Object-based Classification Approach Research Articles

Comparing Pixel- and Object-Based Approaches for Classifying Multispectral Drone Imagery of a Salt Marsh Restoration and Reference Site

Monitoring salt marshes with remote sensing is necessary to evaluate their state and restoration. Determining appropriate techniques for this can be overwhelming. Our study provides insight into whether a pixel- or object-based Random Forest classification approach is best for mapping vegetation in north temperate salt marshes. We used input variables from drone images (raw reflectances, vegetation indices, and textural features) acquired in June, July, and August 2021 of a salt marsh restoration and reference site in Aulac, New Brunswick, Canada. We also investigated the importance of input variables and whether using landcover classes representing areas of change was a practical way to evaluate variation in the monthly images. Our results indicated that (1) the classifiers achieved overall validation accuracies of 91.1–95.2%; (2) pixel-based classifiers outperformed object-based classifiers by 1.3–2.0%; (3) input variables extracted from the August images were more important than those extracted from the June and July images; (4) certain raw reflectances, vegetation indices, and textural features were among the most important variables; and (5) classes that changed temporally were mapped with user’s and producer’s validation accuracies of 86.7–100.0%. Knowledge gained during this study will inform assessments of salt marsh restoration trajectories spanning multiple years.

Leveraging google earth engine cloud computing for large-scale arctic wetland mapping

Climate-driven permafrost degradation and an intensification of the hydrological cycle are rapidly altering the intricate ecohydrological processes of Arctic wetlands, threatening their long-term carbon sequestration capabilities. Addressing this concern through effective management holds immense potential for climate regulation, mitigation, and adaptation efforts. As such, there is growing need for timely spatial inventory data identifying Arctic wetlands with sufficient accuracy, resolution, and detail. Wetland mapping at large scales necessitates the processing of large volumes of Earth observation (EO) data, a challenge known as “Big Data”. Consequently, in this study, we present a cloud-based methodology exploiting the remarkable collection of EO data and computational power of Google Earth Engine (GEE) to map Arctic wetlands at 10 m spatial resolution. Our workflow evaluated temporally aggregated optical and radar satellite imagery and novel hydro-physiographic layers as inputs into a robust Random Forest (RF) machine learning (ML) algorithm. Both pixel and object-based classification approaches were assessed, whereby ML models were calibrated with a training dataset of sufficient and comprehensive samples. The study was conducted over Canada’s Southern Arctic ecozone (830,000 km2). GEE enabled the efficient preprocessing and classification of large volumes of EO data and resulted in excellent yet similar statistical performance for both pixel and object-based approaches, achieving overall accuracies of > 89 % and mean F1-scores of > 0.79. Moreover, McNemar tests indicated that these classifications were not statistically different, which has significant implications regarding computing time and processing efficiencies. These results demonstrate the efficacy and scalability of our cloud-based GEE methodology, and as such can support future endeavors around Pan-Arctic wetland mapping and monitoring.

Refining National Forest Cover Data Based on Fusion Optical Satellite Imageries in Indonesia

Precision mapping towards tropical forest cover data is critical to address the global climate crisis, such as land-based carbon measurement and potential conservation areas identification. In the recent decade, accessibility to open public datasets on forestry is rapidly increased. However, the availability of finer-resolution of forest cover data is still very limited. As a developing country with numerous rainforests, Indonesia suffered multifaceted threats, particularly deforestation. Thus, precise forest cover data can be useful to fulfill Indonesia’s nationally determined contribution to climate change. In this study, we mapped the national forest cover data for Indonesia using a new object-based image classification approach based on combined Planet-NICFI and Sentinel-2 optical imageries. Our findings had relatively high accuracy compared with the other studies, with the F score ranging from 0.67 to 0.99 and can capture the fragmented forest in fine resolution (i.e., ∼5 m). In addition, we found that Planet-NICFI bands had a higher contribution in predicting forest cover than Sentinel-2 imageries. Utilizing forest cover data for further analyses should be performed to help the achievement of national and global agenda, e.g., related to the FOLU net sink in 2030 and the Global Biodiversity Framework.

Object-based mapping and classification features for tropical highlands using on Sentinel-1, Sentinel-2, and GEDI canopy height data - A case study of the Muringato catchment, Kenya

Tropical highlands remain a challenging target for remote sensing due to their high heterogeneity of the landscape and frequent cloud cover, causing a shortage of high-quality and reliable comprehensive data on land use and land cover on a local or regional scale. These, however, are urgently needed by local stakeholders and decisionmakers. This applies for example to the Muringato sub-catchment in Nyeri County, Kenya, where acute water problems have been identified to be usually directly related to specific land use and land cover. This article contributes to the understanding of tropical highlands from a remote sensing perspective by examining Sentinel-1, Sentinel-2 and Global Forest Canopy Height Model data from the Global Ecosystem Dynamics Investigation, all provided by the Google Earth Engine. To do so, we assess classifiers derived from these datasets for different land cover types, analyzing the performance of promising candidates identified in the literature, using 2,800 samples extracted from high-resolution image data across Nyeri County. We also propose an object-based classification strategy based on sequential masking. This strategy is adapted to very heterogeneous landscapes by refining image objects after re-evaluating their homogeneity. Small buildings, which constitute a significant part of the settlement structure in the area, are particularly difficult to detect. To improve the recognition of these objects we additionally consider the local contrast of the relevant classifier to identify potential candidates. Evaluating our sample data, we found that especially optical indices like the Sentinel Water Index, the Enhanced Normalized Difference Impervious Surfaces Index or specific Sentinel-2 bands combined with canopy height data are promising for water, built-up or tree cover detection. With these findings, our proposed object-based classification approach is applied to the Muringato sub-catchment as a representative example of the Kenyan tropical highland region. We achieve a classification accuracy of approximately 88% in the Muringato sub-catchment, outperforming existing products available for the study area. The knowledge gained in the study will also be used for future remote sensing-based monitoring of the region.

Upstream River Erosion vis-a-vis Sediments Variability in Hugli Estuary, India: A Geospatial Approach

Satellite data shows that the Bhagirathi-Hugli River’s riverbank has faced severe erosion during the last decades (1990 to 2020), with the middle stretch of the river being more prone to erosion. This huge sediment load derived from upstream erosion is coming to the estuary. The suspended sediment concentration dynamics of the Hugli estuary were calculated using in-situ data and remote sensing reflectance by establishing a linear regression. A continuous huge sediment load is found in the estuarine water. The sediment concentration was higher pre-monsoon than post-monsoon as the region is highly influenced by monsoonal rainfall and runoff. The sediment concentration was also higher in the estuary’s southwestern section than in the northern part. The impact of this high sediment load contributes to the deposition. This depositional area assessment was performed using an object-based classification approach called Support Vector Machine utilizing Grey Level Co-occurrence Matrix to create cluster textural indices. Despite the impact of continuous sea level rise in the estuary, the result shows that effective island and Chars areas have increased in the past decade due to the upstream erosion-driven sediments.

XÁC ĐỊNH PHẠM VI CHE PHỦ CỦA CÂY DỪA NƯỚC TẠI KHU DỰ TRỮ SINH QUYỂN CÙ LAO CHÀM-HỘI AN BẰNG DỮ LIỆU SENTINEL-2A

The mangrove palm grows at the Thu Bon river’s mouth. Mangrove palm has contributed to the conservation of natural ecosystems, and the biological diversity of Cu Lao Cham – Hoi An Biosphere Reserve, as well as the socio-economic development of Hoi An city. However, in recent year, its coverage has been gradually reduced due to human socio-economic activities. This study has used not only the raw bands of the Sentinel-2A image but also some spectral indices have been calculated via the band ratio for classifying land covers. The principal component analysis (PCA) was applied to these all bands to eliminate redundant and noise contributions in data. Mangrove palm was separated from other land cover categories with an object-based image classification approach using Support Vector Machines (SVMs) algorithm because of its high efficiency, performance, and flexibility. The obtained map indicates that mangrove palm principally distributes on the left bank of Thu Bon River with a total area of 147 ha by 2019. The validation, was realized by comparing the classification results with random samples through the visual interpretation of Google Earth high resolution image. The error assessment shows that the overall accuracy is about 88.67% and the Kappa coefficient reaches 0.84. Although there are some differences related to the spatial resolution of Sentinel-2 images, such obtained results may support authorities in decision-making by providing maps able to suggest necessary responses for the conservation of mangrove palms this coastal area.

Multi-seasonal evaluation of hybrid poplar (P. Deltoides) plantations using Worldview-3 imagery and State-Of-The-Art ensemble learning algorithms

Forest resources are the primary components of the ecosystem environment. Poplars (Populus sp.), a member of the fast-growing trees, are one of the most productive forest tree species for industrial production thanks to their desirable traits comprising rapid growth, hybridization ability, and ease of propagation. Determining poplar cultivated areas and mapping their geographical distributions is critical for planners and decision-makers to increase the ecological and economic benefits of poplar trees. Due to the biodiversity of each geographical region and seasonal vegetation variations, classification based on remotely sensed imagery is essential for cropland monitoring. The main goal of this study is to evaluate the potential of high-resolution multi-temporal (growing season and end of the growing season) Worldview-3 imagery in mapping poplar plantations in the Akyazı district of Sakarya, Turkey. For this purpose, pixel- and object-based image analysis with up-to-date ensemble learning algorithms, namely random forest (RF), categorical boosting (CB), and extreme gradient boosting tree (XGB), were employed for mapping poplar fields. Results indicated that the object-based classification approach provided statistically significant improvements in map-level (about 4%) and class-level accuracy (e.g., approximately 7% and %2 for poplar and young poplar classes, respectively) than pixel-based classification. While the CB performed superior classification performance for the object-based approach (92.56%), the highest classification performance was obtained with the XGB algorithm for the pixel-based approach (90.42%) for the end of the growing season data. McNemar’s statistical test also confirmed that the performances of CB and XGB algorithms were statistically similar in pixel-based classification. Finally, analysis of multi-season images revealed that sensitivity of the vegetation phenology and seasonal effects considerably affect the separability of poplar tree species.

Agricultural Land Abandonment in Bulgaria: A Long-Term Remote Sensing Perspective, 1950–1980

Agricultural land abandonment is a globally significant threat to the sustenance of economic, ecological, and social balance. Although the driving forces behind it can be multifold and versatile, rural depopulation and urbanization are significant contributors to agricultural land abandonment. In our chosen case study, focusing on two locations, Ruen and Stamboliyski, within the Plovdiv region of Bulgaria, we use aerial photographs and satellite imagery dating from the 1950s until 1980, in connection with official population census data, to assess the magnitude of agricultural abandonment for the first time from a remote sensing perspective. We use multi-modal data obtained from historical aerial and satellite images to accurately identify Land Use Land Cover changes. We suggest using the rubber sheeting method for the geometric correction of multi-modal data obtained from aerial photos and Key Hole missions. Our approach helps with precise sub-pixel alignment of related datasets. We implemented an iterative object-based classification approach to accurately map LULC distribution and quantify spatio-temporal changes from historical panchromatic images, which could be applied to similar images of different geographical regions.

Mangrove Area Delineation using Object-Based Classification on Sentinel-2 Imagery: Tuba Island, Langkawi

Abstract Pixel misclassification is a common problem when satellite imagery extracts land-use and land cover classes. Accurate image classification for mangrove areas is essential for management and monitoring to preserve the mangrove ecosystem and expedite the mangrove area delineation process. Therefore, this study aims to i) identify suitable segmentation parameters value to delineate the mangrove area and ii) classify young and mature mangrove trees using the object-based classification (OBIA) approach at Tuba Island, Langkawi, Malaysia. This research applied Support Vector Machine (SVM) based on an object-based method using Sentinel-2A image and segmentation parameters value of scale, compactness, shape, and Gray Level Co-occurrence Matrix (GLCM) mean were tested. Measured tree diameter at breast height (DBH) is used to verify the mangrove tree delineated on the Sentinel-2A image. Segmentation parameters setting of shape (0.2), compactness (0.2), and scale (50) shows minimum errors with mangrove delineation 9.279% as compared to the Global Forest Watch (GFW) data while GLCM mean appropriate to determine the young and mature mangrove tree. The finding of this study will help the Department of Fisheries Malaysia and agritourism to maintain the mangrove ecosystem and enhance the fisheries industry.

Supporting Pro-Poor Reforms of Agricultural Systems in Eastern DRC (Africa) with Remotely Sensed Data: A Possible Contribution of Spatial Entropy to Interpret Land Management Practices

In the eastern Democratic Republic of Congo, agriculture represents the most important economic sector, and land control can be considered a perpetual source of conflict. Knowledge of the existing production system distribution is fundamental for both informing national land tenure reforms and guiding more effective agricultural development interventions. The present paper focuses on existing agricultural production systems in Katoyi collectivity, Masisi territory, where returning Internally and Externally Displaced People are resettling. We aim to define a repeatable methodology for building evidence-based and updated knowledge concerning the spatial distribution of the two existing production systems: subsistence-oriented agriculture (SOA) and business-oriented agriculture (BOA). To this aim, we used a supervised object-based classification approach on remotely sensed Sentinel-2 imagery to classify land cover. To classify production systems further within the “agriculture” and “pasture” land use classes, binary classification based on an entropy value threshold was performed. An iterative approach was adopted to define the final HNDVI threshold that minimised commission and omission errors and maximised overall accuracy and class separability. The methodology achieved acceptable observed accuracy (OA equal to 80–90% respectively for agricultural and pasture areas) in the assessment. SOA and BOA respectively covered 24.4 and 75.6% of the collectivity area (34,606 ha). The results conclude that land use and entropy analysis can draw an updated picture of existing land distribution among different production systems, supporting better-adapted intervention strategies in development cooperation and pro-poor agrarian land tenure reforms in conflict-ridden landscapes.

Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors

ABSTRACT Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-m resolution LULC map was produced for the Tigris-Euphrates basin. In total, 1184 Landsat scenes were processed within the Google Earth Engine (GEE). For the collection of ground truth data, differential manifestations of green cover were considered by dividing the study area into five climatic regions and the training samples were taken from each sub-region. To account for the temporal variation of LULC types, six two-month interval composite layers, including the spectral and thermal bands of Landsat-8, texture and spectral indices, as well as topographic factors were created for the target year 2019. Image segmentation and classification were performed using the simple non-iterative clustering (SNIC) and Random Forest (RF) algorithms, respectively. A computationally effective parallel processing approach was developed, which created a number of tiles and sub-tiles and a bulk command was converted into smaller parallel commands. The generated LULC map showed a satisfactory overall accuracy of 91.7%, with the highest User’s accuracy in water and wetland, and the lowest in rainfed crop and rangeland and the highest Producer’s accuracy in water and barren areas, and the lowest in garden and rangeland. This study highlights the necessity of using multi-temporal data for LULC mapping, in particular, multi-temporal NDVI, for the separation of different green cover types in arid and semi-arid environment.

Detecting and mapping mature, medium, and young age date palms in the arid lands of Abu Dhabi, using hierarchical integrated approach (HIA)

The main objective of the current study was to map dispersed date palm (DP) plantations in the Emirate of Abu Dhabi/United Arab Emirates (UAE), at different age stages: young (<5 years), medium (5–10 years), and mature (≥10 years), using multi-source/multi-sensor data in hierarchical integrated approach (HIA) developed and implemented for the purpose of this study. We initially succeeded in delineating and mapping “mature” DP plantations with acceptable accuracy using Landsat-8 OLI data. However, at this stage, our approach failed to depict “young” and “medium” DP with such level of accuracy, because of inadequate sensor resolution at such level of detail. Consequently, an object-based classification (OBC) approach was implemented using sub-meter WorldView-2 (WV-2) imagery, to depict and map medium and young aged palms in targeted DP plantations. Geographic Information Systems (GIS) was integrated to convert from raster to vector formats, allowing for manual editing of certain young DP polygons hence, increasing the accuracy of their delineation. The outcomes of the implemented approach were the creation of detailed and accurate maps of DP at the three age stages, for the first time in the region. The produced maps were validated using existing ancillary data and field checks. The overall accuracies for young, medium, and mature DP plantations were 86.8%, 88% and 90.7%, respectively; while for mixed-ages DP the value reached up to 94.5%, with an Overall Kappa Statistic estimated at 0.888.

Urban Mapping Performance of Sentinel-2A Remote Sensing System in Malaysia

Sentinel-2A remote sensing satellite system was recently launched, thereby providing free global remote sensing data in a similar way to Landsat systems. The mission enables the acquisition of 10 m spatial resolution global data; however, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban area in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets by use of Wavelet transform (WT), Brovey transform (BT), and principal component analysis. Pixel- and object-based classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentiel-2A data provide superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77% by SVM, followed by 65.27% using DT. BT produces the highest OA for the fusion images of 78.40% with SVM and 52.21% with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33% by SVM, followed by 76.38% using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35% with SVM, followed by 65.66% with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and is useful for future long-term mapping applications

An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

COMPARISON OF PIXEL AND OBJECT BASED CLASSIFICATION METHODS ON RAPIDEYE SATELLITE IMAGE

The aim of this study is to evaluate the classification performances of land use/land cover (LULC) classification methods by comparing the results of pixel and object-based classification approaches on RapidEye satellite image. Pixel-based classification was carried out in ERDAS Imagine 10.4 using the Maximum Likelihood-supervised approach, whilst object-based classification was performed in e-Cognition Developer 64 using the nearest neighbour-supervised classification method. A LULC map of eight classes was created in both methods. While the accuracy for thematic LULC classes varied in both methods, the overall accuracy and kappa values of LULC maps for pixel and object-based classification methods were 58.39%-0.45 and 89.58%-0.86, respectively. Accuracy assessments and comparative results showed that object-based classification gives better results for thematic LULC classes as well as the overall accuracy of LULC maps. Even though pixel-based classification method was good at mapping many thematic classes, there were misclassifications between natural/semi-natural LULC classes. These results can be attributed to parameters set by users, such as the number of control points, etc. However, the capacity of object-based classification method to include auxiliary data (e.g. DEM, NDVI) increases the accuracy of LULC maps with high-resolution satellites.

Future scenarios based on a CA-Markov land use and land cover simulation model for a tropical humid basin in the Cerrado/Atlantic forest ecotone of Brazil

Recently, the advancement of agriculture in Brazil has caused very serious problems, such as deforestation and an increase in the amount of pesticides and suspended sediment that have negatively impacted the environment, especially in the Prata River basin, which is located in the Serra da Bodoquena region, Brazil. Thus, the objective of this study was to analyze the future changes in land use and land cover of the advancement of agriculture in the native vegetation areas of the Cerrado/Atlantic forest ecotone in the Prata River basin in 2033, 2050, 2080 and 2100. To map the future land use and land cover (LULC) in the study area, an object-based classification approach was used on Landsat satellite imagery from 1986, 1999, 2007 and 2016. The future LULC scenarios were created with a cellular automata-Markov forecast model. The simulation results of the LULC changes were considered satisfactory, as evidenced by the values obtained from the kappa for agreement (κstandard) = 0.73, kappa for no information (κno) = 0.79 and kappa for grid-cell level location (κlocation) = 0.85 indices. The modeled future scenarios of LULC indicated the advancement of crop agriculture and decreases in wetlands (banhado), savannahs, riparian forests, seasonal semideciduous forests and wet grasslands, which can be considered a warning about the loss of biodiversity of fauna and flora in the Serra da Bodoquena National Park, mainly in the Prata River basin.

Improved Classification of Urban Trees Using a Widespread Multi-Temporal Aerial Image Dataset

Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high resolution red, green, and blue (RGB) aerial images from a commercial vendor and publicly available LiDAR data. Classifications based on these data were compared with classifications based on World View 2 satellite imagery, which is commonly used for this task but also more expensive. An object-based classification approach was used whereby tree canopies were segmented using LiDAR, and a street tree database was used for generating training and testing datasets. Overall accuracy using multi-temporal aerial images and LiDAR was 70%, which was higher than the accuracy achieved with World View 2 imagery and LiDAR (63%). When all data were used, classification accuracy increased to 74%. Taxa identified with high accuracy included Acer platanoides and Gleditsia, and taxa that were identified with good accuracy included Acer, Platanus, Quercus, and Tilia. Our results show that this large catalogue of multi-temporal aerial images can be leveraged for urban tree identification. While classification accuracy rates vary between taxa, the approach demonstrated can have practical value for socially or ecologically important taxa.

Comparative Analysis of Pixel and Object Based Classification Approach for Rapid Landslide Delineation with the Aid of Open Source Tools in Garhwal Himalaya

Abstract Landslides are the natural geomorphic processes that are essential for the landscape development. Loss of tremendous amount of life, natural resources as well as property has made this phenomenon a natural disaster. Number of researches have been done to understand this phenomenon and how to overcome this calamity with the aid of upcoming modern information services and technological advancements. Despite all these, there are still certain undocumented slope failure events due to inaccessibility and lack of a proper database. With the advancement of satellite remote sensing and geographical information system (GIS), it has certainly become easier to monitor and prepare landslide database especially in rough and rugged terrain of the Himalaya for delineation of risk zones. Free availability of high-resolution images and an open source efficient software have certainly been proved advantageous for this purpose. This paper aims at quick and accurate landslide inventory mapping, using high resolution Sentinel 2 data along with normalized difference vegetation index (NDVI), over which unsupervised and object-based image analysis (OBIA) was done to extract landslide features in an efficient manner. Both these processes were achieved with the aid of open source SAGA (System for Automated Geoscientific Analyses) software. The performances of these classifications were analyzed for their quantitative reliability in extracting the landslide features in two different areas (test area 1 and 2). In this, the object based image classification outstands, with the overall accuracy percentage difference of 17.11% higher than pixel-based classification approach in test area 1 and about 21% higher in test area 2.

National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images

Spatially and thematically explicit information of wetlands is important to understanding ecosystem functions and services, as well as for establishment of management policy and implementation. However, accurate wetland mapping is limited due to lacking an operational classification system and an effective classification approach at a large scale. This study was aimed to map wetlands in China by developing a hybrid object-based and hierarchical classification approach (HOHC) and a new wetland classification system for remote sensing. Application of the hybrid approach and the wetland classification system to Landsat 8 Operational Land Imager data resulted in a wetland map of China with an overall classification accuracy of 95.1%. This national scale wetland map, so named CAS_Wetlands, reveals that China’s wetland area is estimated to be 451,084 ± 2014 km2, of which 70.5% is accounted by inland wetlands. Of the 14 sub-categories, inland marsh has the largest area (152,429 ± 373 km2), while coastal swamp has the smallest coverage (259 ± 15 km2). Geospatial variations in wetland areas at multiple scales indicate that China’s wetlands mostly present in Tibet, Qinghai, Inner Mongolia, Heilongjiang, and Xinjiang Provinces. This new map provides a new baseline data to establish multi-temporal and continuous datasets for China’s wetlands and biodiversity conservation.

Analysing temporal and spatial urban sprawl change of Bursa city using landscape metrics and remote sensing

This paper aims to analyse the effect of spatio-temporal urban dynamics of Bursa city in terms of urban development trends and landscape metrics on the land use/cover change. Four different remotely sensed data recorded in 1979, 1989, 2000, 2013 and future simulation map of 2040 were used for the analysis. SLEUTH model in the frame of cellular automata was adopted for the future development. Object-based classification approach was used to extract the land use/cover maps and determine the quantity and quality of change in order to identify the land degradation. Eight urban landscape metrics were calculated from current and future land use/cover classification data. General phases of diffusion and coalescence in urban sprawl were revealed through the metric calculations. The most devastating change was defined on the agricultural lands. Metric results indicated that the irregular urban growth leads to an increase in patch number which ended up with the land degradation. Although the urbanization pattern mostly moved from the “seed” area to outwards for different time periods, the new spreading centres for the individual patches were observed for the future urban development. The empirical results and findings for historical and future land use/cover provided an insight to urban sprawl characteristics and further development of sustainable urban planning studies.