Mapping land cover gradients through analysis of hyper-temporal NDVI imagery

Ali, Amjad; Bie, C.A.J.M. de; Skidmore, Andrew K.; Scarrott, Rory; Hamad, Amina; Venus, V.; Lymberakis, Petros

doi:10.1016/j.jag.2012.10.001

Cited by 30 publications

(32 citation statements)

References 63 publications

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“…Monitoring vegetation productivity is important in assessing threats to environment and to ensure feed and food sustainability to humans and animals. Ali et al (2013) estimated vegetation productivity using normalized difference vegetation index (NDVI). It is an indicator of photosynthetic activity in a living plant.…”

Section: P Juliflora Trends In the Floodplains And Hillslopesmentioning

confidence: 99%

Temporal relationship between climate variability, Prosopis juliflora invasion and livestock numbers in the drylands of Magadi, Kenya

Kyuma¹,

Wahome²,

M³

et al. 2016

Afr. J. Environ. Sci. Technol.

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Section: P Juliflora Trends In the Floodplains And Hillslopesmentioning

confidence: 99%

Temporal relationship between climate variability, Prosopis juliflora invasion and livestock numbers in the drylands of Magadi, Kenya

Kyuma¹,

Wahome²,

M³

et al. 2016

Afr. J. Environ. Sci. Technol.

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“…Linear stretching was applied to generate the digital NDVI numbers (DN), where −1, the minimum NDVI value, was assigned to 0 and the maximum NDVI value of 1 to 255. The upper envelope of the NDVI time series was derived using the adaptive Savitzky-Golay filter in TIMESAT [53][54][55] (Figure S2) and the resulting time series was classified using the ISODATA [56] clustering algorithm through unsupervised classification [9,[57][58][59].…”

Section: Modis-based Rice System Stratification and Characterizationmentioning

confidence: 99%

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

et al. 2014

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Different rice crop information can be derived from different remote sensing sources to provide information for decision making and policies related to agricultural production and food security. The objective of this study is to generate complementary and comprehensive rice crop information from hypertemporal optical and multitemporal high-resolution SAR imagery. We demonstrate the use of MODIS data for rice-based system characterization and X-band SAR data from TerraSAR-X and CosmoSkyMed for the identification and detailed mapping of rice areas and flooding/transplanting dates. MODIS was classified using ISODATA to generate cropping calendar, cropping intensity, cropping pattern and rice ecosystem information. Season and location specific thresholds from field observations were used to generate detailed maps of rice areas and flooding/transplanting dates from the SAR data. Error matrices were used for the accuracy assessment of the MODIS-derived rice characteristics map and the SAR-derived detailed rice area map, while Root Mean Square Error (RMSE) and linear correlation were used to assess the TSX-derived flooding/transplanting dates. Results showed that multitemporal high spatial resolution SAR data is effective for mapping rice areas and flooding/transplanting dates with an overall accuracy of 90% and a kappa of 0.72 and that hypertemporal moderate-resolution optical imagery is effective for the basic characterization of rice areas with an overall accuracy that ranged from 62% to 87% and a kappa of 0.52 to 0.72. This study has also provided the first assessment of the temporal variation in the backscatter of rice from CSK and TSX using large incidence angles covering all rice crop stages from pre-season until harvest. This complementarity in optical and SAR data can be further exploited in the near future with the increased availability of space-borne optical and SAR sensors. This new information can help improve the identification of rice areas.

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“…The former method need to divide and select the typical vegetation area, and obtain the measurement statistics, in typical regions of vegetation cover by ground measurement method (and Bie, et al, 2013).Then, the relationship between the vegetation coverage of other regions and typical regions was established with the reference of the typical region to estimated the vegetation coverage in the study area. This method does not give full play to the characteristics of remote sensing technology to obtain a large amount of information, the research cycle is longer.…”

Section: Vegetation Coverage Factor Extractionmentioning

confidence: 99%

Soil Erosion Dynamic Detection Based on Remote Sense and GIS Techniques

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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Considering the shortcomings of heavy workload, time-consuming and laborious in common soil erosion observation and experimental methods, Jixian district in Tianjin was selected as the research area in this paper. The multi-factor comprehensive method in the second national soil erosion remote sensing survey is adopted. This paper uses the TM data of 2004 and Landsat-8 data and DEM data in 2015 of this region, according to the Soil Erosion Classification and Grading Standard (SL 190-2007) issued by the Ministry of Water Resources. The soil erosion intensity grading map of two periods in Jixian was obtained and the annual average erosion modulus was calculated. Based on the TM images in 2004 and Landsat-8 data in 2015, DEM data were employed in the study. The results showed that the soil erosion in Jixian district was mildly eroded in the recent ten years, although the average annual erosion modulus has declined, it is still necessary to pay attention to the areas above the strength erosion.

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Mapping land cover gradients through analysis of hyper-temporal NDVI imagery

Cited by 30 publications

References 63 publications

Temporal relationship between climate variability, Prosopis juliflora invasion and livestock numbers in the drylands of Magadi, Kenya

Temporal relationship between climate variability, Prosopis juliflora invasion and livestock numbers in the drylands of Magadi, Kenya

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

Soil Erosion Dynamic Detection Based on Remote Sense and GIS Techniques

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