scholarly journals UAV Based Agricultural Crop Canopy Mapping for Crop Field Monitoring

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Prabath Priyankara ◽  
Takehiro Morimoto
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Field Monitoring ◽  
Decision Makers ◽  
Agricultural Crop ◽  
Crop Canopy ◽  
Crop Fields ◽  
Crop Field ◽  
Multi Temporal

<p><strong>Abstract.</strong> Nowadays, mapping of agricultural crop canopy in different growing stages are vital data for crop field monitoring than field-based observations in large scale agricultural crop fields. By mapping agricultural crop canopy, it is very easy to analyse the status of an agricultural crop field by using different vegetation indices. Further, the data can be used to estimate the yield. These information are timely and reliable spatial information to the farmers and decision makers. Mapping of crop canopy in an agricultural crop field in different growing stages are very challenging using satellite imagery mainly due to the difficulty of recording with high cloud coverage. Also, the cost for satellite imagery are higher in proportion to the spatial resolution. It takes some time to order a satellite imagery and sometimes can’t cover some growing stages. This problem can be solved by using low cost RGB based UAV imageries which can be operated at low altitudes (below the clouds) which and when necessary. This study is therefore aimed at mapping of a maize crop canopy using RGB based UAV imageries. UAV flights at different growth stages were carried out with a high resolution RGB camera over a maize field in Ampara District, Sri Lanka. For accurate crop canopy mapping, very high-resolution multi-temporal ortho-mosaicked images were derived from UAV imageries using free and open source image processing platforms with spatial resolution in centimetre level. The resultant multi-temporal ortho-mosaicked images can be used to map and monitor the crop field’s precise and efficient manner. These information are very important for farmers and decision makers to properly manage the crop fields.</p>

Photogrammetric processing and application of multi-temporal satellite imagery stereopairs

2021 ◽  
Vol 974 (8) ◽  
pp. 36-44
Author(s):  
R.V. Permyakov
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Weather Conditions ◽  
3D Models ◽  
High Resolution Imagery ◽  
Multi Temporal ◽  
Unfavourable Weather ◽  
High Resolution Satellite Imagery ◽  
Very High Resolution Imagery ◽  
Very High

Stereopairs of very-high resolution satellite imagery constitute one of the key high-accurate data sources on heights. A stereophotogrammetric technique is a key method of processing these data. Despite that a number of spacecrafts gathering very-high-resolution imagery in a stereo mode constantly increases, the area of the Earth regularly covered by such data and stored in the archives of RSD operators remains relatively small and, as a rule, is limited only to large urban agglomerations. The new collection may not suit the customer for several reasons. Firstly, the materials of the new stereo collection are more expensive than those of the archived one. Secondly, due to unfavourable weather conditions and a busy schedule of satellites, the completion of the new collection may go beyond the deadline specified by the customer. Well known and brand-new criteria to form multi-temporal, stereopairs are analyzed. The specific of photogrammetric processing multi-temporal stereopairs is demonstrated. Application of multi-temporal stereopairs is described. In conclusion it is confirmed that 3D-models and high accurate DTMs can be generated basing on stereo models from multi-temporal satellite imagery in the absence of the following data

scholarly journals A global approach to estimate irrigated areas – a comparison between different data and statistics

2018 ◽  
Vol 22 (2) ◽  
pp. 1119-1133 ◽  
Author(s):  
Jonas Meier ◽  
Florian Zabel ◽  
Wolfram Mauser
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
Decision Makers ◽  
Data Sets ◽  
Ancillary Data ◽  
Irrigated Area ◽  
Time Period ◽  
Multi Temporal

Abstract. Agriculture is the largest global consumer of water. Irrigated areas constitute 40 % of the total area used for agricultural production (FAO, 2014a) Information on their spatial distribution is highly relevant for regional water management and food security. Spatial information on irrigation is highly important for policy and decision makers, who are facing the transition towards more efficient sustainable agriculture. However, the mapping of irrigated areas still represents a challenge for land use classifications, and existing global data sets differ strongly in their results. The following study tests an existing irrigation map based on statistics and extends the irrigated area using ancillary data. The approach processes and analyzes multi-temporal normalized difference vegetation index (NDVI) SPOT-VGT data and agricultural suitability data – both at a spatial resolution of 30 arcsec – incrementally in a multiple decision tree. It covers the period from 1999 to 2012. The results globally show a 18 % larger irrigated area than existing approaches based on statistical data. The largest differences compared to the official national statistics are found in Asia and particularly in China and India. The additional areas are mainly identified within already known irrigated regions where irrigation is more dense than previously estimated. The validation with global and regional products shows the large divergence of existing data sets with respect to size and distribution of irrigated areas caused by spatial resolution, the considered time period and the input data and assumption made.

scholarly journals Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

2019 ◽  
Vol 12 (1) ◽  
pp. 81 ◽  
Author(s):  
Xinghua Li ◽  
Zhiwei Li ◽  
Ruitao Feng ◽  
Shuang Luo ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Remote Sensing Images ◽  
High Quality ◽  
Geographical Maps

Urban geographical maps are important to urban planning, urban construction, land-use studies, disaster control and relief, touring and sightseeing, and so on. Satellite remote sensing images are the most important data source for urban geographical maps. However, for optical satellite remote sensing images with high spatial resolution, certain inevitable factors, including cloud, haze, and cloud shadow, severely degrade the image quality. Moreover, the geometrical and radiometric differences amongst multiple high-spatial-resolution images are difficult to eliminate. In this study, we propose a robust and efficient procedure for generating high-resolution and high-quality seamless satellite imagery for large-scale urban regions. This procedure consists of image registration, cloud detection, thin/thick cloud removal, pansharpening, and mosaicking processes. Methodologically, a spatially adaptive method considering the variation of atmospheric scattering, and a stepwise replacement method based on local moment matching are proposed for removing thin and thick clouds, respectively. The effectiveness is demonstrated by a successful case of generating a 0.91-m-resolution image of the main city zone in Nanning, Guangxi Zhuang Autonomous Region, China, using images obtained from the Chinese Beijing-2 and Gaofen-2 high-resolution satellites.

Sign in / Sign up

Export Citation Format

Share Document