Accuracy of pixel-based classification: application of different algorithms to landscapes of Western Iran.

peer reviewedScenarios for monitoring land cover on a large scale, involving large volumes of data, are becoming more prevalent in remote sensing applications. The accuracy of algorithms is important for environmental monitoring and assessments. Because they performed equally well throughout the various research regions and required little human involvement during the categorization process, they appear to be resilient and accurate for automated, big area change monitoring. Malekshahi City is one of the important and at the same time critical areas in terms of land use change and forest area reduction in Ilam Province. Therefore, this study aimed to compare the accuracy of nine different methods for identifying land use types in Malekshahi City located in Western Iran. Results revealed that the artificial neural network (ANN) algorithm with back-propagation algorithms could reach the highest accuracy and efficiency among the other methods with kappa coefficient and overall accuracy of approximately 0.94 and 96.5, respectively. Then, with an overall accuracy of about 91.35 and 90.0, respectively, the methods of Mahalanobis distance (MD) and minimum distance to mean (MDM) were introduced as the next priority to categorize land use. Further investigation of the classified land use showed that good results can be provided about the area of the land use classes of the region by applying the ANN algorithm due to high accuracy. According to those results, it can be concluded that this method is the best algorithm to extract land use maps in Malekshahi City because of high accuracy

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google Earth TM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing).Siham Tabik was supported by the Ramón y Cajal Programme (RYC-2015-18136).The work was partially supported by the Spanish Ministry of Science and Technology under the projects: TIN2014-57251-P, CGL2014-61610-EXP, CGL2010-22314 and grant JC2015-00316, and ERDF and Andalusian Government under the projects: GLOCHARID, RNM-7033, P09-RNM-5048 and P11-TIC-7765.This research was also developed as part of project ECOPOTENTIAL, which received funding from the European Union Horizon 2020 Research and Innovation Programme under grant agreement No. 641762, and by the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612

LANDSCAPE SCALE SPECTRAL-TEMPORAL MODELLING OF BAMBOO-DOMINATED FOREST SUCCESSION WITHIN THE ATLANTIC FOREST OF SOUTHERN BRAZIL

Tropical and subtropical ecosystems have become vulnerable to biological invasion (i.e., bamboo) due to human induced forest fragmentation. Bamboo ecological processes have been found to impede forest development, resulting in a state of arrested succession, which has been found to significantly reduce biodiversity, thus contributing to biotic homogenization. In this study we use a semi-empirical approach to develop a community-level spatially explicit ecological process model (hybrid model) using a time-series of Landsat imagery to describe single-landscape scale ecological processes of a pervasive bamboo species (Merostachys skvortzovii) found throughout the Araucaria forest, a critically threatened subtype of Atlantic forest of southern Brazil. The model is subsequently used to map bamboo spatial distribution at a multiple-landscape scale to examine patch pattern throughout a portion of the Araucaria forest. It was determined that the M. skvortzovii lifecycle is a synchronized process occurring at single and multiple-landscapes scale and is comprised of four broad lifecycle phases: pioneer predominance, mature bamboo, dieback and pioneer regeneration. Bamboo patch pattern was found to be associated with human settlement and geographic features, with clusters of patches sharing the same shape and size observed at multiple scales

Mapping natural forest cover, tree species diversity and carbon stocks of a subtropical Afromontane forest using remote sensing.

Doctoral Degree. University of KwaZulu-Natal, Durban.Natural forests cover about a third of terrestrial landmass and provides benefits such as carbon sequestration, and regulation of biogeochemical cycles. It is essential that adequate information is available to support forest management. Remote Sensing imageries provide data for mapping natural forests. Hence, our study aimed at mapping the Nkandla Forest Reserve attributes with Remote Sensing imageries. Quantitative information on the forest attributes is non-existent for many of these forests, including the sub-tropical Afromontane Nkandla Forest Reserve. This does not support scientific and evidence based natural forest management. A review of literature revealed that progress has been made in Remote Sensing monitoring of natural forest attributes. The Random Forest (RF) and Support Vector Machine (SVM) were applied to Landsat 8 in classifying the land use land cover (LULC) classes of the forest. Each of the algorithms produced higher accuracy of above 95% with the SVM performing slightly better than the RF. The SVM, Markov Chain and Multi-Layer Perceptron Neural Network (MLPNN) were adopted for a spatiotemporal change detection over the last 30 years at decadal interval for the forest. There were consistent changes in each of the four LULC classes. The study further conducted a forecasting of LULC distribution for 2029. Aboveground carbon (AGC) estimation was carried out using Sentinel 2 imagery and RF modelling. Four models made up smade of Sentinel 2 products could successfully map the AGC with high accuracies. The last two studies focused on tree species diversity with the first evaluating the influence of spatial and spectral resolution on prediction accuracies by comparing the PlanetScope, RapidEye, Sentinel 2 and Landsat 8. Both the spatial and spectral resolution were found to influence accuracies with the Sentinel 2 emerging as the best imagery. The second aspect focused on identifying the best season for the prediction of tree species diversity. Summer imagery emerged as the best season and the winter being the least performer. Overall, our study indicates that Remote Sensing imageries could be used for successful mapping of natural forest attributes. The outputs of our studies could also be of interest to forest managers and Remote Sensing experts.Author's Publications and Manuscripts can be found on page iii