Mapping Forest Cover in Northeast China from Chinese HJ-1 Satellite Data Using an Object-Based Algorithm

Forest plays a significant role in the global carbon budget and ecological processes. The precise mapping of forest cover can help significantly reduce uncertainties in the estimation of terrestrial carbon balance. A reliable and operational method is necessary for a rapid regional forest mapping. In this study, the goal relies on mapping forest and subcategories in Northeast China through the use of high spatio-temporal resolution HJ-1 imagery and time series vegetation indices within the context of an object-based image analysis and decision tree classification. Multi-temporal HJ-1 images obtained in a single year provide an opportunity to acquire phenology information. By analyzing the difference of spectral and phenology information between forest and non-forest, forest subcategories, decision trees using threshold values were finally proposed. The resultant forest map has a high overall accuracy of 0.91 ± 0.01 with a 95% confidence interval, based on the validation using ground truth data from field surveys. The forest map extracted from HJ-1 imagery was compared with two existing global land cover datasets: GlobCover 2009 and MCD12Q1 2009. The HJ-1-based forest area is larger than that of MCD12Q1 and GlobCover and more closely resembles the national statistics data on forest area, which accounts for more than 40% of the total area of the Northeast China. The spatial disagreement primarily occurs in the northern part of the Daxing’an Mountains, Sanjiang Plain and the southwestern part of the Songliao Plain. The compared result also indicated that the forest subcategories information from global land cover products may introduce large uncertainties for ecological modeling and these should be cautiously used in various ecological models. Given the higher spatial and temporal resolution, HJ-1-based forest products could be very useful as input to biogeochemical models (particularly carbon cycle models) that require accurate and updated estimates of forest area and type.

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Large Uncertainty on Forest Area Change in the Early 21st Century among Widely Used Global Land Cover Datasets

Remote Sensing ◽

10.3390/rs12213502 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3502

Author(s):

He Chen ◽

Zhenzhong Zeng ◽

Jie Wu ◽

Liqing Peng ◽

Venkataraman Lakshmi ◽

...

Keyword(s):

Land Cover ◽

21St Century ◽

Forest Area ◽

Validation Dataset ◽

Area Change ◽

Early 21St Century ◽

Global Land Cover ◽

Global Land ◽

Earth’S System ◽

The Early 21St Century

Forests play an important role in the Earth’s system. Understanding the states and changes in global forests is vital for ecological assessments and forest policy guidance. However, there is no consensus on how global forests have changed based on current datasets. In this study, five global land cover datasets and Global Forest Resources Assessments (FRA) were assessed to reveal uncertainties in the global forest changes in the early 21st century. These datasets displayed substantial divergences in total area, spatial distribution, latitudinal profile, and annual area change from 2001 to 2012. These datasets also display completely divergent conclusions on forest area changes for different countries. Among the datasets, total forest area changes range from an increase of 1.7 × 106 km2 to a decrease of 1.6 × 106 km2. All the datasets show deforestation in the tropics. The accuracies of the datasets in detecting forest cover changes were evaluated by a global land cover validation dataset. The spatial patterns of accuracies are inconsistent among the datasets. This study calls for the development of a more accurate database to support forest policies and to contribute to global actions against climate change.

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A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-1207-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1207-1211

Author(s):

N. Soyama ◽

K. Muramatsu ◽

M. Daigo ◽

F. Ochiai ◽

N. Fujiwara

Keyword(s):

Land Cover ◽

Reference Data ◽

Ground Truth ◽

Plant Functional Type ◽

Data Sets ◽

Functional Type ◽

Reference Dataset ◽

Ground Truth Data ◽

Global Land Cover ◽

Global Land

Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers.

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Integrating global land cover products for improved forest cover characterization: an application in North America

International Journal of Digital Earth ◽

10.1080/17538947.2013.856959 ◽

2013 ◽

Vol 7 (9) ◽

pp. 709-724 ◽

Cited By ~ 33

Author(s):

Xiao-Peng Song ◽

Chengquan Huang ◽

Min Feng ◽

Joseph O. Sexton ◽

Saurabh Channan ◽

...

Keyword(s):

North America ◽

Land Cover ◽

Forest Cover ◽

Global Land Cover ◽

Global Land

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Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada

Remote Sensing ◽

10.3390/rs11192286 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2286

Author(s):

Libo Wang ◽

Paul Bartlett ◽

Darren Pouliot ◽

Ed Chan ◽

Céline Lamarche ◽

...

Keyword(s):

Land Cover ◽

Spatial Resolution ◽

Land Surface ◽

Forest Cover ◽

Reference Sample ◽

Tree Cover ◽

Initial Surface ◽

Forest Cover Change ◽

Global Land Cover ◽

Global Land

Global land cover information is required to initialize land surface and Earth system models. In recent years, new land cover (LC) datasets at finer spatial resolutions have become available while those currently implemented in most models are outdated. This study assesses the applicability of the Climate Change Initiative (CCI) LC product for use in the Canadian Land Surface Scheme (CLASS) through comparison with finer resolution datasets over Canada, assisted with reference sample data and a vegetation continuous field tree cover fraction dataset. The results show that in comparison with the finer resolution maps over Canada, the 300 m CCI product provides much improved LC distribution over that from the 1 km GLC2000 dataset currently used to provide initial surface conditions in CLASS. However, the CCI dataset appears to overestimate needleleaf forest cover especially in the taiga-tundra transition zone of northwestern Canada. This may have partly resulted from limited availability of clear sky MEdium Resolution Imaging Spectrometer (MERIS) images used to generate the CCI classification maps due to the long snow cover season in Canada. In addition, changes based on the CCI time series are not always consistent with those from the MODIS or a Landsat-based forest cover change dataset, especially prior to 2003 when only coarse spatial resolution satellite data were available for change detection in the CCI product. It will be helpful for application in global simulations to determine whether these results also apply to other regions with similar landscapes, such as Eurasia. Nevertheless, the detailed LC classes and finer spatial resolution in the CCI dataset provide an improved reference map for use in land surface models in Canada. The results also suggest that uncertainties in the current cross-walking tables are a major source of the often large differences in the plant functional types (PFT) maps, and should be an area of focus in future work.

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Tropical forest area measured from global land-cover classifications: Inverse calibration models based on spatial textures

Remote Sensing of Environment ◽

10.1016/s0034-4257(96)00077-6 ◽

1997 ◽

Vol 59 (1) ◽

pp. 29-43 ◽

Cited By ~ 34

Author(s):

P Mayaux

Keyword(s):

Land Cover ◽

Tropical Forest ◽

Forest Area ◽

Global Land Cover ◽

Calibration Models ◽

Global Land ◽

Inverse Calibration

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A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-1207-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1207-1211

Author(s):

N. Soyama ◽

K. Muramatsu ◽

M. Daigo ◽

F. Ochiai ◽

N. Fujiwara

Keyword(s):

Land Cover ◽

Reference Data ◽

Ground Truth ◽

Plant Functional Type ◽

Data Sets ◽

Functional Type ◽

Reference Dataset ◽

Ground Truth Data ◽

Global Land Cover ◽

Global Land

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Representation of tree cover in global land cover products: Finland as a case study area

Environmental Monitoring and Assessment ◽

10.1007/s10661-021-08898-2 ◽

2021 ◽

Vol 193 (3) ◽

Author(s):

Titta Majasalmi ◽

Miina Rautiainen

Keyword(s):

Land Cover ◽

Land Surface ◽

Forest Cover ◽

Surface Fluxes ◽

Tree Cover ◽

Deciduous Tree ◽

Spatial Mapping ◽

Deciduous Species ◽

Global Land Cover ◽

Global Land

AbstractForest extent mapping is required for climate modeling and monitoring changes in ecosystem state. Different global land cover (LC) products employ simple tree cover (referred also as “forest cover” or even “vegetation cover”) definitions to differentiate forests from non-forests. Since 1990, a large number of forest extent maps have become available. Although many studies have compared forest extent data, they often use old data (i.e., around the year 2000). In this study, we assessed tree cover representations of three different annual, global LC products (MODIS VCF (MOD44B, Collection 6 (C6)), MCD12Q1 (C6), and CCI LC (v.2.1.1)) using the Finnish Multi-Source National Forest Inventory (MS-NFI) data for the year 2017. In addition, we present an intercomparison approach for analyzing spatial representations of coniferous and deciduous species. Intercomparison of different LC products is often overlooked due to challenges involved in non-standard and overlapping LC class definitions. Global LC products are used for monitoring changes in land use and land cover and modeling of surface fluxes. Given that LC is a major driver of global change through modifiers such as land surface albedo, more attention should be paid to spatial mapping of coniferous and deciduous species. Our results show that tree cover was either overestimated or underestimated depending on the LC product, and classification accuracy varied between 42 and 75%. Intercomparison of the LC products showed large differences in conifer and deciduous species spatial distributions. Spatial mapping of coniferous and deciduous tree covers was the best represented by the CCI LC product as compared with the reference MS-NFI data.

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Integrating Land-Cover Products Based on Ontologies and Local Accuracy

Information ◽

10.3390/info12060236 ◽

2021 ◽

Vol 12 (6) ◽

pp. 236

Author(s):

Ling Zhu ◽

Guangshuai Jin ◽

Dejun Gao

Keyword(s):

Land Cover ◽

Semantic Similarity ◽

Mixed Forest ◽

Global Scale ◽

Semantic Mapping ◽

Global Land Cover ◽

Resolution Observation ◽

Local Accuracy ◽

Global Land ◽

Broad Leaf

Freely available satellite imagery improves the research and production of land-cover products at the global scale or over large areas. The integration of land-cover products is a process of combining the advantages or characteristics of several products to generate new products and meet the demand for special needs. This study presents an ontology-based semantic mapping approach for integration land-cover products using hybrid ontology with EAGLE (EIONET Action Group on Land monitoring in Europe) matrix elements as the shared vocabulary, linking and comparing concepts from multiple local ontologies. Ontology mapping based on term, attribute and instance is combined to obtain the semantic similarity between heterogeneous land-cover products and realise the integration on a schema level. Moreover, through the collection and interpretation of ground verification points, the local accuracy of the source product is evaluated using the index Kriging method. Two integration models are developed that combine semantic similarity and local accuracy. Taking NLCD (National Land Cover Database) and FROM-GLC-Seg (Finer Resolution Observation and Monitoring-Global Land Cover-Segmentation) as source products and the second-level class refinement of GlobeLand30 land-cover product as an example, the forest class is subdivided into broad-leaf, coniferous and mixed forest. Results show that the highest accuracies of the second class are 82.6%, 72.0% and 60.0%, respectively, for broad-leaf, coniferous and mixed forest.

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