Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK

Palchowdhuri, Y.; Valcarce-Diñeiro, Rubén; King, Peter; Sanabria-Soto, M.

doi:10.1017/s0021859617000879

Cited by 49 publications

(40 citation statements)

References 55 publications

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“…Specifically, the Normalized Difference Vegetation Index (NDVI) 12 , Soil-Adjusted Vegetation Index (SAVI) 13 and Enhanced Vegetation Index (EVI) 14 have been used for monitoring vegetation systems or ecological responses to environmental change 15 . MSI data have been used for identifying crop types [16][17][18] , plastic-covered greenhouses 19 , water bodies 20 and some previous studies showed the potential of VIs calculated from MSI data. However, it is possible to calculate a vast number of VIs from MSI data and most of them have been ignored in the previous studies.…”

Section: Introductionmentioning

confidence: 99%

Crop classification from Sentinel-2-derived vegetation indices using ensemble learning

Sonobe

Yamaya

Tani

et al. 2018

J. Appl. Rem. Sens.

141

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The identification and mapping of crops are important for estimating potential harvest as well as for agricultural field management. Optical remote sensing is one of the most attractive options because it offers vegetation indices and some data have been distributed free of charge. Especially, Sentinel-2A, which is equipped with a multispectral sensor (MSI) with blue, green, red and near-infrared-1 bands at 10 m; red edge 1 to 3, nearinfrared-2 and shortwave infrared 1 and 2 at 20 m; and 3 atmospheric bands (Band 1, Band 9 and Band 10) at 60 m, offers some vegetation indices calculated to assess vegetation status. However, sufficient consideration has not been given to the potential of vegetation indices calculated from MSI data. Thus, 82 published indices were calculated and their importance were evaluated for classifying crop types. In this study, the two most common classification algorithms, random forests (RF) and support vector machine (SVM), were applied to conduct cropland classification from MSI data. Additionally, super learning was applied for more improvement, achieving overall accuracies of 90.2-92.2%. Of the two algorithms applied (RF and SVM), the accuracy of SVM was superior and 89.3-92.0% of overall accuracies were confirmed. Furthermore, stacking contributed to higher overall accuracies (90.2-92.2%) and significant differences were confirmed with the results of SVM and RF. Our results showed that vegetation indices had the greatest contributions in identifying specific crop types.

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Section: Introductionmentioning

confidence: 99%

Crop classification from Sentinel-2-derived vegetation indices using ensemble learning

Sonobe

Yamaya

Tani

et al. 2018

J. Appl. Rem. Sens.

141

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“…Accurate land cover assessments form the basis for such analyses in agricultural areas [14], and are particularly important for planning of water resources [15], automated short-term monitoring for yield estimation [16], sustainable land management [17], crop modeling before the end of season [18], or plot extraction for high-throughput phenotyping [19][20][21]. Further, current conditions and extent of land cover are needed as a basis for climate change modeling [22].…”

Section: Introductionmentioning

confidence: 99%

“…Multispectral satellite data, e.g., stemming from RapidEye [35], SPOT 5 [36], QuickBird MS [16], IKONOS [16,37], WorldView-2 [38], or WorldView-3 [14], have been widely used to assess land cover in agricultural areas. These datasets have typical spatial resolutions between 0.5 m-6.5 m, and varying spectral bands (in terms of number, center wavelength and band width) mainly in the visible (VIS) and near-infrared (NIR) spectral range.…”

Section: Introductionmentioning

confidence: 99%

Crop Classification in a Heterogeneous Arable Landscape Using Uncalibrated UAV Data

2018

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Land cover maps are indispensable for decision making, monitoring, and management in agricultural areas, but they are often only available after harvesting. To obtain a timely crop map of a small-scale arable landscape in the Swiss Plateau, we acquired uncalibrated, very high-resolution data, with a spatial resolution of 0.05 m and four spectral bands, using a consumer-grade camera on an unmanned aerial vehicle (UAV) in June 2015. We resampled the data to different spatial and spectral resolutions, and evaluated the method using textural features (first order statistics and mathematical morphology), a random forest classifier for best performance, as well as number and size of the structuring elements. Our main findings suggest the overall best performing data consisting of a spatial resolution of 0.5 m, three spectral bands (RGB—red, green, and blue), and five different sizes of the structuring elements. The overall accuracy (OA) for the full set of crop classes based on a pixel-based classification is 66.7%. In case of a merged set of crops, the OA increases by ~7% (74.0%). For an object-based classification based on individual field parcels, the OA increases by ~20% (OA of 86.3% for the full set of crop classes, and 94.6% for the merged set, respectively). We conclude the use of UAV to be most relevant at 0.5 m spatial resolution in heterogeneous arable landscapes when used for crop classification.

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“…The national grassland frequency map addresses these limitations and improves knowledge about semi-natural grasslands in two ways. First, its 250 m resolution identifies semi-natural grasslands more accurately than maps produced from LPIS or CORINE Land Cover at a spatial resolution of 5 km [22] or from the EVA database at a spatial resolution of 10 km [19]; second, it uses long-term monitoring to discriminate between semi-natural and temporary grasslands, which is something that LULC maps derived from annual time-series analysis of remote sensing data do not do [23,25,30].…”

Section: Can a Decadal Modis 250 M Time-series Identify Semi-natural mentioning

confidence: 99%

“…Beyond these broad-scale maps based on the CORINE Land Cover layer, many studies based on automatic and fine-scale analyses have demonstrated the contribution of multi-temporal and high-spatial-resolution satellite data in discriminating grasslands from other LULC types [16,[23][24][25], characterizing forage quality [20], identifying agricultural practices [26] and mapping floristic variation in semi-natural grasslands [27][28][29]. However, discriminating semi-natural and temporary grasslands accurately remains a concern [23,30] due to the lack of temporal depth in remote sensing time-series and because a one-year observation is insufficient to discriminate between semi-natural and temporary grasslands [31]. For instance, the French national land use map as well as the European high-resolution layer (HRL) for "grassland", both derived from multi-temporal Sentinel and Landsat data, combine semi-natural and temporary grasslands into a single "grassland" class.…”

Section: Introductionmentioning

confidence: 99%

Mapping Grassland Frequency Using Decadal MODIS 250 m Time-Series: Towards a National Inventory of Semi-Natural Grasslands

et al. 2019

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Semi-natural grasslands are perennial ecosystems and an important part of agricultural landscapes that are threatened by urbanization and agricultural intensification. However, implementing national grassland conservation policies remains challenging because their inventory, based on short-term observation, rarely discriminate semi-natural permanent from temporary grasslands. This study aims to map grassland frequency at a national scale over a long period using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m satellite time-series. A three-step method was applied to the entire area of metropolitan France (543,940 km²). First, land-use and land-cover maps—including grasslands—were produced for each year from 2006–2017 using the random forest classification of MOD13Q1 and MYD13Q1 products, which were calibrated and validated using field observations. Second, grassland frequency from 2006–2017 was calculated by combining the 12 annual maps. Third, sub-pixel analysis was performed using a reference layer with 20 m spatial resolution to quantify percentages of land-use and land-cover classes within MODIS pixels classified as grassland. Results indicate that grasslands were accurately modeled from 2006–2017 (F1-score 0.89–0.93). Nonetheless, modeling accuracy varied among biogeographical regions, with F1-score values that were very high for Continental (0.94 ± 0.01) and Atlantic (0.90 ± 0.02) regions, high for Alpine regions (0.86 ± 0.04) but moderate for Mediterranean regions (0.62 ± 0.10). The grassland frequency map for 2006–2017 at 250 m spatial resolution provides an unprecedented view of stable grassland patterns in agricultural areas compared to existing national and European GIS layers. Sub-pixel analysis showed that areas modeled as grasslands corresponded to grassland-dominant areas (60%–94%). This unique long-term and national monitoring of grasslands generates new opportunities for semi-natural grassland inventorying and agro-ecological management.

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Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK

Cited by 49 publications

References 55 publications

Crop classification from Sentinel-2-derived vegetation indices using ensemble learning

Crop classification from Sentinel-2-derived vegetation indices using ensemble learning

Crop Classification in a Heterogeneous Arable Landscape Using Uncalibrated UAV Data

Mapping Grassland Frequency Using Decadal MODIS 250 m Time-Series: Towards a National Inventory of Semi-Natural Grasslands

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