Land cover changed object detection in remote sensing data with medium spatial resolution

2015 ◽  
Vol 38 ◽  
pp. 129-137 ◽  
Author(s):  
Xiao tong Yang ◽  
Huiping Liu ◽  
Xiaofeng Gao
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Object Detection ◽  
Spatial Resolution ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?

2020 ◽  
Vol 12 (3) ◽  
pp. 417 ◽  
Author(s):  
Xin Zhang ◽  
Liangxiu Han ◽  
Lianghao Han ◽  
Liang Zhu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Land Cover ◽  
Object Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Sensing Data

Land cover information plays an important role in mapping ecological and environmental changes in Earth’s diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from Space. Although the availability of high-resolution remote sensing imagery increases significantly every year, traditional land cover analysis approaches based on pixel and object levels are not optimal. Recent advancement in deep learning has achieved remarkable success on image recognition field and has shown potential in high spatial resolution remote sensing applications, including classification and object detection. In this paper, a comprehensive review on land cover classification and object detection approaches using high resolution imagery is provided. Through two case studies, we demonstrated the applications of the state-of-the-art deep learning models to high spatial resolution remote sensing data for land cover classification and object detection and evaluated their performances against traditional approaches. For a land cover classification task, the deep-learning-based methods provide an end-to-end solution by using both spatial and spectral information. They have shown better performance than the traditional pixel-based method, especially for the categories of different vegetation. For an objective detection task, the deep-learning-based object detection method achieved more than 98% accuracy in a large area; its high accuracy and efficiency could relieve the burden of the traditional, labour-intensive method. However, considering the diversity of remote sensing data, more training datasets are required in order to improve the generalisation and the robustness of deep learning-based models.

scholarly journals ESTIMATION OF GROSS PRIMARY PRODUCTION USING SATELLITE DATA AND GIS IN URBAN AREA, DENPASAR

2011 ◽  
Vol 7 (1) ◽  
Author(s):  
A.R. As-syakur ◽  
T. Osawa ◽  
IW.S. Adnyana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Primary Production ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
Gross Primary Production ◽  
Remote Sensing Data ◽  
Sensing Data

Remote sensing data with high spatial resolution is very useful to provideinformation about Gross Primary Production (GPP) especially over spatial coverage in theurban area. Most models of ecosystem carbon exchange based on remote sensing data usedlight use efficiency (LUE) model. The aim of this research was to analyze the distributionof annual GPP urban area of Denpasar. Two main satellite data used in this study wereALOS/AVNIR-2 and Aster satellite data. Result showed that annual value of GPP usingALOS/AVNIR-2 varied from 0.130 gC m-2 yr-1 to 2586.181 gC m-2 yr-1. Meanwhile, usingAster the value varied from 0.144 gC m-2 yr-1 to 2595.264 gC m-2 yr-1. The annual value ofGPP ALOS was lower than the value of Aster, because ALOS have high spatial resolutionand smaller interval of spectral resolution compared to Aster. Different land use couldeffect the value of GPP, because the different land use has different vegetation type,distribution, and different photosynthetic pathway type. The high spatial resolution of theremote sensing data is crucial to discriminate different land cover types in urban region.With heterogeneous land cover surface, maximum value of GPP using ALOS/AVNIR-2was smaller than that of Aster, however, the annual mean of GPP value usingALOS/AVNIR-2 was higher than that of Aster.

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

scholarly journals Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4483
Author(s):  
W. Gareth Rees ◽  
Jack Tomaney ◽  
Olga Tutubalina ◽  
Vasily Zharko ◽  
Sergey Bartalev
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Boreal Forest ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Fundamental Parameter ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

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