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Zhang Y, Sun J, Qiu R, Liu H, Zhang X, Xuan J. Spatial Scale Effect of a Typical Polarized Remote Sensor on Detecting Ground Objects. SENSORS (BASEL, SWITZERLAND) 2021; 21:4418. [PMID: 34203266 PMCID: PMC8271648 DOI: 10.3390/s21134418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/17/2021] [Accepted: 06/22/2021] [Indexed: 12/05/2022]
Abstract
For polarized remote sensors, the polarization images of ground objects acquired at different spatial scales will be different due to the spatial heterogeneity of the ground object targets and the limitation of imaging resolution. In this paper, the quantitative inversion problem of a typical polarized remote sensor at different spatial scales was studied. Firstly, the surface roughness of coatings was inversed based on the polarized bidirectional reflectance distribution function (pBRDF) model according to their polarization images at different distances. A linear-mixed pixel model was used to make a preliminary correction of the spatial scale effect. Secondly, the super-resolution image reconstruction of the polarization imager was realized based on the projection onto convex sets (POCS) method. Then, images with different resolutions at a fixed distance were obtained by utilizing this super-resolution image reconstruction method and the optimal spatial scale under the scene can be acquired by using information entropy as an evaluation indicator. Finally, the experimental results showed that the roughness inversion of coatings has the highest accuracy in the optimal spatial scale. It has been proved that our proposed method can provide a reliable way to reduce the spatial effect of the polarized remote sensor and to improve the inversion accuracy.
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Affiliation(s)
| | | | - Rudong Qiu
- School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China; (Y.Z.); (J.S.); (H.L.); (X.Z.); (J.X.)
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Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices. REMOTE SENSING 2020. [DOI: 10.3390/rs12182970] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests.
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Levitan N, Kang Y, Özdoğan M, Magliulo V, Castillo P, Moshary F, Gross B. Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models. REMOTE SENSING 2019; 11:1928. [PMID: 31534785 PMCID: PMC6750221 DOI: 10.3390/rs11161928] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.
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Affiliation(s)
- Nathaniel Levitan
- Department of Electrical Engineering, City College of New York, 160 Convent Ave., New York, NY 10031, USA
- Correspondence:
| | - Yanghui Kang
- Department of Geography, University of Wisconsin-Madison, 550 N. Park St., Madison, WI 53706, USA
- Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, USA
| | - Mutlu Özdoğan
- Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, USA
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706, USA
| | - Vincenzo Magliulo
- CNR-Institute of Mediterranean Forest and Agricultural Systems, 85 Via Patacca, 80040-Ercolano (Napoli), Italy
| | - Paulo Castillo
- Department of Electrical and Computer Engineering Technology, Farmingdale State College, 2350 Broadhollow Road, Farmingdale, NY 11735-1021, USA
| | - Fred Moshary
- Department of Electrical Engineering, City College of New York, 160 Convent Ave., New York, NY 10031, USA
| | - Barry Gross
- Department of Electrical Engineering, City College of New York, 160 Convent Ave., New York, NY 10031, USA
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Evaluation of Three Techniques for Correcting the Spatial Scaling Bias of Leaf Area Index. REMOTE SENSING 2018. [DOI: 10.3390/rs10020221] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet Plateau based on validated MODIS NDVI. Sci Rep 2017. [PMID: 28646198 PMCID: PMC5482894 DOI: 10.1038/s41598-017-04038-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Spatiotemporal dynamics of aboveground biomass (AGB) is a fundamental problem for grassland environmental management on the Qinghai-Tibet Plateau (QTP). Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data can feasibly be used to estimate AGB at large scales, and their precise validation is necessary to utilize them effectively. In our study, the clip-harvest method was used at 64 plots in QTP grasslands to obtain actual AGB values, and a handheld hyperspectral spectrometer was used to calculate field-measured NDVI to validate MODIS NDVI. Based on the models between NDVI and AGB, AGB dynamics trends during 2000–2012 were analyzed. The results showed that the AGB in QTP grasslands increased during the study period, with 70% of the grasslands undergoing increases mainly in the Qinghai Province. Also, the meadow showed a larger increasing trend than steppe. Future AGB dynamic trends were also investigated using a combined analysis of the slope values and the Hurst exponent. The results showed high sustainability of AGB dynamics trends after the study period. Predictions indicate 60% of the steppe and meadow grasslands would continue to increase in AGB, while 25% of the grasslands would remain in degradation, with most of them distributing in Tibet.
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Spatial Up-Scaling Correction for Leaf Area Index Based on the Fractal Theory. REMOTE SENSING 2016. [DOI: 10.3390/rs8030197] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface. REMOTE SENSING 2015. [DOI: 10.3390/rs71012887] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Assessment of the MODIS LAI Product Using Ground Measurement Data and HJ-1A/1B Imagery in the Meadow Steppe of Hulunber, China. REMOTE SENSING 2014. [DOI: 10.3390/rs6076242] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Zheng G, Moskal LM. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. SENSORS 2009; 9:2719-45. [PMID: 22574042 PMCID: PMC3348792 DOI: 10.3390/s90402719] [Citation(s) in RCA: 108] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2008] [Revised: 04/03/2009] [Accepted: 04/17/2009] [Indexed: 12/04/2022]
Abstract
The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels.
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Affiliation(s)
- Guang Zheng
- Remote Sensing and Geospatial Analysis Laboratory and Precision Forestry Cooperative, College of Forest Resources, University of Washington, Box 352100, Seattle, Washington, USA 98195-2100; E-Mail: (G.Z.)
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Scale issues in remote sensing: a review on analysis, processing and modeling. SENSORS 2009; 9:1768-93. [PMID: 22573986 PMCID: PMC3345842 DOI: 10.3390/s90301768] [Citation(s) in RCA: 212] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2008] [Revised: 03/07/2009] [Accepted: 03/12/2009] [Indexed: 12/04/2022]
Abstract
With the development of quantitative remote sensing, scale issues have attracted more and more the attention of scientists. Research is now suffering from a severe scale discrepancy between data sources and the models used. Consequently, both data interpretation and model application become difficult due to these scale issues. Therefore, effectively scaling remotely sensed information at different scales has already become one of the most important research focuses of remote sensing. The aim of this paper is to demonstrate scale issues from the points of view of analysis, processing and modeling and to provide technical assistance when facing scale issues in remote sensing. The definition of scale and relevant terminologies are given in the first part of this paper. Then, the main causes of scale effects and the scaling effects on measurements, retrieval models and products are reviewed and discussed. Ways to describe the scale threshold and scale domain are briefly discussed. Finally, the general scaling methods, in particular up-scaling methods, are compared and summarized in detail.
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Chen JM, Thomas SC, Yin Y, Maclaren V, Liu J, Pan J, Liu G, Tian Q, Zhu Q, Pan JJ, Shi X, Xue J, Kang E. Enhancing forest carbon sequestration in China: toward an integration of scientific and socio-economic perspectives. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2007; 85:515-23. [PMID: 17182169 DOI: 10.1016/j.jenvman.2006.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2006] [Revised: 04/23/2006] [Accepted: 08/09/2006] [Indexed: 05/13/2023]
Abstract
This article serves as an introduction to this special issue, "China's Forest Carbon Sequestration", representing major results of a project sponsored by the Canadian International Development Agency and the Chinese Academy of Sciences. China occupies a pivotal position globally as a principle emitter of carbon dioxide, as host to some of the world's largest reforestation efforts, and as a key player in international negotiations aimed at reducing global greenhouse gas emission. The goals of this project are to develop remote sensing approaches for quantifying forest carbon balance in China in a transparent manner, and information and tools to support land-use decisions for enhanced carbon sequestration (CS) that are science based and economically and socially viable. The project consists of three components: (i) remote sensing and carbon modeling, (ii) forest and soil assessment, and (iii) integrated assessment of the socio-economic implications of CS via forest management. Articles included in this special issue are highlights of the results of each of these components.
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Affiliation(s)
- J M Chen
- Department of Geography, University of Toronto, 100 St. George Street, Room 5047, Toronto, ON, Canada M5S 3G3.
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