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Author: Food and Agriculture Organization of the United Nations
Publisher: Food & Agriculture Org.
ISBN: 9251319650
Category : Political Science
Languages : en
Pages : 84
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Book Description
Due to more and more frequent extreme climate events (floods, drought, and frosts) and due to the changes in precipitation (amounts, seasonality, intensity) and warming temperatures that are impacting rainfed agriculture and changing growing seasons, the Ministry of Jihad-e-Agriculture (MOJA) of the Islamic Republic of Iran asked the Food and Agriculture Organization of the United Nations (FAO) to provide assistance in setting up an improved agriculture monitoring system, based on integral use of advanced geospatial technologies to support development of the techniques, policy and investment conditions to achieve sustainable agricultural development under the current changing conditions of climate. The project has focused on the identification of state-of-the-art methods and strategy for acreage and yield estimation, based on an assessment of the existing monitoring methodology, optimized through the use of remote sensing. In addition, the project benefitted from the availability of multi-temporal satellite images for testing and monitoring of a range of crops in 3 selected pilot areas: the provinces of Zanjan and Mazandaran and the region of the south of Kerman. The publication reports data collected, processes followed and results obtained at this stage of the still not completely concluded study.
Author: Food and Agriculture Organization of the United Nations
Publisher: Food & Agriculture Org.
ISBN: 9251319650
Category : Political Science
Languages : en
Pages : 84
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Book Description
Due to more and more frequent extreme climate events (floods, drought, and frosts) and due to the changes in precipitation (amounts, seasonality, intensity) and warming temperatures that are impacting rainfed agriculture and changing growing seasons, the Ministry of Jihad-e-Agriculture (MOJA) of the Islamic Republic of Iran asked the Food and Agriculture Organization of the United Nations (FAO) to provide assistance in setting up an improved agriculture monitoring system, based on integral use of advanced geospatial technologies to support development of the techniques, policy and investment conditions to achieve sustainable agricultural development under the current changing conditions of climate. The project has focused on the identification of state-of-the-art methods and strategy for acreage and yield estimation, based on an assessment of the existing monitoring methodology, optimized through the use of remote sensing. In addition, the project benefitted from the availability of multi-temporal satellite images for testing and monitoring of a range of crops in 3 selected pilot areas: the provinces of Zanjan and Mazandaran and the region of the south of Kerman. The publication reports data collected, processes followed and results obtained at this stage of the still not completely concluded study.
Author: Renata Ribeiro
Publisher:
ISBN:
Category : Computers
Languages : en
Pages :
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Book Description
The remote sensing images are more accessible nowadays and there are proper technologies to receive, distribute, manipulate and process long satellite image time series that can be used to improve traditional methods for harvest monitoring and forecasting. The potential of the satellite multi-temporal images to support research of agricultural monitoring has increased according to improvements in technological development, especially in analysis of large volume of data available for knowledge discovery. In Brazil, sugarcane is cultivated on extensive fields and is the main agriculture crop used to produce ethanol. The main objective of this chapter is to monitor the sugarcane crop by clustering analysis with multi-temporal satellite images having low spatial resolution. A large database of this kind of image and specific software were used to perform the image pre-processing phase, extract time series, apply clustering method and enable the data visualization on several steps during the whole analysis process. According to the analysis done, our methodology allows to identify land areas with similar development patterns, also considering different growing seasons for the crops, covering monthly and annual periods. Results confirm that satellite images of low spatial resolution can indeed be satisfactorily used in agricultural crop monitoring in regional scale.
Author: Bidyuth Kumar Mahalder
Publisher:
ISBN:
Category :
Languages : en
Pages : 49
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Author: Dorothy Fay Klasse
Publisher:
ISBN:
Category : Agriculture
Languages : en
Pages : 96
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Satellite imagery can be of very real value to the poorly mapped areas of the world. Because agriculture is dynamic with constantly changing conditions, remote sensing offers the feasibility of monitoring agricultural lands over an extended period of time. The specific objectives of this research are: (1) to estimate the total acreage/hectares of oasis agricultural lands of the Turpan Depression on selected dates from 1972 to 1978, (2) to prepare thematic maps of the oasis agriculture for the years 1972, 1973, 1977, and 1978, and (3) to assess the agricultural land reclamation efforts within this study area. This thesis dealt with the mapping of oasis agriculture from Landsat imagery and the associated problems relating to image resolution, and the lack of ground data and supporting information. (Author).
Author: Association of American Geographers. Meeting
Publisher:
ISBN:
Category : Geography
Languages : en
Pages : 284
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Author:
Publisher:
ISBN:
Category : Regional planning
Languages : en
Pages : 342
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Author:
Publisher:
ISBN:
Category : Cartography
Languages : en
Pages : 36
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Author: Mark Nathaniel Jeunnette
Publisher:
ISBN:
Category :
Languages : en
Pages : 127
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Book Description
Remote sensing, in particular multispectral imagery, can measure crop health and detect in-season disturbances such as pests and diseases before they are visible to the naked eye, but it is inaccessible to small-plot farmers, especially in developing countries. So-called eExtension services provide up-to-date, reliable information for small-plot farmers, but struggle to collect the plot-specific crop health information on which to base personalized recommendations. This thesis addresses this issue using novel ideas in remote sensing system understanding, image processing, and geospatial workflow to develop the Airborne Monitoring System for In-Season Agriculture (AMSISA) and make remotely sensed crop health data accessible and useful for small-plot farmers. A simulation of platform performance characteristics shows that manned aircraft are the better aerial remote sensing platform, given current performance and regulatory realities. The time-series aerial remote sensing (TSARS) approach allows a reduction in spatial resolution to dramatically reduce survey costs and enable frequent updates for better monitoring performance. This allowance for plot-resolution (but not finer) data and minimal cost per hectare over a large area leads to the development of a model to optimize the data collected per plot based on survey altitude, heading, and camera properties. Imagery collected using a custom camera setup aboard a manned aircraft in Maharashtra, India is used to test and verify the models. Surveying at a spatial resolution near the size of farm plots on the ground requires precise registration of remotely sensed images to ensure accurate crop reflectance measurements. Current and novel multi-modal image registration techniques are tested and found to be inadequate for this application. Instead, a technique using known fiducials is presented to achieve the required registration precision.
Author: Emily Myers
Publisher:
ISBN:
Category : Artificial satellites
Languages : en
Pages : 0
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"Agricultural monitoring is an important application of earth-observing satellite systems, which may be used for stress and disease detection, growth stage monitoring, and yield prediction in crops at a fraction of the time and cost it would take to survey fields manually. Satellites within the Landsat program are frequently used for agricultural monitoring, but they do not always collect imagery often enough to capture rapid changes in vegetation health. To address this limitation, an increase in revisit rate is being considered for future Landsat systems. This research aims to determine the necessary overpass frequency for a future Landsat sensor for agricultural growth stage monitoring and yield prediction. Two experiments were conducted to study the effects of temporal resolution on the accuracy of these tasks. The first experiment investigated the impact of imaging frequency on growth stage monitoring. Image-derived plot-average Normalized Difference Vegetation Index (NDVI) time-series data collected over a small corn field were used to estimate phenological transition dates. Images were then removed from the original time-series, and dates were recalculated from the resampled data. Using PlanetScope surface reflectance imagery, the average range of estimated dates increased by a day and the average absolute deviation between estimated dates increased by 1/3 of a day for every day of increase in average revisit interval. Using the higher-quality PlanetScope L3H surface reflectance product, these rates of increase were approximately halved. Higher imaging frequency and higher radiometric quality both led to greater precision in estimates. The second experiment investigated the impacts of imaging frequency and time-series end date on yield correlation accuracy. Plot-average Green Normalized Difference Vegetation Index (GNDVI) time-series data collected over a small corn field during two different growing seasons were resampled to different revisit intervals, gap-filled and smoothed using two different methods, and correlated with plot-average yield at each day of the growing season. These experiments were then repeated with images removed from the end of the time-series. All methods tested performed well on time-series ending 65-72 days or more after green-up, and performed poorly for time-series ending prior to the day of peak GNDVI. Mean R-squared values for GNDVI-yield correlations decreased with increasing revisit intervals. This effect was stronger for the more typical 2019 data, as well as for time-series ending earlier in the growing season. The findings of this study, along with cloud contamination statistics, were used to recommend an overpass frequency of 1-4 days for future yield-monitoring satellite systems. The optimal frequency within this range depends on the specific task being attempted."--Abstract.
Author: Sushil Pradhan
Publisher:
ISBN:
Category :
Languages : nl
Pages : 112
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