scholarly journals Mapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data

2015 ◽  
Vol 8 (1) ◽  
pp. 22 ◽  
Author(s):  
Changming Zhu ◽  
Dengsheng Lu ◽  
Daniel Victoria ◽  
Luciano Dutra
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Thematic Mapper ◽  
Landsat Thematic Mapper ◽  
Mato Grosso ◽  
Enhanced Vegetation Index ◽  
Thematic Mapper Data

scholarly journals Discrimination of Cabbage Fields for Detecting Clubroot Disease Damage Using Landsat Thematic Mapper Data.

1992 ◽  
Vol 61 (3) ◽  
pp. 527-535 ◽  
Author(s):  
Yoichi TORIGOE ◽  
Tetsuro AMANO ◽  
Kei OGAWA ◽  
Michikazu FUKUHARA
Keyword(s):  
Thematic Mapper ◽  
Landsat Thematic Mapper ◽  
Clubroot Disease ◽  
Thematic Mapper Data ◽  
Disease Damage

scholarly journals Characterization of Snow and Ice Reflectance Zones On Glaciers Using Landsat Thematic Mapper Data

1987 ◽  
Vol 9 ◽  
pp. 104-108 ◽  
Author(s):  
D.K. Hall ◽  
J.P. Ormsby ◽  
R.A. Bindschadler ◽  
H. Siddalingaiah
Keyword(s):  
Surface Temperature ◽  
Area Ratio ◽  
Thematic Mapper ◽  
Landsat Thematic Mapper ◽  
Ablation Area ◽  
Thematic Mapper Data ◽  
Glacier System ◽  
Accumulation Area Ratio ◽  
Snow And Ice

Landsat Thematic Mapper (TM) data have been analyzed to study the reflectivity characteristics of three glaciers: the Grossglockner mountain group of glaciers in Austria and the McCall and Meares Glaciers in Alaska, USA. The ratio of TM band 4 (0.76–0.90 μm) to TM band 5 (1.55–1.75 μm) was found to be useful for enhancing reflectivity differences on the glaciers. Using this ratio, distinct zones of similar reflectivity were noted on the Grossglockner mountain group of glaciers and on the Meares Glacier; no distinct zones were observed on the McCall Glacier. On the TM subscene containing the Grossglockner mountain group of glaciers, 28.2% of the glacierized area was determined to be in the zone corresponding most closely to the ablation area, and 71.8% with the location of the accumulation area. Using these measurements, the glacier system has an accumulation area ratio (AAR) of approximately 0.72. Within the accumulation area, two zones of different reflectivity were delineated. Radiometric surface temperatures were measured using TM band 6 (10.4–12.5 μm) on the Grossglockner mountain group of glaciers and on the Meares Glacier. The average radiometric surface temperature of the Grossglockner mountain group of glaciers decreased from 0.9 ± 0.34 °C in the ablation area, to −0.9 ± 0.83 C in the accumulation area.

scholarly journals Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

2019 ◽  
Vol 11 (24) ◽  
pp. 3023 ◽  
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Xiao Zhang ◽  
Jiangning Yang ◽  
Xidong Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Google Earth ◽  
Thematic Mapper ◽  
Series Data ◽  
Land Cover Mapping ◽  
Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

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