Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

Li, Qingting; Wang, Cuizhen; Zhang, Bing; Lu, Linlin

doi:10.3390/rs71215820

Cited by 108 publications

(67 citation statements)

References 48 publications

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“…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

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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.

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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

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“…Most studies on Landsat land cover classification have reported the superior performance of OBIA in various landscapes such as urban areas [89,156], agricultural areas [79,85], forests [86,128] and wetlands [47,157]. The major advantage of OBIA is that it represents the classification units as real world objects on the ground and hence reduces the within class variability.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Landsat and MODIS was used to develop improved images for land cover classification in China and Southwest Missouri, Unites States of America (USA) [27,85]. AVHRR and MODIS have low spatial resolution ranging from 0.25 to 8 km [17,85,116]; however, these images have an advantage of having a high temporal resolution of one day [115,117]. On the other hand, Landsat images have a higher spatial resolution as compared to MODIS and AVHRR.…”

Section: Landsat Image Fusions In Land Cover Classificationmentioning

confidence: 99%

“…Object-based methods are commonly applied to images with high spatial resolution such as IKONOS, GeoEye, QuickBird and SPOT; however, this method has also been applied in land cover classification using medium resolution Landsat images [19,84,85]. For example, object-based classification was used on Landsat MSS, TM and ETM+ for land cover classification in Ethiopia and on urban sprawl in Eritrea [86,87].…”

Section: Object-based Approachmentioning

confidence: 99%

“…The development of effective fusion algorithms coupled with the advent of new remote sensing data such as advanced very high-resolution radiometer (AVHRR) and moderate-resolution imaging spectroradiometer (MODIS) has made Landsat images more useful in land cover classification [114,115]. For example, Landsat and MODIS was used to develop improved images for land cover classification in China and Southwest Missouri, Unites States of America (USA) [27,85]. AVHRR and MODIS have low spatial resolution ranging from 0.25 to 8 km [17,85,116]; however, these images have an advantage of having a high temporal resolution of one day [115,117].…”

Section: Landsat Image Fusions In Land Cover Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Developments in Landsat Land Cover Classification Methods: A Review

2017

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Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.

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Post‐socialist cropland changes and abandonment in Mongolia

et al. 2018

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Large-scale cropland changes have significant implications for global and national food supply as well as degradation in land resources. We examined cropland dynamics at the national scale in Mongolia over the last three decades using Google Earth Engine cloud computing and 11,360 Landsat satellite images. Our overarching goal was to develop the first comprehensive, high-resolution maps of Mongolia's cropland extent for two nominal years: 1990 at the peak of the socialist era production and 2014 after major socio-political and economic regime changes. By comparing the two cropland maps, we estimate cropland changes and identify areas of post-socialist cropland abandonment. Our cropland classifications produce high overall accuracies (overall weighted, statistical accuracies of 98% and 97%, respectively), and the cropland area estimates compare well with county-level crop area statistics (R 2 = 0.91, p value < .001).We estimate 38% cropland decline in Mongolia throughout the major crop regions but 18% increase in the central region due to increased concentration of crop production. The observed large-scale cropland abandonment would be expected to result in substantial land conversion to rangelands as well as land degradation in the abandoned fields, including soil erosion, increased contribution to desertification, and reduced species diversity. Future studies can monitor the long-term trajectory in the abandoned crop fields and recommend targeted restoration strategies.

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Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

Cited by 108 publications

References 48 publications

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

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

Developments in Landsat Land Cover Classification Methods: A Review

Post‐socialist cropland changes and abandonment in Mongolia

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