Land Cover Mapping in Southwestern China Using the HC-MMK Approach

Lei, Guangbin; Li, Ainong; Bian, Jinhu; Zhang, Zhengjian; Jin, Huaan; Nan, Xi; Zhao, Wei; Wang, Jiyan; Cao, X.; Tan, Jianbo; Liu, Qiannan; Yu, Huan; Yang, Guangbin; Feng, Wang

doi:10.3390/rs8040305

Cited by 33 publications

(24 citation statements)

References 46 publications

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“…While pre-training and preparation time are relatively low for RGB image acquisition and RGB image analysis is combined with open source software which reduces the cost of digital image analysis, it took considerably time required to identify the precise threshold HSB colour profile of individual RGB images for the pixel-based supervised image analysis, making the RGB imaging technique unsuitable for large scale plant ground cover estimation.. However, this study suggests that the pixel-based supervised classification technique in ImageJ offers accurate plant ground cover estimation to assess perennial ryegrass persistence, particularly where perennial ryegrass ground cover is relatively low [16]. The multispectral sensor-based ground cover estimate based on eCognition software which converts homogenous pixels of digital images into objects that are classified into user-defined classes and this user-defined classification is one of the specific functions of eCognition that most of the currently available remote sensing software does not possess [32].…”

Section: Discussionmentioning

confidence: 91%

“…Technological developments over the last three decades have seen increased use of sensors for data collection [14], including plant canopy characteristics [15]. Sensor-based methods remove the inconsistency of conventional methods, offer large-scale data collection and provide a non-invasive, quick method for collecting vegetation cover data [16]. Use of high-resolution airborne images and image analysis software offer low-cost platforms to obtain ground-based information for ground cover estimation.…”

Section: Introductionmentioning

confidence: 99%

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An Object-Based Image Analysis Approach to Assess Persistence of Perennial Ryegrass (Lolium perenne L.) in Pasture Breeding

et al. 2019

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Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of the world, but it is prone to having poor persistence due to the incidence of abiotic and biotic stresses. This creates a challenge for livestock producers to use their agricultural lands more productively and intensively within sustainable limits. Breeding perennial ryegrass cultivars that are both productive and persistent is a target of forage breeding programs and will allow farmers to select appropriate cultivars to deliver the highest profitability over the lifetime of a sward. Conventional methods for the estimation of pasture persistence depend on manual ground cover estimation or counting the number of surviving plants or tillers in a given area. Those methods are subjective, time-consuming and/or labour intensive. This study aimed to develop a phenomic method to evaluate the persistence of perennial ryegrass cultivars in field plots. Data acquisition was conducted three years after sowing to estimate the persistence of perennial ryegrass using high-resolution aerial-based multispectral and ground-based red, green and blue(RGB) sensors, and subsequent image analysis. There was a strong positive relationship between manual ground cover and sensor-based ground cover estimates (p < 0.001). Although the manual plant count was positively correlated with sensor-based ground cover (p < 0.001) intra-plot plant size variation influenced the strength of this relationship. We conclude that object-based ground cover estimation is most suitable for use in large-scale breeding programs due to its higher accuracy, efficiency and repeatability. With further development, this technique could be used to assess temporal changes of perennial ryegrass persistence in experimental studies and on a farm scale.

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Section: Discussionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

An Object-Based Image Analysis Approach to Assess Persistence of Perennial Ryegrass (Lolium perenne L.) in Pasture Breeding

et al. 2019

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“…However, this algorithm was proposed for land cover mapping in a single mapping unit (such as a tile of Landsat-8 OLI image). The acquisition time of satellite images used in adjacent mapping units is often different, which would result in the spatial discontinuity of their classification results [48]. Therefore, when the OIC-MCE method was applied to land cover mapping in large areas, it must consider the problem of spatial discontinuity.…”

Section: Application Of the Proposed Oic-mce Methods In The Land Covermentioning

confidence: 99%

“…Further accuracy assessment is still in progress. result in the spatial discontinuity of their classification results [48]. Therefore, when the OIC-MCE 463 method was applied to land cover mapping in large areas, it must consider the problem of spatial 464 discontinuity.…”

Section: Application Of the Proposed Oic-mce Methods In The Land Covermentioning

confidence: 99%

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OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification

Lei

Bian

et al. 2020

Remote Sensing

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Land cover samples are usually the foundation for supervised classification. Unfortunately, for land cover mapping in large areas, only limited samples can be used due to the time-consuming and labor-intensive sample collection. A novel and practical Object-oriented Iterative Classification method based on Multiple Classifiers Ensemble (OIC-MCE) was proposed in this paper. It systematically integrated object-oriented segmentation, Multiple Classifier Ensemble (MCE), and Iterative Classification (IC). In this method, the initial training samples were updated self-adaptively during the iterative processes. Based on these updated training samples, the inconsistent regions (ICR) in the classification results of the MCE method were reclassified to reduce their uncertainty. Three typical case studies in the China-Pakistan Economic Corridor (CPEC) indicate that the overall accuracy of the OIC-MCE method is significantly higher than that of the single classifier. After five iterations, the overall accuracy of the OIC-MCE approach increased by 5.58%-8.38% compared to the accuracy of the traditional MCE method. The spatial distribution of newly added training samples generated by the OIC-MCE approach was relatively uniform. It was confirmed by ten repeated experiments that the OIC-MCE approach has good stability. More importantly, even if the initial sample size reduced by 65%, the quality of the final classification result based on the proposed OIC-MCE approach would not be greatly affected. Therefore, the proposed OIC-MCE approach provides a new solution for land cover mapping with limited samples. Certainly, it is also well suited for land cover mapping with abundant samples.Remote Sens. 2020, 12, 987 2 of 21 various homogeneous or heterogeneous data sources, advanced machine learning algorithms, and a variety of sampling and classification strategies have been introduced into land cover mapping researches [1,[5][6][7].Supervised classification is the most commonly used method in land cover mapping studies, especially for generating a regional or even a global land cover product [8]. Samples are the foundation of this method. The quantity and quality of samples would have a significant impact on the accuracy of land cover products [9,10]. There are many methods to collect samples, such as field survey, crowdsourcing, and manual interpretation [11]. In general, field survey has been a simple and practical method, but it cannot be conducted in inaccessible regions [12]. More importantly, it is time-consuming and labor-intensive. Manual interpretation of high-resolution remote sensing images, relying on the interpretation experiences of the interpreters, has been an effective way to fill the blank of field surveys. However, the subjectivity of the interpreter might affect the objectivity of samples [10]. Crowdsourcing was a new technology that helps to collect samples [13,14]. Unfortunately, it is still in the experimental stage at present, with few available samples and a lack of objective evaluation for their reliability...

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Land degradation and restoration in the arid and semiarid zones of China: Quantified evidence and implications from satellites

et al. 2018

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Quantified information on land degradation and restoration is needed to support policies for sustainable ecosystem management and socioeconomic development. The arid and semiarid zones of China (ASZC) have experienced significant land degradation, and in response to such degradation, multiple ecological projects were implemented. In this study, remote sensing was used to identify degrading areas and where the degraded areas have been restored from 1990 in this ecologically sensitive region. Specifically, we integrated datasets from the ChinaCover and Moderate Resolution Imaging Spectroradiometer products to characterize changes in vegetation, ecosystems, and environmental conditions. The results confirmed that deforestation, desertification, grassland degradation, wetland loss, and the encroachment upon farmlands were notable in the ASZC. Agricultural activity has cultivated extensive areas of natural ecosystems (46,474 km 2 ). The expansion of built-up lands in terms of national policies has destroyed large areas of natural (5,487 km 2 ) and agricultural (4,138 km 2 ) ecosystems and continually results in negative impacts on ecological conservation. China has restored some lands (7,732 km 2 from farmlands and 24,904 km 2 from barren lands) and improved ecosystems, as characterized by increases in the normalized different vegetation index, leaf area index, net primary productivity, and gross primary productivity values in the ASZC, especially after the implementation of many ecological projects. However, it is also necessary to document the conclusions and lessons from these projects to guide future ecological policies and projects in the context of the substantial new challenges facing the protection of this ecologically sensitive environment. KEYWORDS ecological restoration, land cover, land degradation, MODIS, the arid and semiarid zones of China

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Land Cover Mapping in Southwestern China Using the HC-MMK Approach

Cited by 33 publications

References 46 publications

An Object-Based Image Analysis Approach to Assess Persistence of Perennial Ryegrass (Lolium perenne L.) in Pasture Breeding

An Object-Based Image Analysis Approach to Assess Persistence of Perennial Ryegrass (Lolium perenne L.) in Pasture Breeding

OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification

Land degradation and restoration in the arid and semiarid zones of China: Quantified evidence and implications from satellites

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