Understanding the drivers of biological invasions in landscapes is a major goal in invasion ecology.The control of biological invasions has increasingly become critical in the past few decades because invasive species...Understanding the drivers of biological invasions in landscapes is a major goal in invasion ecology.The control of biological invasions has increasingly become critical in the past few decades because invasive species are thought to be a major threat to endemism.In this study,by examining the key variables that influence Acacia mearnsii,we sought to understand its potential invasion in eastern Zimbabwe.We used the maximum entropy(MaxEnt)method against a set of environmental variables to predict the potential invasion front of A.mearnsii.Our study showed that the predictor variables,i.e.,aspect,elevation,distance from streams,soil type and distance from the nearest A.mearnsii plantation adequately explained(training AUC=0.96 and test AUC=0.93)variability in the spatial distribution of invading A.mearnsii.The front of invasion by A.mearnsii seemed also to occur next to existing A.mearnsii plantations.Results from our study could be useful in identifying priority areas that could be targeted for controlling the spread of A.mearnsii in Zimbabwe and other areas under threat from A.mearnsii invasion.We recommend that the plantation owners pay for the control of A.mearnsii invasion about their plantations.展开更多
The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, th...The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.展开更多
文摘Understanding the drivers of biological invasions in landscapes is a major goal in invasion ecology.The control of biological invasions has increasingly become critical in the past few decades because invasive species are thought to be a major threat to endemism.In this study,by examining the key variables that influence Acacia mearnsii,we sought to understand its potential invasion in eastern Zimbabwe.We used the maximum entropy(MaxEnt)method against a set of environmental variables to predict the potential invasion front of A.mearnsii.Our study showed that the predictor variables,i.e.,aspect,elevation,distance from streams,soil type and distance from the nearest A.mearnsii plantation adequately explained(training AUC=0.96 and test AUC=0.93)variability in the spatial distribution of invading A.mearnsii.The front of invasion by A.mearnsii seemed also to occur next to existing A.mearnsii plantations.Results from our study could be useful in identifying priority areas that could be targeted for controlling the spread of A.mearnsii in Zimbabwe and other areas under threat from A.mearnsii invasion.We recommend that the plantation owners pay for the control of A.mearnsii invasion about their plantations.
文摘The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.
