A Composite Method for Predicting Local Accuracies in Remotely Sensed Land-Cover Change Using Largely Non-Collocated Sample Data
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area and Datasets
2.2. Methods
3. Results
3.1. Model Fitting and Predictions
3.2. Performance Evaluations
4. Discussion
4.1. Fuzzy+Product versus PXCOV
4.2. Extensions of Fuzzy+Product to Multi-Temporal Change Analyses and Fuzzy/Fractional Classifications
4.3. Accuracy Characterization: from Local back to Global
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Type | Definition |
---|---|
Cultivated land | Land used for agriculture, horticulture, and gardens, including paddy fields, irrigated and dry farmland, vegetable and fruit garden, etc. |
Forest | Land covered by trees, vegetation covers over 30%, including deciduous and coniferous forests, and sparse woodland with cover 10–30%, etc. |
Grassland | Land covered by natural grass with cover over 10%, etc. |
Water bodies | Water bodies in land area, including river, lake, reservoir, fish pond, etc. |
Artificial surfaces | Land modified by human activities, including all kinds of habitation, industrial and mining area, transportation facilities, interior urban green zones and water bodies, etc. |
Bare land | Land with vegetation cover lower than 10%, including desert, sandy fields, Gobi, bare rocks, saline and alkaline land, etc. |
Configurations | Numbers of Sample Pixels | |||
---|---|---|---|---|
Collocated | Non-Collocated | |||
time 1 | time 2 | time 1 | time 2 | |
0 | 0 | 0 | 630 | 630 |
1 | 63 | 63 | 567 | 567 |
2 | 126 | 126 | 504 | 504 |
3 | 189 | 189 | 441 | 441 |
4 | 252 | 252 | 378 | 378 |
Study Sites | Configurations | ||||
---|---|---|---|---|---|
0 | 1 | 2 | 3 | 4 | |
(a) | |||||
CE | class + ent21 + con9 | class + dom19 + het19 + ent21 + hom7 | class + ent21 + con9 | class + ent21 + con9 | class + ent21 + con17 + hom3 |
NE | class + con5 | class + con5 | class + het5 + ent19 | class + het5 + ent21 | class + het5 |
NW | hom21 + class + ent21 | hom21 + class + ent21 | hom21 + class + ent21 | class + ent21 | class + ent21 |
SW | class + hom3 | class + hom5 | class + dom3 | class + con5 + het5 + dom15 | class + het3 |
SE | class + hom5 + dom9 | class + ent5 + dom17 + dom5 + dom9 + het9 + con5 | class + ent5 + dom19 + con7 + ent11 | class + ent5 + dom19 + con7 + ent11 | class + ent5 + het19 |
(b) | |||||
CE | con7 + class + dom13 + hom3 + dom3 + ent3 + het7 | con7 + class + dom13 | class + ent7 + het15 + ent21 + con15- + dom3 + ent3 + hom5 + con17 + ent11 + ent19 + het19 + het21 | con7 + class + het15 + ent21 + con15 + dom3 + ent3 + het19 + het21 + con19 + ent11 + het7 | con7 + class + dom19 + dom21 + dom3 + ent3 + hom13 |
NE | con7 + class + hom3 + hom5 + hom17 | ent3 + dom5 + class | ent3 + dom7 + class | ent3 + class + con17 | ent3 + class + ent15 |
NW | class + hom9 + con7 + dom21 + het19 + dom13 + con11 + dom9 + hom15 + het15 | class + hom9 + dom21 + het19 + dom13 | class + hom9 + dom21 + het19 + dom13 | class + hom9 + dom21 + het19 + dom13 | class + hom9 + dom21 + het19 + dom13 + het5 + hom21 + con13 + het13 |
SW | con15 + class + con3 + dom7 + dom9 + dom11 | con15 + class + het3 + dom7 + dom9 + dom11 | con15 + class + het3 + dom7 + het9 + het11 | con13 + class + het3 + dom11 | con13 + class + het3 + dom11 + dom7 + dom9 |
SE | ent3 + class + ent15 | ent3 + class + ent15 | ent3 + class + ent15 | ent3 + class + ent19 + het21 + dom21 | ent3 + class + ent21 + het21 + dom21 |
Study Sites | Sample Configurations | |||||
---|---|---|---|---|---|---|
0 | 1 | 2 | 3 | 4 | ||
CE | change | 0.00 | −0.48 | −0.18 | −0.10 | −0.14 |
no-change | 0.83 | 1.00 | 1.00 | 0.51 | 0.78 | |
NE | change | 0.00 | −0.04 | −0.02 | −0.02 | −0.03 |
no-change | 1.00 | 1.00 | 1.00 | 1.00 | 0.70 | |
NW | change | 0.00 | −0.21 | −0.10 | −0.11 | −0.06 |
no-change | 1.00 | 1.00 | 1.00 | 0.92 | 0.80 | |
SW | change | 0.00 | −0.05 | −0.07 | −0.08 | −0.01 |
no-change | 1.00 | 0.79 | 0.94 | 1.00 | 0.86 | |
SE | change | 0.00 | 0.06 | 0.08 | −0.01 | 0.00 |
no-change | 0.45 | 1.00 | 1.00 | 0.77 | 0.77 |
Study Sites | Methods | Configurations | ||||
---|---|---|---|---|---|---|
0 | 1 | 2 | 3 | 4 | ||
CE | I | 0.84 | 0.84 | 0.83 | 0.84 | 0.84 |
II | 0.86 | 0.89 | 0.86 | 0.84 | 0.86 | |
III | 0.77 | 0.74 | 0.76 | 0.78 | 0.76 | |
IV | 0.78 | 0.78 | 0.79 | 0.80 | 0.79 | |
NE | I | 0.72 | 0.70 | 0.70 | 0.72 | 0.72 |
II | 0.73 | 0.71 | 0.72 | 0.72 | 0.72 | |
III | 0.72 | 0.70 | 0.71 | 0.71 | 0.71 | |
IV | 0.72 | 0.70 | 0.70 | 0.70 | 0.71 | |
NW | I | 0.75 | 0.78 | 0.78 | 0.79 | 0.79 |
II | 0.75 | 0.79 | 0.79 | 0.80 | 0.79 | |
III | 0.73 | 0.76 | 0.77 | 0.78 | 0.78 | |
IV | 0.75 | 0.78 | 0.79 | 0.80 | 0.79 | |
SW | I | 0.80 | 0.81 | 0.81 | 0.81 | 0.81 |
II | 0.81 | 0.81 | 0.81 | 0.82 | 0.81 | |
III | 0.78 | 0.79 | 0.78 | 0.79 | 0.79 | |
IV | 0.78 | 0.78 | 0.79 | 0.79 | 0.79 | |
SE | I | 0.71 | 0.71 | 0.71 | 0.71 | 0.71 |
II | 0.71 | 0.72 | 0.72 | 0.71 | 0.71 | |
III | 0.70 | 0.69 | 0.70 | 0.69 | 0.70 | |
IV | 0.70 | 0.70 | 0.70 | 0.69 | 0.70 |
Study Sites | Methods Pairs | Configurations | ||||
---|---|---|---|---|---|---|
0 | 1 | 2 | 3 | 4 | ||
CE | I versus II | −0.02 | −0.05 | −0.03 | −0.00 | −0.02 |
I versus III | 0.07 * | 0.09 * | 0.07 * | 0.06 * | 0.08 * | |
I versus IV | 0.07 * | 0.05 * | 0.04 * | 0.04 * | 0.06 * | |
NE | I versus II | −0.01 | −0.01 | −0.02 | −0.01 | −0.01 |
I versus III | 0.00 | −0.01 | −0.00 | 0.01 * | 0.00 | |
I versus IV | 0.00 | −0.00 | 0.01 | 0.01 * | 0.00 | |
NW | I versus II | −0.01 | −0.01 | −0.01 | −0.01 | −0.00 |
I versus III | 0.01 * | 0.01 * | 0.01 * | 0.01 * | 0.01 * | |
I versus IV | −0.01 | −0.00 | −0.01 | −0.01 | −0.00 | |
SW | I versus II | −0.01 | −0.01 | −0.01 | −0.01 | −0.00 |
I versus III | 0.02 * | 0.02 * | 0.02 * | 0.02 * | 0.02 * | |
I versus IV | 0.02 * | 0.02 * | 0.02 * | 0.02 * | 0.02 * | |
SE | I versus II | 0.00 | −0.01 | −0.01 | −0.00 | −0.00 |
I versus III | 0.01 * | 0.01 * | 0.02 * | 0.02 * | 0.01 * | |
I versus IV | 0.01 * | 0.01 * | 0.01 * | 0.01 * | 0.01 * |
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Mei, Y.; Zhang, J.; Zhang, W.; Liu, F. A Composite Method for Predicting Local Accuracies in Remotely Sensed Land-Cover Change Using Largely Non-Collocated Sample Data. Remote Sens. 2019, 11, 2818. https://doi.org/10.3390/rs11232818
Mei Y, Zhang J, Zhang W, Liu F. A Composite Method for Predicting Local Accuracies in Remotely Sensed Land-Cover Change Using Largely Non-Collocated Sample Data. Remote Sensing. 2019; 11(23):2818. https://doi.org/10.3390/rs11232818
Chicago/Turabian StyleMei, Yingying, Jingxiong Zhang, Wangle Zhang, and Fengzhu Liu. 2019. "A Composite Method for Predicting Local Accuracies in Remotely Sensed Land-Cover Change Using Largely Non-Collocated Sample Data" Remote Sensing 11, no. 23: 2818. https://doi.org/10.3390/rs11232818
APA StyleMei, Y., Zhang, J., Zhang, W., & Liu, F. (2019). A Composite Method for Predicting Local Accuracies in Remotely Sensed Land-Cover Change Using Largely Non-Collocated Sample Data. Remote Sensing, 11(23), 2818. https://doi.org/10.3390/rs11232818