Extracting the Spatiotemporal Pattern of Cropping Systems From NDVI Time Series Using a Combination of the Spline and HANTS Algorithms: A Case Study for Shandong Province

Liang, Shouzhen; Ma, Wei; Sui, Xueyan; Yao, Hanhan; Li, Hongzhong; Liu, Tao; Hou, Xiudan; Wang, Meng

doi:10.1080/07038992.2017.1252906

Cited by 21 publications

(8 citation statements)

References 50 publications

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“…It is necessary to extract the area of interest from the images and this extraction process is known as subsetting (Liang et al 2017;Sanyal and Lu 2004). Here we are clipping the boundary area of the layer stack as shown in Fig.…”

Section: Subsettingmentioning

confidence: 99%

RETRACTED ARTICLE: Spatial and temporal analysis of flood hazard assessment of Cuddalore District, Tamil Nadu, India. Using geospatial techniques

Felix

Sasipraba

2020

J Ambient Intell Human Comput

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Flood is a natural calamity and causes damage of life and property devastation. The main objective of this study was to analyse flood hazard and inundation area mapping of Paravanar sub-Basin. Flood generating factors, like elevation, slope, drainage density, rainfall, soil type and land use were analyzed and delineated flood zones using Geospatial techniques and multi criteria evaluation. The flood generating factor was computed by data integration, floodplain depiction, mapping, and evaluation. Various parameters such as Normalized difference vegetation index (NDVI), Normalized difference build-up index (NDBI), Normalized difference water index (NDWI) were acquired for performing Regression Analysis. This information can be further utilized for hydro-meteorological figures and Surge forecast. These data can be used in building hazard and risk maps for pre-flood prediction. The flood hazard threats in the southeast and in between southeast and west part of the Paravanar River basin are high flood hazard threat zone.

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Section: Subsettingmentioning

confidence: 99%

RETRACTED ARTICLE: Spatial and temporal analysis of flood hazard assessment of Cuddalore District, Tamil Nadu, India. Using geospatial techniques

Felix

Sasipraba

2020

J Ambient Intell Human Comput

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“…Gao et al [17] produced the Landsat-MODIS time series dataset, which fused two types of satellite data and mapped crop growth. Zhou et al [18] compared the remote sensing time series reconstruction models at different time intervals, while Liang et al [19] compared the reconstructed time series data via linear interpolation and with a smoothing algorithm, which improved the quality of the NDVI time series dataset. Previous studies have shown that both the Harmonic Analysis of Time Series (HANTS) and Savitzky–Golay methods are powerful tools to reproduce NDVI time series data [20, 21].…”

Section: Introductionmentioning

confidence: 99%

Area extraction and spatiotemporal characteristics of winter wheat–summer maize in Shandong Province using NDVI time series

et al. 2019

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The use of remote sensing to rapidly and accurately obtain information on the spatiotemporal distribution of large-scale wheat and maize acreage is of great significance for improving the level of food production management and ensuring food security. We constructed a MODIS-NDVI time series dataset, combined linear interpolation and the Harmonic Analysis of Time Series algorithm to smooth the time series data curve, and classified the data with random forest algorithms. The results show that winter wheat–summer maize planting areas were mainly distributed in the western plains, southern region, and north-eastern part of the middle mountainous regions while the eastern hilly regions were less distributed and scattered. The winter wheat–summer maize planting areas in the study area continued to grow from 2004–2016, with the most significant growth in the northern part of the western plains and Yellow River Delta. The spatial planting probability reflected the planting core area and showed an intensive planting pattern. During the study period, the peak value and time for the NDVI of the winter wheat were significantly different and showed an increasing trend, while these parameters for the summer maize were relatively stable with little change. Therefore, we mapped a spatial distribution of the winter wheat and summer maize, using the time series data pre-processing synthesis and phenology curve random forest classification methods. Through precision analysis, we obtained satisfactory results, which provided a straightforward and efficient method to monitor the winter wheat and summer maize.

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“…The scarce vegetation with diverse geology could result error in LULC classification; hence, ground truth points were used for the post-classification refinement of the misclassified pixels. NDVI-based LULC classification is widely used for spatiotemporal differentiation of vegetation cover from other classes [34][35][36][37][47][48][49][50]. The calculated values of NDVI range from ( −)1 (no vegetation) to ( +)1 (vegetation) [51].…”

Section: Methodsmentioning

confidence: 99%

Characterizing Khetri copper mine environment using geospatial tools

2021

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Mines result in land use and land cover (LULC) change due to degradation of natural resources and establishment of new infrastructure for ore extraction and beneficiation. The present study was carried out to, with objectives, (1) characterize LULC change (from 1975 to 2017) in Khetri copper mine region, (2) spatial distribution of pollution indices and (3) spectral response of elemental concentration of soil and groundwater using Landstat and ASTER satellite data. The study was designed to fulfil the objectives and for the same NDVI values were calculated for LULC classification and generated maps were analyzed for landscape pattern. Spatial distribution of pollution indices calculated using geochemical data of soil and groundwater was plotted to understand the impact of contamination on landscape pattern. The correlation of spectral response of Landstat bands with heavy metals concentration was plotted to assess their possible use in quantification of heavy metals. Results show constant increase in settlements, mines and open area while vegetation cover has decreased. Landscape and class level metrics (number of patch, patch density, aggregation index and landscape shape index) indicate increase in the fragmentation of landscape in recent years. Shannon’s Evenness Index indicates increase in uniformity in landscape and it is attributed to loss of vegetation and agriculture patches. Pollution indices, Pollution Load Index for soil is high near the overburden materials and Index of Environmental Risk (IER) and Contamination Index for ground water is high near abandoned mines. Spectral bands 5 and 6 (SWIR 1) show significant negative correlation, and 9 (Cirrus) shows significant positive correlation with metal concentration in soil and water suggesting the possible use of remote sensing in assessment of metal concentration at ground level. Thus, it can be concluded that mines significantly influence the landscape pattern and remote sensing could be used for the assessment and predication of heavy metal contamination at broader scale in a cost-effective way.

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Extracting the Spatiotemporal Pattern of Cropping Systems From NDVI Time Series Using a Combination of the Spline and HANTS Algorithms: A Case Study for Shandong Province

Cited by 21 publications

References 50 publications

RETRACTED ARTICLE: Spatial and temporal analysis of flood hazard assessment of Cuddalore District, Tamil Nadu, India. Using geospatial techniques

RETRACTED ARTICLE: Spatial and temporal analysis of flood hazard assessment of Cuddalore District, Tamil Nadu, India. Using geospatial techniques

Area extraction and spatiotemporal characteristics of winter wheat–summer maize in Shandong Province using NDVI time series

Characterizing Khetri copper mine environment using geospatial tools

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