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In this study, the main aim was to analyze the spatial changes of Burdur and Acigol Lakes by using Remote Sensing approaches and geographical in-formation system (GIS) applications. Five multi-temporal satellite images from Landsat 2, Landsat 5 TM and Landsat 8 OLI were used to monitor and map the shoreline changes for both lakes. Support Vector Machine (SVM) classification was used as an effective way of monitoring the changes between specified time intervals accurately. Object-Based Image Analysis (OBIA) is the second classification approach that was applied to all data in this study. Besides both classification applications, spectral water indexes including the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) were used for the ex-traction of the water body area and digitization of these areas in the GIS platform. The results of both SVM and OBIA classifications indicated that Burdur Lake lost 40% of its water body as the total area of Burdur lake was 209 km2 in 1975 and about 125 km2 in 2019, while Acigol Lake lost about 70% of its water body, with a total area of about 77 km2 in 1975 and 24.6 km2 in 2019. Spatiotemporal changes of Burdur and Acigol Lakes based on the applied meth-ods show a significant diminishing trend in surface area between the time period of 1975 and 2019 in this study. The results show that remote sensing ap-plications including water indexes and classification algorithms are effective ways of identifying changes between spatial and specific time intervals. [ABSTRACT FROM AUTHOR] more...

The availability of multispectral images, with both high spatial and spectral resolution, makes it possible to obtain valuable information about complex urban environment, reducing the need for more expensive surveying techniques. Here, a methodology is tested for the semi-automatic extraction of buildings and the mapping of the main roofing materials over a urban area of approximately 100 km2, including the entire city of Bologna (Italy). The methodology follows an object-oriented approach and exploits a limited number of training samples. After a validation based on field inspections and close-range photos acquired by a drone, the final map achieved an overall accuracy of 94% (producer accuracy 79%) regarding the building extraction and of 91% for the classification of the roofing materials. The proposed approach proved to be flexible enough to catch the strong variability of the urban texture in different districts and can be easily reproducible in other contexts, as only satellite imagery is required for the mapping. more...

Microwave imagery has a distinct advantage over optical imagery in high-latitude areas because it allows data to be acquired independently of cloud cover and solar illumination. Synthetic aperture radar (SAR)-based monitoring has become increasingly important for understanding the state and dynamics of permafrost landscapes at the regional scale. This study presents a permafrost landscape mapping method that uses multi-temporal TerraSAR-X backscatter intensity and interferometric coherence information. The proposed method can classify permafrost landscape features and map the two most important features in sub-arctic permafrost environments: permafrost-affected areas and thermokarst ponds. First, a land cover map is generated through the combined use of object-based image analysis (OBIA) and classification and regression tree (CART) analysis. An overall accuracy of 98% is achieved when classifying rock and water bodies, and an accuracy of 79% is achieved when discriminating between different vegetation types with one year of single-polarized acquisitions. Second, the distributions of the permafrost-affected areas and thermokarst ponds are derived from the classified landscapes. Permafrost-affected areas are inferred from the relationship between vegetation cover and the existence of permafrost, and thermokarst pond distributions are directly inherited from the land cover map. The two mapped features exhibit good agreement with manually delineated references. The proposed method can produce permafrost landscape maps in complex sub-arctic environments and improve our understanding of the effects of climate change on permafrost landscapes. This classification strategy can be transferred to other time-series SAR datasets, e.g., Sentinel-1, and other heterogeneous environments. [ABSTRACT FROM AUTHOR] more...

This paper aims to investigate the possibility of automatic identification of the forest main group species by the use of Airborne LiDAR data (ALS) and multispectral aerial images recorded by unmanned airplanes. The case study is located in South West Romania, in the Mic Mountain area, in the forests belonging to the Production Unit 6 Cuntu, Experimental Base of Carasenbeş. Three research plots with a size of 1 ha (100 x 100 m) each were placed within representative mixed stands of coniferous and deciduous tree species. The tree species within the plots were measured using the FieldMap equipment, the tree heights were recorded by the use of the Vertex IV ultrasound dendrometer, and the diameters were measured with an electronic caliper. Airborne LiDAR data has been processed using specific algorithms, resulting the digital terrain model (DTM), the digital surface model (DSM), and the normalized canopy height model (CHM). Very high spatial resolution images were captured using an eBee RTK device equipped with a CanonS110 RGB camera, in the case of the 2016 flight, or with a CanonS110 NIR camera in the case of the 2017 flight. The Object-Based Image Analysis (OBIA) supervised classification was performed for both RGB and CIR images through several stages, presented as a logical succession of specific processes. The OBIA classification of the RGB image gave better results compared to the OBIA classification of the CIR image, the overall accuracy of the RGB image classification was up to 16.7% higher than in the case of the CIR image. The object-based image analysis gave promising results in terms of automatic identification of the main group trees species, indicating a high level of standardization as well as the ability to be replicated under various conditions (different study areas and / or types of images). [ABSTRACT FROM AUTHOR] more...

In the Galapagos Islands, where 97% of the territory is protected and ecosystem dynamics are highly vulnerable, timely and accurate information is key for decision making. An appropriate monitoring system must meet two key features: on one hand, being able to capture information in a systematic and regular basis, and on the other hand, to quickly gather information on demand for specific purposes. The lack of such a system for geographic information limits the ability of Galapagos Islands' institutions to evaluate and act upon environmental threats such as invasive species spread and vegetation degradation. In this context, the use of UAVs (unmanned aerial vehicles) for capturing georeferenced images is a promising technology for environmental monitoring and management. This paper explores the potential of UAV images for monitoring degradation of littoral vegetation in Puerto Villamil (Isabela Island, Galapagos, Ecuador). Imagery was captured using two camera types: Red Green Blue (RGB) and Infrarred Red Green (NIR). First, vegetation presence was identified through NDVI. Second, object-based classification was carried out for characterization of vegetation vigor. Results demonstrates the feasibility of UAV technology for base-line studies and monitoring on the amount and vigorousness of littoral vegetation in the Galapagos Islands. It is also showed that UAV images are not only useful for visual interpretation and object delineation, but also to timely produce useful thematic information for environmental management. [ABSTRACT FROM AUTHOR] more...

South Patagonian peat bogs are little studied sources of methane (CH4). Since CH4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH4 fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH4 fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species- and 0.83 for the microform-level classification, respectively. CH4 fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH4 closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH4 fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg·m-2· day-1 to 26.8 mg·m-2·day-1. Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH4 fluxes when plot-scale fluxes are up-scaled. [ABSTRACT FROM AUTHOR] more...

Manual vectorization of multispectral images is a widely used method for making land-use or land-cover maps. Although it is usually considered relatively accurate it is very time consuming, which has prompted the use in recent years of various semiautomatic methods for classifying remotely sensed images. One of the most promising of the latter is object-oriented image analysis based upon image segmentation, but the accuracy of its results, as well as its time demands, are disputed. Accordingly, this paper compared manual vectorization with object-oriented classification to reveal the strong and weak points of each. Two qualitatively different datasets were classified using both methods; time costs were monitored and accuracy levels were compared. It was found that manual vectorization achieved better overall accuracy (up to 93% versus 84%), but the semiautomatic method was usually more accurate when classifying some specific features such as roads, built-up areas, broadleaf trees and coniferous trees. The verdict regarding time-efficiency was less clear cut. The best method depends upon the spatial and spectral resolution of the data being processed. [ABSTRACT FROM AUTHOR] more...

The availability of multispectral images, with both high spatial and spectral resolution, makes it possible to obtain valuable information about complex urban environment, reducing the need for more expensive surveying techniques. Here, a methodology is tested for the semi-automatic extraction of buildings and the mapping of the main roofing materials over a urban area of approximately 100 km2, including the entire city of Bologna (Italy). The methodology follows an object-oriented approach and exploits a limited number of training samples. After a validation based on field inspections and close-range photos acquired by a drone, the final map achieved an overall accuracy of 94% (producer accuracy 79%) regarding the building extraction and of 91% for the classification of the roofing materials. The proposed approach proved to be flexible enough to catch the strong variability of the urban texture in different districts and can be easily reproducible in other contexts, as only satellite imagery is required for the mapping. [ABSTRACT FROM AUTHOR] more...

Recently, the severe intensification of atmospheric carbon has highlighted the importance of urban tree contributions in atmospheric carbon mitigations in city areas considering sustainable urban green planning and management systems. Explicit and timely information on urban trees and their roles in the atmospheric Carbon Stock (CS) are essential for policymakers to take immediate actions to ameliorate the effects of deforestation and their worsening outcomes. In this study, a detailed methodology for urban tree CS calibration and mapping was developed for the small urban area of Sassuolo in Italy. For dominant tree species classification, a remote sensing approach was applied, utilizing a high-resolution WV3 image. Five dominant species were identified and classified by applying the Object-Based Image Analysis (OBIA) approach with an overall accuracy of 78%. The CS calibration was done by utilizing an allometric model based on the field data of tree dendrometry—i.e., Height (H) and Diameter at Breast Height (DBH). For geometric measurements, a terrestrial photogrammetric approach known as Structure-from-Motion (SfM) was utilized. Out of 22 randomly selected sample plots of 100 square meters (10 m × 10 m) each, seven plots were utilized to validate the results of the CS calibration and mapping. In this study, CS mapping was done in an efficient and convenient way, highlighting higher CS and lower CS zones while recognizing the dominant tree species contributions. This study will help city planners initiate CS mapping and predict the possible CS for larger urban regions to ensure a sustainable urban green management system. [ABSTRACT FROM AUTHOR] more...