Emerging technologies for integrated nematode management: remote sensing or proximal sensing as a potential tool to detect and identify nematode infestation.

2021 ◽  
pp. 414-420
Author(s):  
Matheus T. Kuska ◽  
Matthias Daub ◽  
Anne-Katrin Mahlein
Keyword(s):  
Remote Sensing ◽  
Optical Sensors ◽  
Developmental Stages ◽  
Remote Sensing Data ◽  
Bursaphelenchus Xylophilus ◽  
Heterodera Schachtii ◽  
Proximal Sensing ◽  
The Difference ◽  
Sugarbeet Cyst Nematode ◽  
Physiological Reactions

Abstract Remote or proximal sensing defines the use of optical sensors, in combination with a carrier platform, to obtain information from objects in a non-invasive manner. Optical properties of plants provide valuable information on the health status, vitality or developmental stages of plants. The difference among remote-sensing and proximal-sensing technologies is mainly characterized by the distance between the measurement system and the object of interest. This chapter discusses physiological reactions influencing optical characteristics in nematode infested plants, remote sensing with satellites, the use of robots and drones for a more flexible infield assessment, as well as the analysis and interpretation of remote-sensing data. Some case studies with pine wood nematode (Bursaphelenchus xylophilus) and sugarbeet cyst nematode (Heterodera schachtii) are presented. Further use of remote and proximal sensing for the advancement of agriculture is also mentioned.

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 A data science challenge for converting airborne remote sensing data into ecological information

2018 ◽  
Author(s):  
Sergio Marconi ◽  
Sarah J. Graves ◽  
Dihong Gong ◽  
Morteza Shahriari Nia ◽  
Marion Le Bras ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Quantitative Methods ◽  
Data Science ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Species Classification ◽  
Ecological Information ◽  
The Difference ◽  
Segmentation Task ◽  
Individual Trees

Ecology has reached the point where data science competitions, in which multiple groups solve the same problem using the same data by different methods, will be productive for advancing quantitative methods for tasks such as species identification from remote sensing images. We ran a competition to help improve three tasks that are central to converting images into information on individual trees: 1) crown segmentation, for identifying the location and size of individual trees; 2) alignment, to match ground truthed trees with remote sensing; and 3) species classification of individual trees. Six teams (composed of 16 individual participants) submitted predictions for one or more tasks. The crown segmentation task proved to be the most challenging, with the highest-performing algorithm yielding only 34% overlap between remotely sensed crowns and the ground truthed trees. However, most algorithms performed better on larger trees. For the alignment task, an algorithm based on minimizing the difference, in terms of both position and tree size, between ground truthed and remotely sensed crowns yielded a perfect alignment. In hindsight, this task was over simplified by only including targeted trees instead of all possible remotely sensed crowns. Several algorithms performed well for species classification, with the highest-performing algorithm correctly classifying 92% of individuals and performing well on both common and rare species. Comparisons of results across algorithms provided a number of insights for improving the overall accuracy in extracting ecological information from remote sensing. Our experience suggests that this kind of competition can benefit methods development in ecology and biology more broadly.

scholarly journals A data science challenge for converting airborne remote sensing data into ecological information

2018 ◽  
Author(s):  
Sergio Marconi ◽  
Sarah J. Graves ◽  
Dihong Gong ◽  
Morteza Shahriari Nia ◽  
Marion Le Bras ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Quantitative Methods ◽  
Data Science ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Species Classification ◽  
Ecological Information ◽  
The Difference ◽  
Segmentation Task ◽  
Individual Trees

Ecology has reached the point where data science competitions, in which multiple groups solve the same problem using the same data by different methods, will be productive for advancing quantitative methods for tasks such as species identification from remote sensing images. We ran a competition to help improve three tasks that are central to converting images into information on individual trees: 1) crown segmentation, for identifying the location and size of individual trees; 2) alignment, to match ground truthed trees with remote sensing; and 3) species classification of individual trees. Six teams (composed of 16 individual participants) submitted predictions for one or more tasks. The crown segmentation task proved to be the most challenging, with the highest-performing algorithm yielding only 34% overlap between remotely sensed crowns and the ground truthed trees. However, most algorithms performed better on larger trees. For the alignment task, an algorithm based on minimizing the difference, in terms of both position and tree size, between ground truthed and remotely sensed crowns yielded a perfect alignment. In hindsight, this task was over simplified by only including targeted trees instead of all possible remotely sensed crowns. Several algorithms performed well for species classification, with the highest-performing algorithm correctly classifying 92% of individuals and performing well on both common and rare species. Comparisons of results across algorithms provided a number of insights for improving the overall accuracy in extracting ecological information from remote sensing. Our experience suggests that this kind of competition can benefit methods development in ecology and biology more broadly.

scholarly journals RELATIONSHIP OF SPECTRAL REFLECTANCE AND NDVI TO SOME SOIL PROPERTIES OF BRICKS FACTORIES SOILS IN NAHRAWAN AREA,BAGHDAD IRAQ

2019 ◽  
Vol 50 (3) ◽  
Author(s):  
R. K. Abdullatiff
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Spectral Reflectivity ◽  
Tigris River ◽  
The Difference ◽  
Remote Sensing Techniques ◽  
The One ◽  
Relationship Of

A study was conducted to investigate the effect of the brick industry on the environmental system of these project soils of the brick factories in Alnahrawan district. Remote sensing techniques was used to study the relationship between the spectral reflectivity and the vegetative index on the one hand and some surface soil characters of the project and to determine the variation in vegetation cover for the same area and for two different periods.Ten sites were selected to study spectral reflectivity under similar geomorphological conditions near the brickworks project in the Anahrawan district with an area of 10,000 hectares. Soil samples were taken from the surface and at a depth of 0-30 cm. Some chemical and physical characters of research soil were analyzed in the soil department laboratories, college of Agriculture, Baghdad University.Several satellite images taken from the satellite Land sat (ETM) 2013 and another from same satellite in 1990 T.M to determining the change between the two periods. After obtaining remote sensing data (reflectivity and vegetation index).the correlation analysis was carried out between these data. It was observed that the soil salinity values were decreased due to the drainage that the area was confined between the Tigris River and the Diyala tributary which leads to good natural drainage.The attached tables indicate that thedigital numbers of the soil sampling sites in 2013 are highly significant correlated, While some of the characters did not show the use of this region industrially. After calculating the difference between the two images to determine the change. A 100% change was observed and the vegetation cover was sharply reduced between the two images. as well as the extension of the land of empty land, although these lands are still suitable for agriculture.

scholarly journals THE EFFECT OF DIFFERENT ATMOSPHERIC CORRECTIONS ON BATHYMETRY EXTRACTION USING LANDSAT 8 SATELLITE IMAGERY

2017 ◽  
Vol 12 (1) ◽  
pp. 13 ◽  
Author(s):  
Kuncoro Teguh Setiawan ◽  
Yennie Marini ◽  
Johannes Manalu ◽  
Syarif Budhiman
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Image Data ◽  
Landsat 8 ◽  
Calculation Error ◽  
Sensing Technology ◽  
The Difference ◽  
Landsat Satellite

Remote sensing technology can be used to obtain information bathymetry. Bathymetric information plays an important role for fisheries, hydrographic and navigation safety. Bathymetric information derived from remote sensing data is highly dependent on the quality of satellite data use and processing. One of the processing to be done is the atmospheric correction process. The data used in this study is Landsat 8 image obtained on June 19, 2013. The purpose of this study was to determine the effect of different atmospheric correction on bathymetric information extraction from Landsat satellite image data 8. The atmospheric correction methods applied were the minimum radiant, Dark Pixels and ATCOR. Bathymetry extraction result of Landsat 8 uses a third method of atmospheric correction is difficult to distinguish which one is best. The calculation of the difference extraction results was determined from regression models and correlation coefficient value calculation error is generated.

scholarly journals ON THE INFORMATION CONTENT OF SPECTRAL CHANNELS AND NDVI OF THE LANDSAT 5 TM LANDSAT 7 ETM+ REMOTE SENSING SATELLITE FOR ASSESSING THE STAGES OF FOREST OVERGROWTH OF ABANDONED AGRICULTURAL LANDS

ÈKOBIOTEH ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 178-185
Author(s):  
I.R. Tuktamyshev ◽  
◽  
P.S. Shirokikh ◽  
R.Y. Mullagulov ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
The Urals ◽  
Landsat 7 ◽  
Landsat 5 Tm ◽  
The Difference

Abandoned arable land is a widespread phenomenon in land use. Methods based on the use of remote sensing data are most suitable for studying and monitoring farmlands overgrown with forest. Multispectral satellite images and vegetation indices can reflect the difference at certain stages of the successional development of fallow vegetation. The aim of the work is to evaluate the informative value of individual channels of medium-resolution images of Landsat satellites and the normalized difference vegetation index (NDVI) for identifying vegetation areas at various stages of reforestation succession on abandoned arable land in the zone of distribution of broad-leaved forests in the Urals. As the source material we used 30 georeferenced relevés of different overgrowth stages made in 2012, and 9 cloudless Landsat 5 TM and Landsat 7 ETM+ images for the period from April to October 2011. Using the data, NDVI and values of three spectral bands (Red, NIR, Thermal) were calculated for the relevé points. The most informative when dividing the stages of reforestation on abandoned fields in the zone of distribution of broad-leaved forests in the Urals were the NDVI vegetation index and the surface temperature estimated by the thermal channel. In addition, the red band can be useful for identifying the initial stage of succession.

scholarly journals Utilization of Remote Sensing Technology for Carbon Offset Identification in Malaysian Forests

2021 ◽  
Author(s):  
Hamdan Omar ◽  
Thirupathi Rao Narayanamoorthy ◽  
Norsheilla Mohd Johan Chuah ◽  
Nur Atikah Abu Bakar ◽  
Muhamad Afizzul Misman
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Tropical Forests ◽  
Optical Sensors ◽  
Landsat Imagery ◽  
Remote Sensing Data ◽  
Tropical Country ◽  
Sensing Technology ◽  
Carbon Offset ◽  
Carbon Dioxide Co2

Rapid growth of Malaysia’s economy recently is often associated with various environmental disturbances, which have been contributing to depletion of forest resources and thus climate change. The need for more spaces for numerous land developments has made the existing forests suffer from deforestation. This chapter presents an overview and demonstrates how remote sensing data is used to map and quantify changes of tropical forests in Malaysia. The analysis dealt with image processing that produce seamless mosaics of optical satellite data over Malaysia, within 15 years period, with 5-year intervals. The challenges were about the production of cloud-free images over a tropical country that always covered by clouds. These datasets were used to identify eligible areas for carbon offset in land use, land use change and forestry (LULUCF) sector in Malaysia. Altogether 580 scenes of Landsat imagery were processed to complete the observation period and came out with a seamless, wall to wall images over Malaysia from year 2005 to 2020. Forests have been identified from the image classification and then classified into three major types, which are dry-inland forest, peat swamp and mangroves. Post-classification change detection technique was used to determine areas that have been undergoing conversions from forests to other land uses. Forest areas were found to have declined from about 19.3 Mil. ha (in 2005) to 18.2 Mil. ha in year 2020. Causes of deforestation have been identified and the amount of carbon dioxide (CO2) that has been emitted due to the deforestation activity has been determined in this study. The total deforested area between years 2005 and 2020 was at 1,087,030 ha with rate of deforestation of about 72,469 ha yr.−1 (or 0.37% yr.−1). This has contributed to the total CO2 emission of 689.26 Mil. Mg CO2, with an annual rate of 45.95 Mil. Mg CO2 yr.−1. The study found that the use of a series satellite images from optical sensors are the most appropriate sensors to be used for monitoring of deforestation over the Malaysia region, although cloud covers are the major issue for optical imagery datasets.

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