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

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.

scholarly journals Spatial Heterogeneity in Chinese Forest Area Change in the Early 21st Century

Forests ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 232 ◽  
Author(s):  
Jiayue Wang ◽  
Liangjie Xin ◽  
Minghong Tan ◽  
Yahui Wang
Keyword(s):  
Spatial Heterogeneity ◽  
21St Century ◽  
Forest Area ◽  
Area Change ◽  
Early 21St Century ◽  
Forest Area Change ◽  
The Early 21St Century

Difficult to map regions in 30 m global land cover mapping determined with a common validation dataset

2018 ◽  
Vol 39 (12) ◽  
pp. 4077-4087 ◽  
Author(s):  
Le Yu ◽  
Xiaoxuan Liu ◽  
Yuanyuan Zhao ◽  
Chaoqing Yu ◽  
Peng Gong
Keyword(s):  
Land Cover ◽  
Validation Dataset ◽  
Land Cover Mapping ◽  
Global Land Cover ◽  
Global Land

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

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4452 ◽  
Author(s):  
Chunying Ren ◽  
Bai Zhang ◽  
Zongming Wang ◽  
Lin Li ◽  
Mingming Jia
Keyword(s):  
Land Cover ◽  
Temporal Resolution ◽  
Northeast China ◽  
Forest Cover ◽  
Forest Area ◽  
Ground Truth Data ◽  
Biogeochemical Models ◽  
Global Land Cover ◽  
Object Based ◽  
Global Land

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.

scholarly journals Development of Global Cropland Agreement Level Analysis by Integrating Pixel Similarity of Recent Global Land Cover Datasets

2017 ◽  
Author(s):  
Ketut Wikantika ◽  
Anjar Dimara Sakti ◽  
Wataru Takeuchi
Keyword(s):  
Land Cover ◽  
National Level ◽  
Sources Of Information ◽  
Change Analysis ◽  
Total Size ◽  
Area Change ◽  
Cropland Area ◽  
Global Land Cover ◽  
Area Distribution ◽  
Global Land

Global cropland monitoring is important when considering tactical strategies for achieving food sustainability. Different global land cover (GLC) datasets providing cropland information have already been published and they are used in many applications. The different data input methods, classification techniques, class definitions and production years among the different GLC datasets make them all independently useful sources of information. This study attempted to produce a cropland agreement level (CAL) analysis based on the integration of several cropland datasets to more accurately estimate cropland area distribution. Estimating cropland area and how it has changed on a national level was done by converting the level of cropland agreement into percentages with an existing cropland fraction map. A pre-analysis showed that the four GLC datasets used in the 2005 and 2010 groups had similar year input data acquisitions. Therefore, we placed these four datasets (GlobCover, MODIS LC, GLCNMO and ESACCI LC) into 2005 and 2010 year-groups and selected them to process dataset integration through a CRISP approach. The results of this process proposed four agreement levels for this CAL analysis, and the model correlation was converted into percentage values. The cropland estimate results from the CAL analysis were observed along with FAO data statistics and showed the highest accuracy, with a 0.70 and 0.71 regression value for 2005 and 2010 respectively. In the cropland area change analysis, this CAL change analysis had the highest level of accuracy when describing the total size of cropland area change from 2005 and 2010 when compared to other individual original GLC datasets

scholarly journals Global Land Cover Assessment Using Spatial Uniformity Validation Dataset

2021 ◽  
Vol 13 (15) ◽  
pp. 2950
Author(s):  
Yoshie Ishii ◽  
Koki Iwao ◽  
Tsuguki Kinoshita
Keyword(s):  
Land Cover ◽  
Accuracy Assessment ◽  
Validation Dataset ◽  
Validation Data ◽  
Global Land Cover ◽  
Spatial Uniformity ◽  
Global Land ◽  
Land Cover Map ◽  
Map Validation ◽  
Land Cover Maps

The Degree Confluence Project (DCP) is a volunteer-based validation dataset that comprises useful information for global land cover map validation. However, there is a problem with using DCP points as validation data for the accuracy assessment of land cover maps. While resolutions of typical global land cover maps are several hundred meters to several kilometers, DCP points can only guarantee an area of several tens of meters that can be confirmed by ground photographs. So, the objective of this study is to create a land cover map validation dataset with added spatial uniformity information using satellite images and DCP points. For this, we devised a new method to semiautomatically guarantee the spatial uniformity of DCP validation data points at any resolution. This method can judge the validation data with guaranteed uniformity with a user’s accuracy of 0.954. Furthermore, we conducted the accuracy assessment for the existing global land cover maps by the DCP validation data with guaranteed spatial uniformity and found that the trends differed by class and region.

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