Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM

Liu, Ping; Chen, Xi

doi:10.3390/ijgi8020086

Cited by 12 publications

(8 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies were conducted on maize extraction using optical satellite data, and relatively mature extraction methods were developed [ 29 , 30 ]. The SVM method, a non-parametric algorithm, is widely used to perform maize mapping with a high accuracy [ 26 , 31 , 32 , 33 ]. Considering the crop calendar and avoiding the lodging effect, the images taken before the lodging event were used to map the spatial distribution of maize using the SVM classifier.…”

Section: Methodsmentioning

confidence: 99%

“…The GF-1 satellite has a short revisit cycle of 4 days and a wide scanning swath of 800 km, which is important for observing the lodging phenomenon since lodging usually occurs instantly and over a large area. Table 1 provides an overview of the relevant GF-1 data [ 26 ]. Two images for Zhaodong City and one for Ningjiang District retrieved on 6 September 2020 were used to study the spectral characteristics of lodged maize and distinguish lodged maize from non-lodged maize in this paper.…”

Section: Study Area and Datamentioning

confidence: 99%

See 1 more Smart Citation

A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data

Chen

Sun

Pei

et al. 2022

Sensors

View full text Add to dashboard Cite

Crop lodging is a major destructive factor for agricultural production. Developing a cost-efficient and accurate method to assess crop lodging is crucial for informing crop management decisions and reducing lodging losses. Satellite remote sensing can provide continuous data on a large scale; however, its utility in detecting lodging crops is limited due to the complexity of lodging events and the unavailability of high spatial and temporal resolution data. Gaofen1 satellite was launched in 2013. The short revisit cycle and wide orbit coverage of the Gaofen1 satellite make it suitable for lodging identification. However, few studies have explored lodging detection using Gaofen1 data, and the operational application of existing approaches over large spatial extents seems to be unrealistic. In this paper, we discuss the identification method of lodged maize and explore the potential of using Gaofen1 data. An analysis of the spectral features after maize lodging revealed that reflectance increased significantly in all bands, compared to non-lodged maize. A spectral sum index was proposed to distinguish lodged and non-lodged maize. Two study areas were considered: Zhaodong City in Heilongjiang Province and Ningjiang District in Jilin Province. The results of the identified lodged maize from the Gaofen1 data were validated based on three methods: first, ground sample points exhibited the overall accuracies of 92.86% and 88.24% for Zhaodong City and Ningjiang District, respectively; second, the cross-comparison differences of 1.01% for Zhaodong City and 1.13% for Ningjiang District were obtained, compared to the results acquired from the finer-resolution Planet data; and third, the identified results from Gaofen1 data and those from farmer survey questionnaires were found to be consistent. The validation results indicate that the proposed index is promising, and the Gaofen1 data have the potential for rapid lodging monitoring.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Study Area and Datamentioning

confidence: 99%

A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data

Chen

Sun

Pei

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…It must be noted that different crops might have different phenologic states characterized by different NDVI values. Moreover, the crops can have different basal NDVI values [35][36][37]. These basal differences can cause misclassifications of the crop with regular vigour as a crop with low vigour leading to unneeded treatment.…”

Section: Relevance Of Proposed Methods For Intercropping Systems and ...mentioning

confidence: 99%

Methodology to Differentiate Legume Species in Intercropping Agroecosystems Based on UAV with RGB Camera

Parra

Mostaza-Colado²,

Marín³

et al. 2022

Electronics

View full text Add to dashboard Cite

Mixed crops are one of the fundamental pillars of agroecological practices. Row intercropping is one of the mixed cropping options based on the combination of two or more species to reduce their impacts. Nonetheless, from a monitoring perspective, the coexistence of different species with different characteristics complicates some processes, requiring a series of adaptations. This article presents the initial development of a procedure that differentiates between chickpea, lentil, and ervil in an intercropping agroecosystem. The images have been taken with a drone at the height of 12 and 16 m and include the three crops in the same photograph. The Vegetation Index and Soil Index are used and combined. After generating the index, aggregation techniques are used to minimize false positives and false negatives. Our results indicate that it is possible to differentiate between the three crops, with the difference between the chickpea and the other two legume species clearer than that between the lentil and the ervil in images gathered at 16 m. The accuracy of the proposed methodology is 95% for chickpea recognition, 86% for lentils, and 60% for ervil. This methodology can be adapted to be applied in other crop combinations to improve the detection of abnormal plant vigour in intercropping agroecosystems.

show abstract

“…Algorithms 2020, 13, x FOR PEER REVIEW 7 of 16 2.3.1. SVM Support vector machine (SVM) is a type of generalized linear classifier for binary classification by supervised learning, and has been widely used for remote sensing [37,40] and computer graphics [41,42]. It can find an optimal hyperplane in the feature space and divide the data into two categories (Figure 8).…”

Section: Svmmentioning

confidence: 99%

Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold

Wang

Cui

et al. 2020

Algorithms

View full text Add to dashboard Cite

In the case of marine accidents, monitoring marine oil spills can provide an important basis for identifying liabilities and assessing the damage. Shipborne radar can ensure large-scale, real-time monitoring, in all weather, with high-resolution. It therefore has the potential for broad applications in oil spill monitoring. Considering the original gray-scale image from the shipborne radar acquired in the case of the Dalian 7.16 oil spill accident, a complete oil spill detection method is proposed. Firstly, the co-frequency interferences and speckles in the original image are eliminated by preprocessing. Secondly, the wave information is classified using a support vector machine (SVM), and the effective wave monitoring area is generated according to the gray distribution matrix. Finally, oil spills are detected by a local adaptive threshold and displayed on an electronic chart based on geographic information system (GIS). The results show that the SVM can extract the effective wave information from the original shipborne radar image, and the local adaptive threshold method has strong applicability for oil film segmentation. This method can provide a technical basis for real-time cleaning and liability determination in oil spill accidents.

show abstract

Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM

Cited by 12 publications

References 41 publications

A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data

A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data

Methodology to Differentiate Legume Species in Intercropping Agroecosystems Based on UAV with RGB Camera

Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold

Contact Info

Product

Resources

About