An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data
"> Graphical abstract
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<p>Mega-file data cube (MFDC) of Tajikistan using Landsat ETM+, MODIS images, and secondary data as described in Section 2.2. Illustrated for MFDC2005. The time-series NDVI profiles of MODIS for 4 land cover types from typical pixels are illustrated.</p> ">
<p>Data layers of Tajikistan in the mega file data cube of independent year 2010 (MFDC2010). Data layers illustrated here are: (<b>a</b>) Landsat scaled NDVI, representing biomass and the leaf area index (LAI); (<b>b</b>) Landsat band 3 reflectance, representing chlorophyll absorption; (<b>c</b>) Landsat band 5 reflectance, representing moisture sensitivity; (<b>d</b>) Landsat band 6 digital number (DN), representing thermal emissivity; (<b>e</b>) SRTM-derived elevation; (<b>f</b>) SRTM-derived slope; and (<b>g</b>) MODIS NDVI monthly MVC for the independent year 2010.</p> ">
<p>Data layers of Tajikistan in the mega file data cube of independent year 2010 (MFDC2010). Data layers illustrated here are: (<b>a</b>) Landsat scaled NDVI, representing biomass and the leaf area index (LAI); (<b>b</b>) Landsat band 3 reflectance, representing chlorophyll absorption; (<b>c</b>) Landsat band 5 reflectance, representing moisture sensitivity; (<b>d</b>) Landsat band 6 digital number (DN), representing thermal emissivity; (<b>e</b>) SRTM-derived elevation; (<b>f</b>) SRTM-derived slope; and (<b>g</b>) MODIS NDVI monthly MVC for the independent year 2010.</p> ">
<p>Knowledge capture for producing the truth layer of croplands. (<b>a</b>) Step 1: bispectral plots. Cropland <span class="html-italic">vs.</span> non-cropland identification and labeling based on tasseled cap bi-spectral plots of unsupervised classes using Landsat red <span class="html-italic">vs.</span> near infrared bands and very high resolution imagery (sub-meter to 5 m data) snap-shots as in-situ data. (<b>b</b>) Step 2: secondary data and decision trees. Watering method (e.g., irrigated or rainfed) and crop type determination using temporal characteristics of MODIS 250 m NDVI, Crop Water Requirement (CWR), secondary data, and irrigation structure. (<b>c</b>) Step 3: textural characteristics using spatial data. Looking at spatial extent to identify and label classes based on large scale (<span class="html-italic">i.e.</span>, contiguous) <span class="html-italic">vs.</span> small scale (<span class="html-italic">i.e.</span>, fragmented) established using very high resolution (sub-meter to 5 m) data. (<b>d</b>) Step 4: spectro-temporal characteristics using MODIS time-series. MODIS NDVI temporal profile to identify and label cropping intensities. Illustrated here for irrigated <span class="html-italic">vs.</span> rainfed.</p> ">
<p>Knowledge capture for producing the truth layer of croplands. (<b>a</b>) Step 1: bispectral plots. Cropland <span class="html-italic">vs.</span> non-cropland identification and labeling based on tasseled cap bi-spectral plots of unsupervised classes using Landsat red <span class="html-italic">vs.</span> near infrared bands and very high resolution imagery (sub-meter to 5 m data) snap-shots as in-situ data. (<b>b</b>) Step 2: secondary data and decision trees. Watering method (e.g., irrigated or rainfed) and crop type determination using temporal characteristics of MODIS 250 m NDVI, Crop Water Requirement (CWR), secondary data, and irrigation structure. (<b>c</b>) Step 3: textural characteristics using spatial data. Looking at spatial extent to identify and label classes based on large scale (<span class="html-italic">i.e.</span>, contiguous) <span class="html-italic">vs.</span> small scale (<span class="html-italic">i.e.</span>, fragmented) established using very high resolution (sub-meter to 5 m) data. (<b>d</b>) Step 4: spectro-temporal characteristics using MODIS time-series. MODIS NDVI temporal profile to identify and label cropping intensities. Illustrated here for irrigated <span class="html-italic">vs.</span> rainfed.</p> ">
<p>Automated cropland classification algorithm (ACCA). Sample illustrations of irrigated (Algorithm 1(a,b,c), Algorithm 2(a,b,c) and Algorithm 3) and rainfed (Algorithm 4(a,b,c,d,e)) algorithms. Numerous such algorithms are written, based on knowledge capture, to finally complete ACCA.</p> ">
<p>Automated cropland classification algorithm (ACCA). Sample illustrations of irrigated (Algorithm 1(a,b,c), Algorithm 2(a,b,c) and Algorithm 3) and rainfed (Algorithm 4(a,b,c,d,e)) algorithms. Numerous such algorithms are written, based on knowledge capture, to finally complete ACCA.</p> ">
<p>Land cover classification (16 classes, (<b>a</b>)) and <b>truth cropland layer (2 classes, b)) for Tajikistan for year 2005 (TCL2005)</b> generated using mega-file data cube of year 2005 (MFDC2005, <a href="#f1-remotesensing-04-02890" class="html-fig">Figure 1</a>, <a href="#t1-remotesensing-04-02890" class="html-table">Table 1</a>). Detailed image interpretation techniques involved bispectral plots (e.g., <a href="#f3-remotesensing-04-02890" class="html-fig">Figure 3(a)</a>), secondary data (e.g., <a href="#f3-remotesensing-04-02890" class="html-fig">Figure 3(b)</a>), decision trees (e.g., <a href="#f3-remotesensing-04-02890" class="html-fig">Figure 3(b)</a>), textural characteristics (e.g., <a href="#f3-remotesensing-04-02890" class="html-fig">Figure 3(c)</a>), spectro-temporal characteristics (e.g., <a href="#f3-remotesensing-04-02890" class="html-fig">Figure (3(d)</a>), and field-plot data (e.g <a href="#f3-remotesensing-04-02890" class="html-fig">Figure 3d</a>)).</p> ">
<p>Automated Cropland Classification Algorithm (ACCA) derived croplands of Tajikistan for year 2005 <b>(ACL2005)</b> using the same data (MFDC2005). This is compared with CTL (<a href="#f5-remotesensing-04-02890" class="html-fig">Figure 5</a>). The result matrix is in <a href="#t3-remotesensing-04-02890" class="html-table">Table 3(a)</a>.</p> ">
<p><b>TCL2010</b> (<b>a</b>) <b><span class="html-italic">vs.</span></b> (<b>ACL2010</b> (<b>b</b>). Automated Cropland Classification Algorithm (ACCA) derived croplands of Tajikistan for year 2010 (<b>ACL2010,</b> (b)) is compared with the truth cropland layer for Tajikistan for year 2010 (<b>TCL2010,</b> (<b>a</b>)). TCL2010 is generated using the independent data layer (MFDC20). ACL2010 is produced by applying ACCA algorithm of MFDC2010. The result matrix is in <a href="#t3-remotesensing-04-02890" class="html-table">Table 3(b)</a>.</p> ">
<p>Comparisons between truth cropland layer (TCL) of 2005 (<b>a</b>) / 2010 (<b>b</b>) and ACCA-derived cropland layers (ACL) of 2005 (<b>c</b>) / 2010 (<b>d</b>). Specifically, comparisons of <b>TCL2005</b> (<b>a</b>) <b><span class="html-italic">vs</span></b>. <b>ACL2005</b> (<b>c</b>), and <b>TCL2010</b> (<b>b</b>) <b><span class="html-italic">vs</span></b>. <b>ACL2010</b> (<b>d</b>). Automated Cropland Classification Algorithm (ACCA) derived croplands of Tajikistan for years 2005 (<b>ACL2005</b>) and 2010 (<b>ACL2010</b>) are compared with the truth cropland layer for Tajikistan for year 2005 (<b>TCL2005</b>) and year 2010 (<b>TCL2010</b>). The results matrices are in <a href="#t3-remotesensing-04-02890" class="html-table">Table 3(a,b)</a>.</p> ">
Abstract
:1. Introduction
2. Methods
2.1. Study Area
2.2. Data Fusion and Mega File Data Cubes (MFDC) Involving MODIS, Landsat, and Secondary Data
2.3. In situ Data
2.4. Algorithm Development Methods
- Generate/obtain truth/reference cropland layer (TCL): TCL can be obtained from secondary sources (e.g., from a reliable national institute such as the cropland data layer from United States Department of Agriculture). In absence of such a layer, TCL is generated using remote sensing data (see Section 2.4.1). In this study, TCLs for Tajikistan were produced for the year 2005 (TCL2005) as well as for the year 2010 (TCL2010) as described in detail in Section 2.4.1.
- ACCA rules and coding leading to ACCA derived cropland layer (ACL): ACCA rules are written for mega file data cube (MFDC, Section 2.2) in order to produce ACCA generated cropland layer (ACL) products that accurately replicate or come very close to replicating TCLs (within 20% quantity disagreement amongst ∼80% user’s and producer’s accuracies). The ACCA was developed for Tajikistan for the year 2005 using MFDC2005 and then applied for the year it was developed (year 2005) as well as for another independent year (year 2010). The ACCA coding process is described in detail in Section 2.4.2.
- Difference between the methods for generating TCL and ACL to demonstrate the advantage of ACL: The process of generating TCL is time consuming. For example, it involves image classification, class identification, and accuracy assessment and so on. Typical methods and approaches of generating TCLs are explained in Thenkabail and others [11]. TCLs can also be obtained from secondary sources, but they too go through time-consuming and resource-intensive processes [11]. In contrast, ACL only requires time and resource to develop the ACCA for the first year. After that the ACCA can be routinely applied for the area for which it is developed year after year to produce ACLs.
- Testing ACCA on independent data layers, comparing ACLs vs. TCLs: For Tajikistan, ACCA was developed (Section 2.4.2) using mega file data cube for year 2005 (MFDC2005). This resulted in ACCA generated cropland layer (ACL2005). ACCA is then applied to produce cropland data layers for the year for which it was developed, 2005, and for another independent year, 2010. This resulted in two ACCA generated cropland layers: ACL2005 and ACL2010. The ACLs were then tested for accuracies and errors against reference/truth cropland layers (TCLs) of corresponding years: TCL2005 and TCL2010. First for the year for which it was developed (year 2005) and then for another independent year (year 2010). This process is discussed in Section 2.4.3.
2.4.1. Producing Truth/Reference Cropland Layer (TCL)
- bispectral plots (e.g., Figure 3(a)),
- secondary data and decision trees (e.g., Figure 3(b)),
- textural characteristics (e.g., Figure 3(c)),
- spectro-temporal characteristics (e.g., Figure 3(d)), and
- field-plot data (e.g., Figure 3(d)) and very high resolution imagery (e.g., Figure 3(a)) involving a total of 1,770 points from precise locations (Section 2.3).
2.4.2. Automated Cropland Classification Algorithm (ACCA) Derived Cropland Layer (ACL)
2.4.3. Applying ACCA on Independent Years
3. Results and Discussions
3.1. Truth/Reference Layer of Croplands for Tajikistan Based on Data for Year 2005
3.2. ACCA Generated Croplands (ACLs) for Tajikistan for the Same Year (2005) as That of the Truth Cropland Layer (TCL, Year 2005)
3.3. Truth/Reference Layer of Croplands for Tajikistan Based on Data for Year 2010
3.4. ACCA Generated Croplands for Tajikistan for the Independent Year of 2010
3.5. Accuracies and Errors
4. Uniqueness, Importance, Impact and Limitations of ACCA
4.1. The Uniqueness of ACCA Algorithm
4.2. Limitations of ACCA and the Way forward to Developing a Global ACCA
4.3. Implications and Applications of ACCA in Global Cropland Mapping
5. Conclusions and Way Forward
Acknowledgments
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Satellite | Sensor | Year of Image | Spatial | Spectral | Radiometric | Band Range | Band Widths | Irradiance | Data Points | Frequency of Revisit | Usage |
---|---|---|---|---|---|---|---|---|---|---|---|
no unit | no unit | no unit | m | # | bit | μm | μm | W·m−2·sr−1·μm−1 | # per hectares | days | no unit |
Landsat | ETM+ | GLS2005 | 30 | 8 | 8 | 0.45–0.52 | 0.07 | 1,970 | 11.1 | 16 | In MFDC2005 |
0.52–0.60 | 0.08 | 1,843 | |||||||||
0.63–0.69 | 0.06 | 1,555 | |||||||||
0.75–0.90 | 0.15 | 1,047 | |||||||||
1.55–1.75 | 0.2 | 227 | |||||||||
10.4–12.5 | 2.1 | 0 | |||||||||
MODIS | Terra | 2005 | 250 | 36 | 12 | 0.62–0.67 | 0.05 | 1,528 | 0.16 | 1 | In MFDC2005 |
Landsat | ETM+ | 2010 | 30 | 8 | 8 | Same as GLS2005 | Same as GLS2005 | Same as GLS2005 | 11.1 | 16 | In MFDC2010 |
MODIS | Terra | 2010 | 250 | 36 | 12 | Same as MODIS2005 | Same as MODIS2005 | Same as MODIS2005 | 0.16 | 1 | In MFDC2010 |
Class # | Class Name | Area | % Total Area | Landsat NDVI | MODIS NDVI Profile | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |||||
ha | no units | no units | no units | no units | no units | no units | no units | no units | no units | no units | no units | no units | no units | no units | ||
1 | 01 Irrigated, conjunctive use, cotton-wheat-rice-dominant, large scale, double crop | 710,166 | 5 | 0.36 | 0.09 | 0.19 | 0.28 | 0.36 | 0.37 | 0.33 | 0.36 | 0.41 | 0.39 | 0.35 | 0.28 | 0.15 |
2 | 02 Rainfed, wheat-barley-dominant, large scale, single crop | 273,188 | 1.9 | 0.18 | 0.05 | 0.00 | 0.31 | 0.42 | 0.47 | 0.33 | 0.26 | 0.24 | 0.22 | 0.20 | 0.18 | 0.03 |
3 | 03 Shrub/rangeland dominate with rainfed croplands | 470,037 | 3.3 | 0.20 | 0.01 | −0.01 | 0.22 | 0.38 | 0.39 | 0.37 | 0.29 | 0.26 | 0.23 | 0.21 | 0.09 | 0.01 |
4 | 04 Shrublands-grasslands | 3,384,129 | 23.6 | 0.16 | 0.02 | 0.03 | 0.12 | 0.24 | 0.24 | 0.24 | 0.21 | 0.20 | 0.18 | 0.16 | 0.07 | 0.03 |
5 | 05 Mixed, shrublands, grasslands, urban built-up | 70,554 | 0.5 | 0.21 | 0.01 | 0.02 | 0.20 | 0.33 | 0.34 | 0.34 | 0.29 | 0.26 | 0.24 | 0.22 | 0.16 | 0.09 |
6 | 06 Forest | 835,732 | 5.8 | 0.20 | 0.00 | −0.01 | 0.02 | 0.12 | 0.16 | 0.28 | 0.28 | 0.25 | 0.22 | 0.18 | 0.02 | −0.01 |
7 | 07 Tundra | 3,596,562 | 25.1 | 0.20 | −0.02 | −0.03 | −0.04 | −0.05 | −0.05 | 0.01 | 0.14 | 0.20 | 0.17 | 0.09 | −0.02 | −0.02 |
8 | 08 Wetlands | 116,386 | 0.8 | 0.21 | 0.01 | 0.02 | 0.02 | −0.05 | 0.02 | 0.15 | 0.26 | 0.25 | 0.23 | 0.20 | 0.12 | 0.00 |
9 | 09 Barren or sparcely vegetated | 3,380,265 | 23.6 | 0.11 | −0.01 | −0.03 | −0.02 | −0.01 | −0.02 | 0.02 | 0.08 | 0.10 | 0.09 | 0.05 | −0.01 | −0.01 |
10 | 10 Snow | 1,381,150 | 9.7 | 0.05 | −0.03 | −0.03 | −0.04 | −0.14 | −0.25 | −0.08 | −0.08 | −0.05 | −0.04 | −0.03 | −0.03 | −0.03 |
11 | 11 Waterbodies | 91,831 | 0.6 | −0.11 | −0.15 | −0.17 | −0.18 | −0.23 | −0.11 | −0.06 | −0.05 | −0.07 | −0.12 | −0.13 | −0.23 | −0.17 |
Total | 14,218,169 | 100 |
a. | ACCA Algorithm Derived Data for Year 2005 | ||||||
---|---|---|---|---|---|---|---|
TRUTH LAYER YR 2005 | Irrigated areas | Rainfed areas | All other LCLU classes | Row total | Producer’s accuracy | Errors of Omissions | |
Irrigated areas | 7,398,009 | 152,082 | 30,326 | 7,580,417 | 97.6 | 2.4 | |
Rainfed areas | 143,585 | 2,519,546 | 252,914 | 2,916,045 | 86.4 | 13.6 | |
All other LCLU classes | 24,215 | 20,577 | 142,205,696 | 142,250,488 | 99.97 | 0.03 | |
Column total | 7,565,809 | 2,692,205 | 142,488,936 | 152,123,251 | |||
User’s accuracy | 97.8 | 93.6 | 99.8 | 152,746,950 | |||
Errors of Commission | 2.2 | 6.4 | 0.2 | ||||
Overall accuracy | 99.6 | ||||||
Khat | 0.97 |
b. | ACCA Algorithm Derived Data for Year 2010 (Landsat ETM+ 2010 and MODIS 2010) | ||||||
---|---|---|---|---|---|---|---|
TRUTH LAYER YR 2010 | Irrigated areas | Rainfed areas | All other LCLU classes | Row total | Producer’s accuracy | Errors of Omissions | |
Irrigated areas | 7,258,443 | 412,788 | 325,116 | 7,996,347 | 90.8 | 9.2 | |
Rainfed areas | 882,856 | 2719,506 | 3,917,696 | 7,520,058 | 36.2 | 63.8 | |
All other LCLU classes | 618,103 | 1,255,142 | 178,375,106 | 180,248,351 | 99.0 | 1.0 | |
Column total | 8,759,402 | 4,387,436 | 182,617,918 | 188,353,055 | |||
User’s accuracy | 82.9 | 62.0 | 97.7 | 195,764,756 | |||
Errors of Commission | 17.1 | 38.0 | 2.3 | ||||
Overall accuracy | 96.2 | ||||||
Khat | 0.96 |
Share and Cite
Thenkabail, P.S.; Wu, Z. An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data. Remote Sens. 2012, 4, 2890-2918. https://doi.org/10.3390/rs4102890
Thenkabail PS, Wu Z. An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data. Remote Sensing. 2012; 4(10):2890-2918. https://doi.org/10.3390/rs4102890
Chicago/Turabian StyleThenkabail, Prasad S., and Zhuoting Wu. 2012. "An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data" Remote Sensing 4, no. 10: 2890-2918. https://doi.org/10.3390/rs4102890