Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of Landsat 8 OLI imagery

Ding, Yanling; Zhao, Kai; Zheng, Xingming; Jiang, Tao

doi:10.1016/j.jag.2014.01.009

Cited by 63 publications

(43 citation statements)

References 24 publications

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“…A slight overestimation in the averaged magnitude of 0.4 was found for the MODIS LAI product compared with the HJ-retrieved LAI maps, and the relative absolute errors of the product ranged from 10% to 50%. Ding et al [149] introduced a concept of mean length variability to be used as an index to quantify spatial heterogeneity over croplands for multi-temporal NDVI, near-infrared, and red reflectance. This investigation suggested that the spatial heterogeneity varied with the changes in the fraction of vegetation cover, and a spatial resolution larger than 120 m could effectively limit the difference of spatial heterogeneity among different remote sensing observation methods.…”

Section: Other Validation Experimentsmentioning

confidence: 99%

Validation of Regional-Scale Remote Sensing Products in China: From Site to Network

Wang

et al. 2016

Remote Sensing

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Abstract:Validation is mandatory to quantify the reliability of remote sensing products (RSPs). However, this process is not straightforward and usually presents formidable challenges in terms of both theory and real-world operations. In this context, a dedicated validation initiative was launched in China, and we identified a validation strategy (VS). This overall VS focuses on validating regional-scale RSPs with a systematic site-to-network concept, consisting of four main components: (1) general guidelines and technical specifications to guide users in validating various land RSPs, particularly aiming to further develop in situ sampling schemes and scaling approaches to acquire ground truth at the pixel scale over heterogeneous surfaces; (2) sound site-based validation activities, conducted through multi-scale, multi-platform, and multi-source observations to experimentally examine and improve the first component; (3) a national validation network to allow for comprehensive assessment of RSPs from site or regional scales to the national scale across various zones; and (4) an operational RSP evaluation system to implement operational validation applications. Research progress on the development of these four components is described in this paper. Some representative research results, with respect to the development of sampling methods and site-based validation activities, are also highlighted. The development of this VS improves our understanding of validation issues, especially to facilitate validating RSPs over heterogeneous land surfaces both at the pixel scale level and the product level.

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Section: Other Validation Experimentsmentioning

confidence: 99%

Validation of Regional-Scale Remote Sensing Products in China: From Site to Network

Wang

et al. 2016

Remote Sensing

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“…TIRS collects data in two long wavelength thermal infrared bands of 100 m spatial resolution. TIRS data is registered to the OLI data to create radiometrically and geometrically calibrated, terrain-corrected Level 1 data products, raising their radiometric resolution to 16-bit [11]- [13].…”

Section: Landsat 8 Imagerymentioning

confidence: 99%

Processing of Landsat 8 Imagery and Ground Gamma-Ray Spectrometry for Geologic Mapping and Dose-Rate Assessment, Wadi Diit along the Red Sea Coast, Egypt

Gawad¹,

Donia²,

Elsaid³

2016

OJG

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Maximum Likelihood (MLH) supervised classification of atmospherically corrected Landsat 8 imagery was applied successfully for delineating main geologic units with a good accuracy (about 90%) according to reliable ground truth areas, which reflected the ability of remote sensing data in mapping poorly-accessed and remote regions such as playa (Sabkha) environs, subdued topography and sand dunes. Ground gamma-ray spectrometric survey was to delineate radioactive anomalies within Quaternary sediments at Wadi Diit. The mean absorbed dose rate (D), annual effective dose equivalent (AEDE) and external hazard index (Hex) were found to be within the average worldwide ranges. Therefore, Wadi Diit environment is said to be radiological hazard safe except at the black-sand lens whose absorbed dose rate of 100.77 nGy/h exceeds the world average. So, the inhabitants will receive a relatively high radioactive dose generated mainly by monazite and zircon minerals from black-sand lens.

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“…To deal with the high spatio-spectro-temporal resolutions new satellite sensors are now offering, dimension reduction is usually performed through the use of a vegetation index such as NDVI [50,52,63,64], PCA [65] or spectro-temporal metrics [35,66]. However, a large amount of spectro-temporal information is lost with these solutions.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

et al. 2017

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This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the α-Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands' management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands' discrimination. Results are compared to other existing pixel-and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.

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Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of Landsat 8 OLI imagery

Cited by 63 publications

References 24 publications

Validation of Regional-Scale Remote Sensing Products in China: From Site to Network

Validation of Regional-Scale Remote Sensing Products in China: From Site to Network

Processing of Landsat 8 Imagery and Ground Gamma-Ray Spectrometry for Geologic Mapping and Dose-Rate Assessment, Wadi Diit along the Red Sea Coast, Egypt

Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

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