MODIS Data-based Crop Classification using Selective Hierarchical Classification

Kim, Yeseul; Lee, Kyung-Do; Na, Sang-Il; Hong, Su Young; Park, No-Wook; Yoo, Hee Young

doi:10.7780/kjrs.2016.32.3.3

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Of course, the CNN model has a sample size requirement, meaning that more samples are needed to train a more stable model [ 68 ]; therefore, deep learning algorithms outperform traditional machine learning algorithms when the sample size is sufficient [ 69 , 70 ]. CNN can be used as a first choice to identify the level of damage of Erannis jacobsoni Djak pests.…”

Section: Discussionmentioning

confidence: 99%

Potential of Unmanned Aerial Vehicle Red–Green–Blue Images for Detecting Needle Pests: A Case Study with Erannis jacobsoni Djak (Lepidoptera, Geometridae)

Bai,

Huang,

Dashzebeg

et al. 2024

Insects

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Erannis jacobsoni Djak (Lepidoptera, Geometridae) is a leaf-feeding pest unique to Mongolia. Outbreaks of this pest can cause larch needles to shed slowly from the top until they die, leading to a serious imbalance in the forest ecosystem. In this work, to address the need for the low-cost, fast, and effective identification of this pest, we used field survey indicators and UAV images of larch forests in Binder, Khentii, Mongolia, a typical site of Erannis jacobsoni Djak pest outbreaks, as the base data, calculated relevant multispectral and red–green–blue (RGB) features, used a successive projections algorithm (SPA) to extract features that are sensitive to the level of pest damage, and constructed a recognition model of Erannis jacobsoni Djak pest damage by combining patterns in the RGB vegetation indices and texture features (RGBVI&TF) with the help of random forest (RF) and convolutional neural network (CNN) algorithms. The results were compared and evaluated with multispectral vegetation indices (MSVI) to explore the potential of UAV RGB images in identifying needle pests. The results show that the sensitive features extracted based on SPA can adequately capture the changes in the forest appearance parameters such as the leaf loss rate and the colour of the larch canopy under pest damage conditions and can be used as effective input variables for the model. The RGBVI&TF-RF440 and RGBVI&TF-CNN740 models have the best performance, with their overall accuracy reaching more than 85%, which is a significant improvement compared with that of the RGBVI model, and their accuracy is similar to that of the MSVI model. This low-cost and high-efficiency method can excel in the identification of Erannis jacobsoni Djak-infested regions in small areas and can provide an important experimental theoretical basis for subsequent large-scale forest pest monitoring with a high spatiotemporal resolution.

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Section: Discussionmentioning

confidence: 99%

Potential of Unmanned Aerial Vehicle Red–Green–Blue Images for Detecting Needle Pests: A Case Study with Erannis jacobsoni Djak (Lepidoptera, Geometridae)

Bai,

Huang,

Dashzebeg

et al. 2024

Insects

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“…The study area covers most areas of the ECS (26)(27)(28)(29)(30)(31)(32)(33) • N, 119-126 • E), including the Yangtze River Estuary (YRE) and the coasts of Shanghai Municipality and Zhejiang Province (Figure 1). Most of the coastal ocean has a depth of less than 50 m and is influenced by the Yangtze River plume, the Taiwan Strait Warm Current, the Kuroshio Current, and the coastal current along the Zhejiang coast [29,30].…”

Section: Study Area and In Situ Measurementsmentioning

confidence: 99%

Assessment of VIIRS on the Identification of Harmful Algal Bloom Types in the Coasts of the East China Sea

Tao

Liu³

et al. 2022

Remote Sensing

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Visible Infrared Imaging Radiometer Suite (VIIRS) data were systematically evaluated and used to detect harmful algal bloom (HAB) and classify algal bloom types in coasts of the East China Sea covered by optically complex and sediment-rich waters. First, the accuracy and spectral characteristics of VIIRS retrieved normalized water-leaving radiance or the equivalent remote sensing reflectance from September 2019 to October 2020 that were validated by the long-term observation data acquired from an offshore platform and underway measurements from a cruise in the Changjiang Estuary and adjacent East China Sea. These data were evaluated by comparing them with data from the Moderate-Resolution Imaging Spectroradiometer. The bands of 486, 551, and 671 nm provided much higher quality than those of 410 and 443 nm and were more suitable for HAB detection. Secondly, the performance of four HAB detection algorithms were compared. The Ratio of Algal Bloom (RAB) algorithm is probably more suitable for HAB detection in the study area. Importantly, although RAB was also verified to be applicable for the detection of different kinds of HAB (Prorocentrum donghaiense, diatoms, Ceratium furca, and Akashiwo sanguinea), the capability of VIIRS in the classification of those algal species was limited by the lack of the critical band near 531 nm.

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“…Crop cover data ( Figure 1b) were collected from 2000 to 2013 in Illinois using the NASS database (https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php). The crop cover data in China ( Figure 2b) were generated using remote sensing [30]. Crop areas were identified from the MODIS Land cover type product (MCD12Q1).…”

Section: Crop Cover Datamentioning

confidence: 99%

Using MODIS Data to Predict Regional Corn Yields

Ban

Kim

Park

et al. 2016

Preprint

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A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data product, which are 8-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season [end of DOY (EOD)] was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield; the yield model (YP) used LAD from predicted emergence to maturity, and the yield model (YF) used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed a similar or better performance, even for the much earlier yield prediction at DOY 257, compared to that of the YF model. In conclusion, the phenology and yield models were developed based only on logistic changes in remote sensing-derived LAD, and predicted phenological dates and corn yields with considerable accuracy and precision for the two regions selected for this study. However, these models must be examined for spatial portability in more diverse agro-climatic regions.

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MODIS Data-based Crop Classification using Selective Hierarchical Classification

Cited by 3 publications

References 26 publications

Potential of Unmanned Aerial Vehicle Red–Green–Blue Images for Detecting Needle Pests: A Case Study with Erannis jacobsoni Djak (Lepidoptera, Geometridae)

Potential of Unmanned Aerial Vehicle Red–Green–Blue Images for Detecting Needle Pests: A Case Study with Erannis jacobsoni Djak (Lepidoptera, Geometridae)

Assessment of VIIRS on the Identification of Harmful Algal Bloom Types in the Coasts of the East China Sea

Using MODIS Data to Predict Regional Corn Yields

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