Crop and land cover classification in Iran using Landsat 7 imagery

Akbari, M.; Mamanpoush, A.; Gieske, A.S.M.; Miranzadeh, M.; Torabi, M.; Salemi, H. R.

doi:10.1080/01431160600784192

Cited by 38 publications

(24 citation statements)

References 13 publications

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“…This is because single-date analysis in visible cropping intensity often does not take into account planting dates that vary from year to year. Therefore, multi-temporal analysis has greater potential to define irrigated areas [46]. Ultimately, classification results are conditional upon the temporal and spatial variability of the spectral signature of the land cover type in question, so suitable images must be available for the temporal approach to provide a complete inventory of all irrigated fields in a study area.…”

Section: Digital Image Classificationmentioning

confidence: 99%

Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

et al. 2010

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Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, is to synthesize principle findings and assess the state of the art. We take a taxonomic approach to group studies based on location, scale, inputs, and methods, in an effort to categorize different approaches within a logical framework. We seek to evaluate the ability of remote sensing to provide synoptic and timely coverage of irrigated lands in several spectral regions. We also investigate the value of archived data that enable comparison of images through time. This overview of the studies to date indicates that remote sensing-based monitoring of irrigation is at an intermediate stage of development at local scales. For instance, there is overwhelming consensus on the efficacy of vegetation indices in identifying irrigated fields. Also, single date imagery, acquired at peak growing season, may suffice to identify irrigated lands, although to multi-date image data are necessary for improved classification and to distinguish different crop types. At local scales, the mapping of irrigated lands with remote sensing is also strongly affected by the timing of image acquisition and the number of images used. At the regional and global scales, on the other hand, remote sensing has not been fully operational, as methods that work in one place and time are not necessarily transferable to other locations and periods. Thus, at larger scales, more work is required to indentify the best spectral indices, best time periods, and best classification methods under different climatological and cultural environments. Existing studies at regional scales also establish the fact that both remote sensing and national statistical approaches require further refinement with a substantial OPEN ACCESSRemote Sensing 2010, 2 2275 investment of time and resources for ground-truthing. An additional challenge in mapping irrigation across large areas occurs in fragmented landscapes with small irrigated and cultivated fields, where the spatial scale of observations is pitted against the need for high frequency temporal acquisitions. Finally, this review identifies passive and active microwave observations, advanced image classification methods, and data fusion including optical and radar sensors or with information from sources with multiple spatial and temporal characteristics as key areas where additional research is needed.

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Section: Digital Image Classificationmentioning

confidence: 99%

Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

et al. 2010

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“…The features were surface reflectance composited at specific events of the growing season when crops are expected to behave differently than other land covers do. Among the cropland discrimination studies [46][47][48][49], five temporal features were selected: the maximum of the red band (max red), the minimum and maximum of the Normalized Difference Vegetation Index (NDVI) and the increasing and decreasing slopes of the NDVI profile ( Figure 7b). Soil preparation practices, such as tillage and sowing, clear the land surface contrasting with naturally-vegetated areas and resulting in higher reflectance in the red band.…”

Section: Cropland Classificationmentioning

confidence: 99%

Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m

2016

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Abstract:Early warning systems for food security require accurate and up-to-date information on the location of major crops in order to prevent hazards. A recent systematic analysis of existing cropland maps identified priority areas for cropland mapping and highlighted a major need for the Sahelian and Sudanian agrosystems. This paper proposes a knowledge-based approach to map cropland in the Sahelian and Sudanian agrosystems that benefits from the 100-m spatial resolution of the recent PROBA-V sensor. The methodology uses five temporal features characterizing crop development throughout the vegetative season to optimize cropland discrimination. A feature importance analysis validates the efficiency of using a diversity of temporal features. The fully-automated method offers the first cropland map at 100-m using the PROBA-V sensor with an overall accuracy of 84% and an F-score for the cropland class of 74%. The improvements observed compared to existing cropland products are related to the hectometric resolution, to the methodology and to the quality of the labeling layer from which reliable training samples were automatically extracted. Classification errors are mainly explained by data availability and landscape fragmentation. Further improvements are expected with the upcoming enhanced cloud screening of the PROBA-V sensor.

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“…Among the spectral indices, the NDVI was commonly used because of its differential spectral response in presence of either irrigated or rainfed crops. The analysis of multiple satellite acquisitions over a growing season was shown to be more efficient, as it reflects the differences in phenological evolution between crops [107]. However, when a peak was observed within a known given small time period in an irrigation season, one image acquired at the right time may suffice to identify irrigated areas [92].…”

Section: Irrigationmentioning

confidence: 99%

“…The identification of irrigated areas is also easier to complete over arid or semi-arid regions. In these regions, almost all vegetative growth is linked to irrigation [107] and only a few classes have to be distinguished, whereas in humid areas, natural wetlands can be confused with irrigated areas [92].…”

Section: Irrigationmentioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

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Crop and land cover classification in Iran using Landsat 7 imagery

Cited by 38 publications

References 13 publications

Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m

Remote Sensing and Cropping Practices: A Review

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