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
In general,topographic shadow may reduce performance of forest mapping over mountainous regions in remotely sensed images.In this paper,information in shadow was synthesized by using two filling techniques,namely,roif...In general,topographic shadow may reduce performance of forest mapping over mountainous regions in remotely sensed images.In this paper,information in shadow was synthesized by using two filling techniques,namely,roifill and imfill,in order to improve the accuracy of forest mapping over mountainous regions.These two methods were applied to Landsat Enhanced Thematic Mapper (ETM +) multispectral image from Dong Yang County,Zhejiang Province,China.The performance of these methods was compared with two conventional techniques,including cosine correction and multisource classification.The results showed that by applying filling approaches,average overall accuracy of classification was improved by 14 percent.However,through conventional methods this value increased only by 9 percent.The results also revealed that estimated forest area on the basis of shadow-corrected images by 'roifill' technique was much closer to the survey data compared to traditional algorithms.Apart from this finding,our finding indicated that topographic shadow was an accentuated problem in medium resolution images such as Landsat ETM+ over mountainous regions.展开更多
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
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.
基金supported by the funding from National Natural Science Foundation of China(Grant No 30671212)partially by NASA projects NNX08AH50G and G05GD49G at Michigan State University
文摘In general,topographic shadow may reduce performance of forest mapping over mountainous regions in remotely sensed images.In this paper,information in shadow was synthesized by using two filling techniques,namely,roifill and imfill,in order to improve the accuracy of forest mapping over mountainous regions.These two methods were applied to Landsat Enhanced Thematic Mapper (ETM +) multispectral image from Dong Yang County,Zhejiang Province,China.The performance of these methods was compared with two conventional techniques,including cosine correction and multisource classification.The results showed that by applying filling approaches,average overall accuracy of classification was improved by 14 percent.However,through conventional methods this value increased only by 9 percent.The results also revealed that estimated forest area on the basis of shadow-corrected images by 'roifill' technique was much closer to the survey data compared to traditional algorithms.Apart from this finding,our finding indicated that topographic shadow was an accentuated problem in medium resolution images such as Landsat ETM+ over mountainous regions.
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.
