Crop discrimination with multitemporal SPOT/HRV data in the Saga Plains, Japan

Murakami, Takuhiko; Ogawa, Shigeo; Ishitsuka, N.; Kumagai, Koji; Saito, Gunzi

doi:10.1080/01431160151144378

Cited by 115 publications

(60 citation statements)

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“…A second aspect is the dependency on the image acquisition dates when different vegetation classes are separated using phenological NDVI information [40]. As described in Section 3.2.2 we balanced missing acquisition dates by annual mean NDVI values.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

“…Schematic representation of the rule sets for the hierarchical decision trees. The reliability of classification results decreases in case of incomplete time series, e.g., when one image of a relevant phenological stage is missing [40]. To account for the varying acquisition dates for which Landsat data was available within one year, annual mean NDVI values were used in the rules of the decision trees.…”

Section: Knowledge-based Detection Of Irrigated Landmentioning

confidence: 99%

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Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series

Edlinger

Conrad

Lamers³

et al. 2012

Remote Sensing

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Abstract:The expansion of irrigated agriculture during the Soviet Union (SU) era made Central Asia a leading cotton production region in the world. However, the successor states of the SU in Central Asia face on-going environmental damages and soil degradation that are endangering the sustainability of agricultural production. With Landsat MSS and TM data from 1972/73, 1977, 1987, 1998, and 2000 the expansion and densification of the irrigated cropland could be reconstructed in the Kashkadarya Province of Uzbekistan, Central Asia. Classification trees were generated by interpreting multitemporal normalized difference vegetation index data and crop phenological knowledge. Assessments based on image-derived validation samples showed good accuracy. Official statistics were found to be of limited use for analyzing the plausibility of the results, because they hardly represent the area that is cropped in the very dry study region. The cropping area increased from OPEN ACCESS Remote Sens. 2012, 4 3973 134,800 ha in 1972/73 to 470,000 ha in 2009. Overlaying a historical soil map illustrated that initially sierozems were preferred for irrigated agriculture, but later the less favorable solonchaks and solonetzs were also explored, illustrating the strategy of agricultural expansion in the Aral Sea Basin. Winter wheat cultivation doubled between 1987 and 1998 to approximately 211,000 ha demonstrating its growing relevance for modern Uzbekistan. The spatial-temporal approach used enhances the understanding of natural conditions before irrigation is employed and supports decision-making for investments in irrigation infrastructure and land cultivation throughout the Landsat era.

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Section: Accuracy Assessmentmentioning

confidence: 99%

Section: Knowledge-based Detection Of Irrigated Landmentioning

confidence: 99%

Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series

Edlinger

Conrad

Lamers³

et al. 2012

Remote Sensing

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“…In this study, we used the JM distance to measure the period-by-period separability for each pair of crops, because previous research has shown that JM distance can provide a more accurate classification than other distance measures, such as Euclidean distance or divergence [10,19]. The JM distance between a pair of class-specific functions is given by:…”

Section: Extension Of the Jeffries-matusita Distancementioning

confidence: 99%

“…For example, Conrad, et al [33] showed that using two images from a single sensor to map dominating crops could produce acceptable accuracy. However, in high-agrodiversity regions, classification results could be further improved by a fusion of Landsat data with remotely sensed data from other sensing systems and selecting the optimal temporal steps [10].…”

Section: Separability Of Different Crops and Optimal Time Period Selementioning

confidence: 99%

“…At medium spatial resolution (10-100 m), multi-temporal Landsat-5 Thematic Mapper (TM) images, with 30-m spatial resolution and a 16-day revisit frequency, have a proven potential for crop classification [7,8]. Although images of key time-periods are sufficient for accurate crop mapping [9][10][11], misclassification may still occur, because cloud-free images that cover all critical periods are difficult to obtain from large areas using a single sensor, such as the Landsat-5 TM.…”

Section: Introductionmentioning

confidence: 99%

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The Potential of Time Series Merged from Landsat-5 TM and HJ-1 CCD for Crop Classification: A Case Study for Bole and Manas Counties in Xinjiang, China

et al. 2014

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Time series data capture crop growth dynamics and are some of the most effective data sources for crop mapping. However, a drawback of precise crop classification at medium resolution (30 m) using multi-temporal data is that some images at crucial time periods are absent from a single sensor. In this research, a medium-resolution, 15-day time series was obtained by merging Landsat-5 TM and HJ-1 CCD data (with similar radiometric performances in multi-spectral bands). Subsequently, optimal temporal windows for accurate crop mapping were evaluated using an extension of the Jeffries-Matusita (JM) distance from the merged time series. A support vector machine (SVM) was then used to compare the classification accuracy of the optimal temporal windows and the entire time series. In addition, different training sample sizes (10% to 90% of the entire training sample in 10% increments; five repetitions for each sample size) were used to investigate the stability of optimal temporal windows. The results showed that time series in optimal temporal windows can achieve high classification accuracies. The optimal temporal windows were robust when the training sample size was sufficiently large. However, they were not stable when the sample size was too small (i.e., less than 300) and may shift in different agro-ecosystems, because of different classes. In addition, merged time series had higher temporal resolution and were more likely to comprise the optimal temporal periods than time series from single-sensor data. Therefore, the use of merged time series increased the possibility of precise crop classification.

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Extracting Winter Wheat Planting Area Based on Cropping System with Remote Sensing

Sui

Xiao-dong

et al. 2011

Computer and Computing Technologies in Agriculture IV

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Winter wheat is one kind of important crop in China. It's planting area is one key element to explain yield change. To obtain winter wheat planting area as soon as possible can provide scientific reference for our country's making related policy. Basing on the cropping system in Shandong province, winter wheat is divided into two kinds "winter wheat sowed by machine-maize" and "people broadcast winter wheat-rice". Using MODIS data, NDVI characters of winter wheat, garlic, greenhouse vegetable, from sowing till overwintering stage were analyzed. Together with NDVI characters of former stubble crops in middle September, extracting requirements were set up for winter wheat planting area which was sowed by machine this year. In view of the spectrum similarity between rice wheat and greenhouse vegetable from sowing stage till overwintering stage, rice wheat planting area of former year was extracted relying on the character of biomass rapid growth at jointing stage. Because of the "people broadcast winter wheat-rice" cropping system is very fixed in Shandong province, then the rice wheat planting area of former year can take the place of the rice wheat planting area this year. Two kinds of winter wheat area were merged, and tested by 284 groups of located spots data, the accuracy reached 94.01%. The result showed that it is feasible to extract winter wheat area before overwintering stage, and the time is 4 months earlier than using jointing stage NDVI.

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Crop discrimination with multitemporal SPOT/HRV data in the Saga Plains, Japan

Cited by 115 publications

References 0 publications

Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series

Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series

The Potential of Time Series Merged from Landsat-5 TM and HJ-1 CCD for Crop Classification: A Case Study for Bole and Manas Counties in Xinjiang, China

Extracting Winter Wheat Planting Area Based on Cropping System with Remote Sensing

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