Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery

Stow, Douglas A.; Hamada, Yuki; Coulter, Lloyd L.; Anguelova, Zlatina

doi:10.1016/j.rse.2007.07.011

Cited by 109 publications

(48 citation statements)

References 24 publications

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“…However, the pixel-based classification results inevitably include "salt and pepper" noise and dis-jointed farm fragments in practice. Object-based analysis can improve salt and pepper effects and increase classification accuracies over pixel-based image classification [82,83]. Image segmentation gathers several similar neighbor pixels together as objects, and categorizes or labels objects, which would be further labelled as croplands or non-croplands in the integration step with pixel-based classification in Section 3.2.…”

Section: Recursive Hierarchical Image Segmentation (Rhseg)mentioning

confidence: 99%

“…Image segmentation gathers several similar neighbor pixels together as objects, and categorizes or labels objects, which would be further labelled as croplands or non-croplands in the integration step with pixel-based classification in Section 3.2. Image segmentation procedures have many implementation [82,83] with very high memory and CPU requirements. GEE provides APIs related to image segmentation such as region grow [84].…”

Section: Recursive Hierarchical Image Segmentation (Rhseg)mentioning

confidence: 99%

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Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

et al. 2017

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A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of

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Section: Recursive Hierarchical Image Segmentation (Rhseg)mentioning

confidence: 99%

Section: Recursive Hierarchical Image Segmentation (Rhseg)mentioning

confidence: 99%

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

et al. 2017

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“…Furthermore, replacement of pixel values belonging to the same segment by their means lowers the variance of the complete pixels' set (see Huygens theorem in Edwards and Cavalli-Sforza (1965) [27]). As a result, several studies have proven that object-based approaches can be very useful for mapping vegetation structure and to discriminate structural stages in vegetation [5,[28][29][30]. Furthermore, different authors claimed that OBIA is better suited for classifying VHR imagery compared to pixel-based methods [23,[31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms

et al. 2016

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Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify land cover components of African savanna in wet and dry season. We compared the performance of Object-Based Image Analysis (OBIA) and pixel-based approach with several algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification and regression trees (CART) and support vector machines (SVM). Results showed that classifications of WV-2 imagery produce high accuracy results (>77%) regardless of the applied classification approach. However, OBIA had a significantly higher accuracy for almost every classifier with the highest overall accuracy score of 93%. Amongst tested classifiers, SVM and RF provided highest accuracies. Overall classifications of the wet season image provided better results with 93% for RF. However, considering woody leaf-off conditions, the dry season classification also performed well with overall accuracy of 83% (SVM) and high producer accuracy for the tree cover (91%). Our findings demonstrate the potential of imagery like WorldView-2 with OBIA and advanced supervised machine-learning algorithms in seasonal fine-scale land cover classification of African savanna.

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“…It has achieved some improvements in land-cover completeness and mapping accuracy, compared to per-pixel image analysis [65][66][67]. However, the geoscene-based image analysis (GEOSIBA) proposed in this study aims to delineate and analyze functional zones by concentrating on object aggregations and functional-zone categories.…”

Section: A Comparison Between Geoscene-based Image Analysis and Geobiamentioning

confidence: 99%

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Zhang

Wang

et al. 2018

Remote Sensing

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Urban functional zones, such as commercial, residential, and industrial zones, are basic units of urban planning, and play an important role in monitoring urbanization. However, historical functional-zone maps are rarely available for cities in developing countries, as traditional urban investigations focus on geographic objects rather than functional zones. Recent studies have sought to extract functional zones automatically from very-high-resolution (VHR) satellite images, and they mainly concentrate on classification techniques, but ignore zone segmentation which delineates functional-zone boundaries and is fundamental to functional-zone analysis. To resolve the issue, this study presents a novel segmentation method, geoscene segmentation, which can identify functional zones at multiple scales by aggregating diverse urban objects considering their features and spatial patterns. In experiments, we applied this method to three Chinese cities-Beijing, Putian, and Zhuhai-and generated detailed functional-zone maps with diverse functional categories. These experimental results indicate our method effectively delineates urban functional zones with VHR imagery; different categories of functional zones extracted by using different scale parameters; and spatial patterns that are more important than the features of individual objects in extracting functional zones. Accordingly, the presented multiscale geoscene segmentation method is important for urban-functional-zone analysis, and can provide valuable data for city planners.

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Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery

Cited by 109 publications

References 24 publications

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

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