The identification of sugarcane varieties through remote sensing is studied to reduce the time taken to identify in the field, also is useful to identify non-certified varieties and to monitor the adoption of new vari...The identification of sugarcane varieties through remote sensing is studied to reduce the time taken to identify in the field, also is useful to identify non-certified varieties and to monitor the adoption of new varieties. The purpose of this study is to evaluate the Landsat 7 ETM＋ images to discriminate varieties CC85-92 and CC84-75 in the Cauca river valley in Colombia. The method used to measure the spectral separability between varieties was Jeffries-Matusita. The results indicated that the only period where a clear discrimination of the varieties is between 4th and 5th months, with a global precision of 80.8% and kappa index 0.62. The proposed methodology and preliminary results show that remote sensing is a useful tool for monitoring and identification of varieties and could be used for identification of varieties already registered and planted in other countries without the consent of their true creators.展开更多
The study examines the changes of land cover/use resources for the period under investigation.An unsupervised vegetation classification is being performed that provides five distinctive classes and thus assesses these...The study examines the changes of land cover/use resources for the period under investigation.An unsupervised vegetation classification is being performed that provides five distinctive classes and thus assesses these changes in five broad land cover classes-high/moist forests,forest regrowth,mixed savanna,bare land/ grass and water.The remote sensing images used in this work are both images of TM and ETM+in different time periods(1986 to 2001)to determine land cover/use changes.A fairly accuracy report is recorded after performing the unsupervised classification,which shows vegetation has been depleted for over the years.Changes created are mostly human and to a lesser extent environment.Human activities are mainly encroachment thus altering the landscape through activities such as population growth,agriculture,settlements,etc.and environment due to some perceive climatic changes.This vegetation classification highlights the importance to acquire and publish information about the country's partial vegetation cover and vegetation change including vegetation maps and other basic vegetation influencing factors,leading to an understanding of its evolution for a period.展开更多
Up to date information about the existing land cover patterns and changes in land cover over time is one of the prime prerequisites for the preparation of an integrated development plan and economic development progra...Up to date information about the existing land cover patterns and changes in land cover over time is one of the prime prerequisites for the preparation of an integrated development plan and economic development program of a region. By using ETM＋ image data from 2002, we provided a land cover map of deciduous forest regions in Azerbaijan Province, Iran. Initial qualitative evaluation of the data showed no significant radiometric errors. Image classification was carried out using a maximum likelihood-based supervised classification method. In the end, we determined five major land cover classes, i.e., grass lands, deciduous broad-leaf forest, cultivated land, river and land without vegetation cover. Accuracy, estimated by the use of criteria such as overall accuracy from a confusion matrix of classification was 86% with a 0.88 Kappa coefficient. Such high accuracy results demonstrate that the combined use of spectral and textural characteristics increased the number of classes in the field classification, also with excellent accuracy. The availability and use of time series of remote sensing data permit the detection and quantification of land cover changes and improve our understanding of the past and present status of forest ecosystems.展开更多
文摘The identification of sugarcane varieties through remote sensing is studied to reduce the time taken to identify in the field, also is useful to identify non-certified varieties and to monitor the adoption of new varieties. The purpose of this study is to evaluate the Landsat 7 ETM＋ images to discriminate varieties CC85-92 and CC84-75 in the Cauca river valley in Colombia. The method used to measure the spectral separability between varieties was Jeffries-Matusita. The results indicated that the only period where a clear discrimination of the varieties is between 4th and 5th months, with a global precision of 80.8% and kappa index 0.62. The proposed methodology and preliminary results show that remote sensing is a useful tool for monitoring and identification of varieties and could be used for identification of varieties already registered and planted in other countries without the consent of their true creators.
文摘The study examines the changes of land cover/use resources for the period under investigation.An unsupervised vegetation classification is being performed that provides five distinctive classes and thus assesses these changes in five broad land cover classes-high/moist forests,forest regrowth,mixed savanna,bare land/ grass and water.The remote sensing images used in this work are both images of TM and ETM+in different time periods(1986 to 2001)to determine land cover/use changes.A fairly accuracy report is recorded after performing the unsupervised classification,which shows vegetation has been depleted for over the years.Changes created are mostly human and to a lesser extent environment.Human activities are mainly encroachment thus altering the landscape through activities such as population growth,agriculture,settlements,etc.and environment due to some perceive climatic changes.This vegetation classification highlights the importance to acquire and publish information about the country's partial vegetation cover and vegetation change including vegetation maps and other basic vegetation influencing factors,leading to an understanding of its evolution for a period.
文摘Up to date information about the existing land cover patterns and changes in land cover over time is one of the prime prerequisites for the preparation of an integrated development plan and economic development program of a region. By using ETM＋ image data from 2002, we provided a land cover map of deciduous forest regions in Azerbaijan Province, Iran. Initial qualitative evaluation of the data showed no significant radiometric errors. Image classification was carried out using a maximum likelihood-based supervised classification method. In the end, we determined five major land cover classes, i.e., grass lands, deciduous broad-leaf forest, cultivated land, river and land without vegetation cover. Accuracy, estimated by the use of criteria such as overall accuracy from a confusion matrix of classification was 86% with a 0.88 Kappa coefficient. Such high accuracy results demonstrate that the combined use of spectral and textural characteristics increased the number of classes in the field classification, also with excellent accuracy. The availability and use of time series of remote sensing data permit the detection and quantification of land cover changes and improve our understanding of the past and present status of forest ecosystems.
