{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:20:05Z","timestamp":1760242805664,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2016,7,6]],"date-time":"2016-07-06T00:00:00Z","timestamp":1467763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Ministry of Science and Technology, P.R. China under the national \u201c863\u201d project \u201cThe study on the key technologies of digital forest resources monitoring","award":["2012AA102001"],"award-info":[{"award-number":["2012AA102001"]}]},{"name":"the National Natural Science Foundation of China \u201cThe study on the selection of band windows of forest remote sensing monitoring\u201d","award":["31370639"],"award-info":[{"award-number":["31370639"]}]},{"name":"China Postdoctoral Science Foundation funded project","award":["2014M562147"],"award-info":[{"award-number":["2014M562147"]}]},{"name":"Hunan Province Science and Technology Plan Project","award":["2015RS4048"],"award-info":[{"award-number":["2015RS4048"]}]},{"name":"the Central South University of Forestry and Technology","award":["0990"],"award-info":[{"award-number":["0990"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Combining sample plot and image data has been widely used to map forest carbon density at local, regional, national and global scales. When mapping is conducted using multiple spatial resolution images at different scales, field observations have to be collected at the corresponding resolutions to match image values in pixel sizes. Given a study area, however, to save time and cost, field observations are often collected from sample plots having a fixed size. This will lead to inconsistency of spatial resolutions between sample plots and image pixels and impede the mapping and product quality assessment. In this study, a methodological framework was proposed to conduct mapping and accuracy assessment of forest carbon density at four spatial resolutions by combining remotely sensed data and reference values of sample plots from a systematical, nested and clustering sampling design. This design led to one field observation dataset at a 30 m spatial resolution sample plot level and three other reference datasets by averaging the observations from three, five and seven sample plots within each of 250 m and 500 m sub-blocks and 1000 m blocks, respectively. The datasets matched the pixel values of a Landsat 8 image and three MODIS products. A sequential Gaussian co-simulation (SGCS) and a sequential Gaussian block co-simulation (SGBCS), an upscaling algorithm, were employed to map forest carbon density at the spatial resolutions. This methodology was tested for mapping forest carbon density in Huang-Feng-Qiao forest farm of You County in Eastern Hunan of China. The results showed that: First, all of the means of predicted forest carbon density values at four spatial resolutions fell in the confidence intervals of the reference data at a significance level of 0.05. Second, the systematical, nested and clustering sampling design provided the potential to obtain spatial information of forest carbon density at multiple spatial resolutions. Third, the relative root mean square error (RMSE) of predicted values at the plot level was much greater than those at the sub-block and block levels. Moreover, the accuracies of the up-scaled estimates were much higher than those from previous studies. In addition, at the same spatial resolution, SGCSWA (scaling up the SGCS and Landsat derived 30 m resolution map using a window average (WA)) resulted in smallest relative RMSEs of up-scaled predictions, followed by combinations of Landsat images and SGBCS. The accuracies from both methods were significantly greater than those from the combinations of MODIS images and SGCS. Overall, this study implied that the combinations of Landsat 8 images and SGCSWA or SGBCS with the systematical, nested and clustering sampling design provided the potential to formulate a methodological framework to map forest carbon density and conduct accuracy assessment at multiple spatial resolutions. However, this methodology needs to be further refined and examined in other forest landscapes.<\/jats:p>","DOI":"10.3390\/rs8070571","type":"journal-article","created":{"date-parts":[[2016,7,6]],"date-time":"2016-07-06T09:55:55Z","timestamp":1467798955000},"page":"571","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon Density by Combining Image and Plot Data from a Nested and Clustering Sampling Design"],"prefix":"10.3390","volume":"8","author":[{"given":"Enping","family":"Yan","sequence":"first","affiliation":[{"name":"Research Center of Forest Remote Sensing &amp; Information Engineering, Central South University of Forestry &amp; Technology, Changsha 410004, China"}]},{"given":"Hui","family":"Lin","sequence":"additional","affiliation":[{"name":"Research Center of Forest Remote Sensing &amp; Information Engineering, Central South University of Forestry &amp; Technology, Changsha 410004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5419-4547","authenticated-orcid":false,"given":"Guangxing","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Center of Forest Remote Sensing &amp; Information Engineering, Central South University of Forestry &amp; Technology, Changsha 410004, China"},{"name":"Department of Geography, Southern Illinois University, Carbondale, IL 62901, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5401-6783","authenticated-orcid":false,"given":"Hua","family":"Sun","sequence":"additional","affiliation":[{"name":"Research Center of Forest Remote Sensing &amp; Information Engineering, Central South University of Forestry &amp; Technology, Changsha 410004, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5559","DOI":"10.3390\/rs6065559","article-title":"A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico","volume":"6","author":"Cartus","year":"2014","journal-title":"Remote Sens."},{"key":"ref_2","unstructured":"Intergovernmental Panel on Climate Change (IPCC) (2007). Climate Change 2007: The Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the IPCC, Cambridge University Press."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1111\/j.1365-2486.2005.00955.x","article-title":"Aboveground forest biomass and the global carbon balance","volume":"11","author":"Houghton","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s11027-006-4583-5","article-title":"A simulation of temporal and spatial variations in carbon at landscape level: A case study for Lake Abitibi Model Forest in Ontario, Canada","volume":"12","author":"Zhou","year":"2007","journal-title":"Mitig. Adapt. Strateg. Glob. Chang."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.3390\/rs3071427","article-title":"Evaluating the remote sensing and inventory-based estimation of biomass in the Western Carpathians","volume":"3","author":"Moisen","year":"2011","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3693","DOI":"10.3390\/rs6053693","article-title":"Improving of above groud biomass using dual polarimetric PALSAR and ETM+ data in the Hyrcanian fore tainnuomts (Iran)","volume":"6","author":"Attarchi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_7","first-page":"160","article-title":"Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area","volume":"14","author":"Tian","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2009.12.018","article-title":"Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches","volume":"114","author":"Powell","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A Survey of Remote Sensing-Based Aboveground Biomass Estimation Methods","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1126\/science.1058629","article-title":"Changes in forest biomass carbon storage in China between 1949 and 1998","volume":"292","author":"Fang","year":"2002","journal-title":"Science"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2013.10.029","article-title":"Estimating landscape net ecosystem exchange at high spatial\u2013temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements","volume":"141","author":"Fu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/S0378-1127(97)00026-1","article-title":"A generalized model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance, and partitioning","volume":"95","author":"Landsberg","year":"1997","journal-title":"For. Ecol. Manag."},{"key":"ref_13","unstructured":"Ehleringer, J.R., and Field, C.B. (1993). Scaling Physiological Processes: Leaf to Globe, Academic Press, Inc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5452","DOI":"10.3390\/rs6065452","article-title":"Carbon stock assessment using remote sensing and forest inventory data in Savannakhet, Lao PDR","volume":"6","author":"Phutchard","year":"2014","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.3390\/rs5052257","article-title":"Retrieval of forest aboveground biomass and stem volume with airborne scanning LiDAR","volume":"5","author":"Kankare","year":"2013","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5452","DOI":"10.3390\/rs6065452","article-title":"Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data in Savannakhet, Lao PDR","volume":"6","author":"Vicharnakorn","year":"2014","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.3390\/rs5031001","article-title":"Impacts of spatial variability on aboveground biomass estimation from L-band Radar in a temperate forest","volume":"5","author":"Robinson","year":"2013","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1109\/LGRS.2015.2451091","article-title":"Improvement of forest carbon estimation by integration of regression modeling and spectral unmixing of Landsat data","volume":"12","author":"Yan","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s10342-014-0838-y","article-title":"Comparison of methods toward multi-scale forest carbon mapping and spatial uncertainty analysis: Combining national forest inventory plot data and Landsat TM images","volume":"134","author":"Fleming","year":"2015","journal-title":"Eur. J. For. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1016\/j.foreco.2009.06.056","article-title":"Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images","volume":"258","author":"Wang","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.rse.2007.04.002","article-title":"Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery","volume":"111","author":"McRoberts","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.ecolmodel.2006.12.040","article-title":"Semi-empirical models for assessing biological productivity of Northern Eurasian forests","volume":"204","author":"Shvidenko","year":"2007","journal-title":"Ecol. Model."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1111\/j.1744-7429.2010.00644.x","article-title":"Effects of Plot Size and Census Interval on Descriptors of Forest Structure and Dynamics","volume":"42","author":"Wagner","year":"2010","journal-title":"Biotropica"},{"key":"ref_25","first-page":"5711","article-title":"Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks","volume":"11","author":"Detto","year":"2014","journal-title":"Biogeosci. Discuss."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.ecoinf.2007.06.002","article-title":"Combining stratification and up-scaling method-block cokriging with remote sensing imagery for sampling and mapping an erosion cover factor","volume":"2","author":"Gertner","year":"2007","journal-title":"Ecol. Inf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1109\/TGRS.2004.831889","article-title":"Spatial variability based algorithms for scaling up spatial data and uncertainties","volume":"42","author":"Wang","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1080\/07038992.1999.10874734","article-title":"The scale issue in social and natural sciences","volume":"25","author":"Marceau","year":"1999","journal-title":"Can. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1080\/07038992.1999.10874735","article-title":"Remote sensing contributions to the scale issue","volume":"25","author":"Marceau","year":"1999","journal-title":"Can. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4961","DOI":"10.1080\/01431160410001680428","article-title":"Up-scaling methods based on variability-weighted and simulation for inferring spatial information cross scales","volume":"25","author":"Wang","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Weng, Q. (2014). Scale Issue in Remote Sensing, John Wiley and Sons.","DOI":"10.1002\/9781118801628"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.5194\/bg-9-2683-2012","article-title":"High-resolution mapping of forest carbon stocks in the Colombian Amazon","volume":"9","author":"Asner","year":"2012","journal-title":"Biogeosciences"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guitet, S., Herault, B., Molto, Q., Brunaux, O., and Couteron, P. (2015). Spatial structure of above-ground biomass limits accuracy of carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0138456"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1890\/13-1574.1","article-title":"Aboveground biomass mapping of African forest mosaics using canopy texture analysis: Toward a regional approach","volume":"24","author":"Bastin","year":"2014","journal-title":"Ecol. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, G., and Weng, Q. (2013). Remote Sensing of Natural Resources, CRC Press, Taylor & Francis Group.","DOI":"10.1201\/b15159"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.envsoft.2012.08.003","article-title":"Comparing simulations of three conceptually different forest models with National Forest Inventory data","volume":"40","author":"Huber","year":"2013","journal-title":"Environ. Model. Softw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.livsci.2015.10.020","article-title":"Cross-validation of genetic and genomic predictions of temperament in Nellore\u2013Angus crossbreds","volume":"182","author":"Hulsman","year":"2015","journal-title":"Livestock Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1016\/j.patcog.2015.03.009","article-title":"Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation","volume":"48","author":"Wong","year":"2015","journal-title":"Pattern Recogn."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.ecolmodel.2008.07.024","article-title":"Uncertainty analysis in carbon models of forest ecosystems: Research needs and development of a theoretical framework to estimate error propagation","volume":"219","author":"Larocque","year":"2008","journal-title":"Ecol. Model."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.rse.2015.01.009","article-title":"Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels","volume":"150","author":"Chen","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.foreco.2008.04.010","article-title":"Comparison of uncertainties in carbon sequestration estimates for a tropical and a temperate forest","volume":"256","author":"Nabuurs","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_42","unstructured":"Yan, E. (2011). Study on Extraction of Broad-leaved Forest Information Based on Medium and High Spatial Resolution Remote Sensing Image, Master Degree Thesis of Central South University of Forestry & Technology."},{"key":"ref_43","unstructured":"Li, H. (2011). Estimation and Evaluation of Forestry Biomass Carbon Storage in China, China Forestry Press."},{"key":"ref_44","first-page":"41","article-title":"Spatio temporal dynamics of forest carbon storage in Taihe County of Jiangxi Province in 1985\u20132030","volume":"22","author":"Wu","year":"2011","journal-title":"Chinese J. Appl. Ecol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"073486","DOI":"10.1117\/1.JRS.7.073486","article-title":"Algorithms for moderate resolution imaging spectroradiometer cloud-free image compositing","volume":"7","author":"Xiang","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_46","unstructured":"Song, X., and Li, J. (2007). Technologies for Sampling and Inventory, Chinese Forestry Press. [2rd ed.]."},{"key":"ref_47","unstructured":"Deutsch, C.V., and Journel, A.G. (1998). Geostatistical Software Library and User\u2019s Guide, Oxford University Press."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.catena.2007.09.005","article-title":"Repeated measurements on permanent plots using local variability based sampling for monitoring soil erosion","volume":"73","author":"Wang","year":"2008","journal-title":"Catena"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.ecolmodel.2008.05.006","article-title":"How to evaluate models: Observed vs. predicted or predicted vs. observed?","volume":"216","author":"Pineiro","year":"2008","journal-title":"Ecol. Model."},{"key":"ref_50","first-page":"2919","article-title":"Mapping of forest carbon by combining forest inventory data and satellite images with co-simulation based up-scaling method","volume":"29","author":"Zhang","year":"2009","journal-title":"Acta Ecol. Sin."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/S0034-4257(02)00066-4","article-title":"Mapping and uncertainty of predictions based on multiple primary variables from joint co-simulation with TM image","volume":"83","author":"Gertner","year":"2002","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/7\/571\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:25:31Z","timestamp":1760210731000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/7\/571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,6]]},"references-count":51,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2016,7]]}},"alternative-id":["rs8070571"],"URL":"https:\/\/doi.org\/10.3390\/rs8070571","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2016,7,6]]}}}