenic burden using land-based research methods is very time-consuming. Since the end of the 20th century, the usage of the Earth remote sensing (ERS) techniques has served as their more efficient alternative. The study objectives included geoinformation zoning and evaluation of the level of technogenic changes in the areas according to NDVI (Normalized Difference Vegetation Index) values. Materials and methods: The cities of the Voronezh Region and their suburban ten-kilometer territories were chosen as the study objects. For the spatial analysis of the area of anthropogenically modified territories based on the example of the cities of the Voronezh Region we created an archive of multichannel satellite images taken by the Landsat-7 and Landsat-8 satellites. The data were borrowed from the Website of the US Geological Survey. Space images were grouped by two periods (the years of 2001 and 2016). Depending on NDVI values, territories with high and low anthropogenic burden, natural framework zones, and water bodies were distinguished. Results: We established that the smallest percentage of areas of the natural framework and their poor location was observed in the city of Voronezh. The largest area occupied by the natural framework was identified within the town of Borisoglebsk. This fact is attributed to the sensible policy of ensuring environmental and hygienic safety of the population implemented by the regional and municipal authorities. Discussion: At present, it is still impossible to fully use space monitoring data to assess health risks of technogenic factors; they can only be used simultaneously with ground monitoring that includes instrumental and laboratory monitoring of environmental quality indicators within the framework of the socio-hygienic monitoring. Conclusions: The analysis of changes in the proportion of areas with a high anthropogenic burden relative to the natural framework performed using satellite images taken in 2001 and 2016 showed an increase in the technogenic burden on the urban environment.

scholarly journals A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 389-394 ◽  
Author(s):  
R. Bala ◽  
R. Prasad ◽  
V. P. Yadav ◽  
J. Sharma
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sensor Data ◽  
Landsat 8 ◽  
Temperature Relation

<p><strong>Abstract.</strong> The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body.</p>

scholarly journals The Relationship between the Mixed Pixel Spectral Value of Landsat 8 OLI Data and LAPAN Surveillance Aircraft (LSA) Aerial-Photo Data

2017 ◽  
Vol 31 (1) ◽  
pp. 83-98
Author(s):  
Nurwita Mustika Sari ◽  
Galdita Aruba Chulafak ◽  
Zylshal Zylshal ◽  
Dony Kushardono
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Actual Condition ◽  
Landsat 8 ◽  
Landsat Data ◽  
Aerial Photo ◽  
Visual Interpretation ◽  
Mixed Pixel ◽  
Multispectral Data ◽  
Medium Resolution

Medium resolution satellite data such as Landsat is very potential for mixed pixel (mixel) to occur. Indonesian land use diverse especially urban areas makes high potential mixel in the first Landsat pixel size of 30 meters x 30 meters on the actual condition. Aircraft multispectral aerial photo data LAPAN Surveillance Aircraft (LSA) with a spatial resolution reached 58 cm can display objects in more detail in these sizes. The purpose of this research is to study mixel on Landsat data with multispectral data LSA as a complement Landsat data. The method proposed in this study is a visual interpretation with GEOBIA method for classification of land cover, and then test the validity of the sample to be used in research, and the use of such vegetation index NDVI to see the connection between vegetation index data of vegetation index LSA with Landsat data. The results showed that the regression equation obtained by regression between NDVI of Landsat data and NDVI of  LSA with a significance of less than 0.05 is y = 0.732x - 0102 with a value of R2 = 0.887. Through these results we can conclude that the NDVI values on both the data related to one another.

scholarly journals Annual cropland mapping using data mining and OLI Landsat-8

2019 ◽  
Vol 23 (12) ◽  
pp. 952-958 ◽  
Author(s):  
Lucas V. Oldoni ◽  
Carlos E. V. Cattani ◽  
Erivelto Mercante ◽  
Jerry A. Johann ◽  
João F. G. Antunes ◽  
...  
Keyword(s):  
Data Mining ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Environmental Laws ◽  
Annual Crops ◽  
New Methodologies ◽  
Using Data ◽  
Global Accuracy

ABSTRACT In the state of Paraná, Brazil, there are no major changes in areas cultivated with annual crops, mainly due to environmental laws that do not allow expansions to new areas. There is a great contribution of the annual crops to the domestic demand of food and economic demand in the exports. Thus, the area and distribution of annual crops are information of great importance. New methodologies, such as data mining, are being tested with the objective of analyzing and improving their potential use for classification of land use and land cover. This study used the classifiers decision tree and random forest with Normalized Difference Vegetation Index (NDVI) temporal metrics on images from Operational Land Imager (OLI)/Landsat-8. The results were compared with traditional methods spectral images and Maximum Likelihood Classifier (MLC). At first, seven classes were mapped (water bodies, sugarcane, urban area, annual crops, forest, pasture and reforestation areas); then, only two classes were considered (annual crops and other targets). When classifying the seven targets, both methods had corresponding results, showing global accuracy near 84%. NDVI temporal metrics showed producer’s and user’s accuracy for the annual crop class of 86 and 100%, respectively. However, if considering only two classes, the NDVI temporal metrics reached global accuracy of near 98% and producer’s and user’s accuracy above 94%.

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