Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Hu, Qiong; Wu, Wenbin; Xia, Tian; Yu, Qiangyi; Yang, Peng; Li, Zhengguo; Song, Qian

doi:10.3390/rs5116026

Cited by 264 publications

(164 citation statements)

References 50 publications

Supporting

Mentioning

152

Contrasting

Unclassified

Order By: Relevance

“…We also interpreted the visual criteria of each land cover class (Table 1) in the satellite imagery of Quickbird and Google Earth [38]. Since GE launched in 2005, many studies have been conducted using GE to explore the reference data [15,39]. The hillside field and unstocked forest still have confusing visual characteristics in satellite images.…”

Section: Training Samples Collectionmentioning

confidence: 99%

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

et al. 2016

View full text Add to dashboard Cite

Abstract:Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify specific types of deforestation in North Korea, and to increase the accuracy of classification, using phenological characteristics extracted with multi-index and random forest algorithms. The mapping of deforestation area based on RF was carried out by merging phenology-based multi-indices (i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference soil index (NDSI)) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products and topographical variables. Our results showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87. In particular, for forest and farm land categories with similar phenological characteristic (e.g., paddy, plateau vegetation, unstocked forest, hillside field), this approach improved the classification accuracy in comparison with pixel-based methods and other classes. The deforestation types were identified by incorporating point data from high-resolution imagery, outcomes of image classification, and slope data. Our study demonstrated that the proposed methodology could be used for deciding on the restoration priority and monitoring the expansion of deforestation areas.

show abstract

Section: Training Samples Collectionmentioning

confidence: 99%

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

et al. 2016

View full text Add to dashboard Cite

show abstract

“…For the challenges due to crop variability and pixel heterogeneity, traditional pixel-based classification methods are unable to incorporate the detailed spatial information, which limits their application mainly in the regions where crop fields are fragmented with high spectral variability [27,28]. To overcome the "salt-and-pepper" effect, object-based approaches have been increasingly implemented in remote-sensed image analysis [29,30].…”

Section: Introductionmentioning

confidence: 99%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

show abstract

“…GE provides free access to high-resolution satellite imagery. In addition to being used as "pictures" for visualization, GE has been recognized as a significant resource for ground-truth data [44], improving visual interpretation, and even classifying complex LULC types [45]. In order to obtain better reference data, the time stamps of the GE images we chose were as close as possible to those of the original images It has been suggested that a minimum 50 samples (or pixels) of each class should be included in error analyses [16].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China

Hua

Zhang

Chen

et al. 2017

IJGI

View full text Add to dashboard Cite

Accurate mapping of temporal changes in urban land use and land cover (LULC) is important for monitoring urban expansion and changes in LULC, urban planning, environmental management, and environmental modeling. In this study, we present a feature-based approach of the decision tree classification (FBA-DTC) method for mapping LULC based on spectral and topographic information. Landsat 5 TM and Land 8 OLI images were employed, and the technique was applied to the coastal city of Xiamen, China. The method integrates multi-spectral features such as SAVI (soil adjustment vegetation index), NDWI (normalized water index), MNDBaI (modified normalized difference barren index), BI (brightness index), and WI (wetness index), with topographic features including DEM and slope. In addition, the new approach distinguishes between fallow land and cropland, and separates high-rise buildings from beaches and water bodies. Several of the FBA-DTC parameters (or rules) from 1997 to 2015 remained constant (i.e., DEM and slope), whereas others changed slightly. WI was negatively related to percent area of beach land, and BI was negatively related to percent area of arable land. The FBA-DTC method had an average user's accuracy (UA) of 91.36% for built-up land, an average overall accuracy (OA) of 92.13%, and a Kappa coefficient (KC) of 0.90 for the period from 2003 to 2015, representing respective increases of 15.87%, 10.17%, and 0.13, compared with values calculated using maximum likelihood classification (MLC). Over the past 12 years, built-up land increased from 23.67% to 43.17% owing to occupation of coastal reclamation, arable land, and forest land. The FBA-DTC method presented here is a valuable technique for evaluating urban growth and changes in LULC classification for coastal cities.

show abstract

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Cited by 264 publications

References 50 publications

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China

Contact Info

Product

Resources

About