Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain

Olsoy, Peter J.; Mitchell, Jessica J.; Glenn, Nancy F.; Flores, Alejandro N.

doi:10.3390/rs9100981

Cited by 12 publications

(5 citation statements)

References 59 publications

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“…Rice and other crops with similar spectral data are often misclassified. Vegetation phenology has advantages in paddy-rice classification, so paddy-rice classification using spectral data with and without using the phenological features [98][99][100][101] was performed. When only spectral features were considered, paddy rice was misclassified as grass or other vegetation types due to similar spectral characteristics.…”

Section: Discussionmentioning

confidence: 99%

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

et al. 2018

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Rice is one of the world’s major staple foods, especially in China. Highly accurate monitoring on rice-producing land is, therefore, crucial for assessing food supplies and productivity. Recently, the deep-learning convolutional neural network (CNN) has achieved considerable success in remote-sensing data analysis. A CNN-based paddy-rice mapping method using the multitemporal Landsat 8, phenology data, and land-surface temperature (LST) was developed during this study. First, the spatial–temporal adaptive reflectance fusion model (STARFM) was used to blend the moderate-resolution imaging spectroradiometer (MODIS) and Landsat data for obtaining multitemporal Landsat-like data. Subsequently, the threshold method is applied to derive the phenological variables from the Landsat-like (Normalized difference vegetation index) NDVI time series. Then, a generalized single-channel algorithm was employed to derive LST from the Landsat 8. Finally, multitemporal Landsat 8 spectral images, combined with phenology and LST data, were employed to extract paddy-rice information using a patch-based deep-learning CNN algorithm. The results show that the proposed method achieved an overall accuracy of 97.06% and a Kappa coefficient of 0.91, which are 6.43% and 0.07 higher than that of the support vector machine method, and 7.68% and 0.09 higher than that of the random forest method, respectively. Moreover, the Landsat-derived rice area is strongly correlated (R2 = 0.9945) with government statistical data, demonstrating that the proposed method has potential in large-scale paddy-rice mapping using moderate spatial resolution images.

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Section: Discussionmentioning

confidence: 99%

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

et al. 2018

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“…We contend that these and other improvements in hydrologic modeling were made possible by integrating the cumulative knowledge and institutional memory that comprise the core benefit of long‐term catchment research sites. Additionally, the wealth of data in the DCEW benefits research to evaluate remote sensing products (Gould, Glenn, Sankey, McNamara, & Spaete, ; Homan et al, ; Olsoy, Mitchell, Glenn, & Flores, ; Walters et al, ), and instrumentation development (Chandler, Seyfried, Murdock, & McNamara, ; Nayak, Chandler, Marks, McNamara, & Seyfried, ; Tyler et al, ).…”

Section: Fieldwork Leads To Improved Modelsmentioning

confidence: 99%

Form and function relationships revealed by long‐term research in a semiarid mountain catchment

et al. 2017

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Fifteen years of cumulative research in the Dry Creek Experimental Watershed in southwest Idaho, USA, has revealed relationships between catchment form and function that would not have been possible through independent short‐term projects alone. The impacts of aspect and elevation on incident energy and water, coupled with climate seasonality, have produced tightly connected landform properties and hydrologic processes. North‐facing hillslopes have steeper slope angles, thicker soil mantles, finer soil texture, and higher water holding capacities than their south‐facing counterparts. This trend is modulated by elevation and vegetation; higher elevation sites, where aspect differences in vegetation are less evident, exhibit less distinct hydrologic properties. The storage of water first as snow, then as soil moisture determines how upland ecosystems survive the seasonal and persistent water stress that happens each year, and sustains streamflow throughout the year. The cumulative body of local knowledge has improved general understanding of catchment science, serves as a resource for conceptual and numerical evaluation of process‐based models, and for data‐driven hydrologic education. WIREs Water 2018, 5:e1267. doi: 10.1002/wat2.1267 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water and Environmental Change

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“…To overcome data availability issues, Landsat data have been fused with information collected by satellites with higher temporal resolution (e.g., MODIS) to create high-temporal, cloud-free time series [15][16][17]. Likewise, Landsat images from multiple years (and sensors) can be merged into a single time series, but such methods need to consider inter-annual differences in phenology, land surface changes, and different sensor characteristics [14,18].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Landsat-8 and Sentinel-2 Data to Support Monitoring of Dryland Ecosystems

Pastick

Wylie

2018

Remote Sensing

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Drylands are the habitat and source of livelihood for about two fifths of the world's population and are highly susceptible to climate and anthropogenic change. To understand the vulnerability of drylands to changing environmental conditions, land managers need to effectively monitor rates of past change and remote sensing offers a cost-effective means to assess and manage these vast landscapes. Here, we present a novel approach to accurately monitor land-surface phenology in drylands of the Western United States using a regression tree modeling framework that combined information collected by the Operational Land Imager (OLI) onboard Landsat 8 and the Multispectral Instrument (MSI) onboard Sentinel-2. This highly-automatable approach allowed us to precisely characterize seasonal variations in spectral vegetation indices with substantial agreement between observed and predicted values (R 2 = 0.98; Mean Absolute Error = 0.01). Derived phenology curves agreed with independent eMODIS phenological signatures of major land cover types (average r-value = 0.86), cheatgrass cover (average r-value = 0.96), and growing season proxies for vegetation productivity (R 2 = 0.88), although a systematic bias towards earlier maturity and senescence indicates enhanced monitoring capabilities associated with the use of harmonized Landsat-8 Sentinel-2 data. Overall, our results demonstrate that observations made by the MSI and OLI can be used in conjunction to accurately characterize land-surface phenology and exclusion of imagery from either sensor drastically reduces our ability to monitor dryland environments. Given the declines in MODIS performance and forthcoming decommission with no equivalent replacement planned, data fusion approaches that integrate observations from multispectral sensors will be needed to effectively monitor dryland ecosystems. While the synthetic image stacks are expected to be locally useful, the technical approach can serve a wide variety of applications such as invasive species and drought monitoring, habitat mapping, production of phenology metrics, and land-cover change modeling.

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Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain

Cited by 12 publications

References 59 publications

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

Form and function relationships revealed by long‐term research in a semiarid mountain catchment

Spatiotemporal Analysis of Landsat-8 and Sentinel-2 Data to Support Monitoring of Dryland Ecosystems

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