Editorial Board Member: Journal of Applied Remote Sensing |
Author
Area of Expertise:
Hyperspectral Remote Sensing ,
Image Classification ,
Mapping and Monitoring Land Use/Cover Changes ,
Remote Sensing for Vegetation and Precision Agriculture ,
Machine Learning Methods for Remote Sensing data
Guest Editors Elhadi Adam, Craig Coburn, and Anthony Campbell introduce the Special Section on the 50th Anniversary of Landsat—Current Achievement and Future Directions.
Plant species invasion is known to be a major threat to socioeconomic and ecological systems. Due to high cost and limited extents of urban green spaces, high mapping accuracy is necessary to optimize the management of such spaces. We compare the performance of the new-generation WorldView-2 (WV-2) and SPOT-5 images in mapping the bracken fern [Pteridium aquilinum (L) kuhn] in a conserved urban landscape. Using the random forest algorithm, grid-search approaches based on out-of-bag estimate error were used to determine the optimal ntree and mtry combinations. The variable importance and backward feature elimination techniques were further used to determine the influence of the image bands on mapping accuracy. Additionally, the value of the commonly used vegetation indices in enhancing the classification accuracy was tested on the better performing image data. Results show that the performance of the new WV-2 bands was better than that of the traditional bands. Overall classification accuracies of 84.72 and 72.22% were achieved for the WV-2 and SPOT images, respectively. Use of selected indices from the WV-2 bands increased the overall classification accuracy to 91.67%. The findings in this study show the suitability of the new generation in mapping the bracken fern within the often vulnerable urban natural vegetation cover types.
Classification of different tree species in semiarid areas can be challenging as a result of the change in leaf structure and orientation due to soil moisture constraints. Tree species mapping is, however, a key parameter for forest management in semiarid environments. In this study, we examined the suitability of 5-band RapidEye satellite data for the classification of five tree species in mopane woodland of Botswana using machine leaning algorithms with limited training samples.We performed classification using random forest (RF) and support vector machines (SVM) based on EnMap box. The overall accuracies for classifying the five tree species was 88.75 and 85% for both SVM and RF, respectively. We also demonstrated that the new red-edge band in the RapidEye sensor has the potential for classifying tree species in semiarid environments when integrated with other standard bands. Similarly, we observed that where there are limited training samples, SVM is preferred over RF. Finally, we demonstrated that the two accuracy measures of quantity and allocation disagreement are simpler and more helpful for the vast majority of remote sensing classification process than the kappa coefficient. Overall, high species classification can be achieved using strategically located RapidEye bands integrated with advanced processing algorithms.
This study explored the utility of an object-based image classification approach for mapping land cover in a heterogeneous coastal zone using WorldView-2 imagery. Two relatively modern and robust supervised machine learning algorithms i.e. random forest (RF) and support vector machines (SVM) were also compared. Image segmentation was performed, and ten broad land cover classes were identified. Subsequently, we assessed the performance of an object based image classification and the selected machine learning algorithms in mapping the land cover classes. The validation of the thematic land cover maps derived from RF and SVM were assessed using an independent test dataset generated from field work data and aerial photography interpretation. Results showed that both the machine learning classifiers in combination with the object-based approach are useful in mapping land cover in heterogeneous coastal areas. However, SVM achieved the best overall accuracy (93.79%) and kappa statistic (0.93) while RF produced an overall accuracy of 86.94% and kappa value of 0.85. Overall, the study underlined the utility of combining an objectbased image classification with machine learning classifiers for mapping land-cover in heterogeneous coastal areas – a previously challenging task with broad band satellite sensors and traditional pixel-based image classification approaches.
The saturation problem associated with the use of NDVI for biomass estimation in high canopy density vegetation is a well-known phenomenon. Recent field spectroscopy experiments have shown that narrow band vegetation indices
computed from the red edge and the NIR shoulder can improve the estimation of biomass in such situations. However, the wide scale unavailability of high spectral resolution satellite sensors with red edge bands has not seen the up-scaling of these techniques to spaceborne remote sensing of high density biomass. This paper explored the possibility of estimating biomass in a densely vegetated wetland area using indices computed from Worldview-2 imagery, which contains a red edge band centred at 725 nm. Indices derived from the red edge band and the NIR shoulder yielded higher accuracies (R2 = 0.71) for biomass estimation as compared to indices computed from other portions of the electromagnetic spectrum. Predicting biomass on an independent test data set using the Random forest algorithm and 3 NDVIs computed from the red edge and NIR bands yielded a root mean square error of prediction (RMSEP) of 441g/m2 ( 13 % of observed mean biomass) as compared to the traditional spectral bands. The results demonstrate the utility of Worldview-2 imagery in estimating and ultimately mapping vegetation biomass at high density - a previously challenging task with broad band satellite sensors.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.