Energy crop mapping with enhanced TM/MODIS time series in the BCAP agricultural lands

Wang, Cuizhen; Fan, Qian; Li, Qingting; SooHoo, William M.; Lu, Linlin

doi:10.1016/j.isprsjprs.2016.12.002

Cited by 27 publications

(16 citation statements)

References 25 publications

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“…Its 8-km grid size, however, is too coarse to classify individual tree species in this study. More recently, data fusion techniques have been developed to build Landsat-like, daily image series by taking advantage of image pairs between coarse-spatial, but high-temporal resolution imagery (e.g., MODIS) and medium-spatial resolution Landsat data [26]. While inter-calibration of different sensors could be a concern in the fused NDVI time series, it could be leveraged by explicit trajectory smoothing.…”

Section: Tree Species Classificationmentioning

confidence: 99%

Assessing Re-Composition of Xing’an Larch in Boreal Forests after the 1987 Fire, Northeast China

Wang

Zang

2017

Remote Sensing

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Abstract:Xing'an larch, a deciduous coniferous species, is the zonal tree of the Greater Xing'an Mountains in Northeast China. In May 1987, a catastrophic fire broke out in the mountains and burned 1.3 million hectares of forests in 26 days. While studies have shown that forest greenness has come back to normal in certain years, the re-composition of this zonal species has not been studied after the 1987 fire. With a series of Landsat 8 OLI images acquired in 2013-2015, this study builds the Normalized Difference Vegetation Index (NDVI) and Green Vegetation Index (GVI) time series in a complete growing cycle. A decision tree is developed to classify tree species with an overall accuracy of 86.16% and Kappa coefficient of 0.80. The re-composition of Xing'an larch after the 1987 fire is extracted, and its variations in areas under different fire intensities are statistically analyzed. Results show that Xing'an larch comprises 17.52%, 26.20% and 33.19% of forests in burned areas with high, medium and low fire intensities, respectively. Even around 30 years after the 1987 fire, the composition of this zonal species in boreal forest has not been fully recovered in the Greater Xing'an Mountains. The Xing'an larch map extracted in this study could serve as base information for ecological and environmental studies in this south end of the boreal Eurasia.

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Section: Tree Species Classificationmentioning

confidence: 99%

Assessing Re-Composition of Xing’an Larch in Boreal Forests after the 1987 Fire, Northeast China

Wang

Zang

2017

Remote Sensing

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“…At present, many scholars have proposed various spatiotemporal fusion algorithms based on multi-source data [19][20][21], among which the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) proposed by Gao et al [22] has been most widely used. Currently, spatiotemporal fusion predictions have demonstrated their ability and potential for crop-type and other land cover classifications [23][24][25][26][27]. For example, Zhu et al, 2017 fused Landsat and MODIS images and used SVM for crop type classification.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al, 2015 fused Landsat TM and MODIS imagery using ESTARFM and used decision tree classification to map six crop types [28]. Wang et al, 2017 adopted similar strategy to map six energy crop types [24]. One recent study used several fused images on key dates to map paddy rice fields in Hunan, China by supervised random tree (RT) classifier [25].…”

Section: Introductionmentioning

confidence: 99%

“…Those approaches usually rely on image statistics and/or training sample collection and/or visual interpretation, which is time-consuming, labor-intensive and region-dependent. The classification models trained for one region may not be suitable for other regions, which may make the classification results not always satisfying [9,24,31]. On the other hand, some studies used several predicted images only instead of constructing time series data throughout the growing season [25,32].…”

Section: Introductionmentioning

confidence: 99%

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Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

et al. 2019

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Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields.

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“…For instance, Wardlow and Egbert [28] successfully estimated the extents of alfalfa, summer crops, winter wheat, and fallow in Kansas, one of the topographically flattest states in the United States. Wang et al [29] identified corn, soybean, and winter wheat in western Missouri, a state adjacent to Kansas. While the results are promising, the performance of phenology-based mapping has yet to be confirmed if multiple crop types (e.g., over 10) are the mapping subjects.…”

Section: Introductionmentioning

confidence: 99%

Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography

Wei

Tong²,

Chen

et al. 2019

Agriculture

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Sustainable agricultural practices necessitate accurate baseline data of crop types and their detailed spatial distribution. Compared with field surveys, remote sensing has demonstrated superior performance, offering spatially explicit crop distribution in a timely manner. Recent studies have taken advantage of remote sensing time series to capture the variation in plant phenology, inferring major crop types. However, such an approach was rarely used to extract detailed, multiple crop types spanning a large area, and the impact of topography has yet to be well analyzed in mountainous regions. This study aims to answer two questions in crop type extraction: (i) Is it feasible to accurately map multiple crop types over a large mountainous area with phenology-based modeling? (ii) What are the effects of topography in such modeling? To answer the questions, phenological metrics were extracted from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite time series, and the random forests classifier was used to map 12 crop types in South China (236,700 km2), featuring a subtropical monsoon climate and high topographic variation. Our study revealed promising results using MODIS EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) time series, although EVI outperformed NDVI (overall accuracy: 85% versus 81%). The spectral and temporal metrics of plant phenology significantly contributed to crop identification, where the spectral information exhibited greater importance. The increase of slope led to a decrease in model accuracy in general. However, uniformly distributed tree plantations (e.g., tea-oil camellia, gum, and tea trees) being cultivated on large slopes (>15 degrees) achieved accuracies greater than 80%.

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Energy crop mapping with enhanced TM/MODIS time series in the BCAP agricultural lands

Cited by 27 publications

References 25 publications

Assessing Re-Composition of Xing’an Larch in Boreal Forests after the 1987 Fire, Northeast China

Assessing Re-Composition of Xing’an Larch in Boreal Forests after the 1987 Fire, Northeast China

Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography

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