Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area
2. Study Area
2.1. Study Area Background
2.2. Agricultural Practicing During Drought Period
3. Materials and Methodologies
3.2. Image Analysis Procedures
3.3. Recommendations on Object-Based Classification Procedure
- Shadowed areas: On GeoEye 1 image, we observed shadows of vegetation and low buildings. In this circumstance, this shadow effect was not significant, so no further procedure was applied. However, if shadow effects are obvious (mainly in urban or forested areas), an object—spatial relationship (nearest neighbor, for instance) will be applied in the decision rules.
- Band indices: Using different band indices is a good approach to derive effective rules of decision tree to assign classes. It required less time to categorize classes on the Worldview2 image than the GeoEye1 one, especially when it is effective to differentiate roads from water layer. On images, road and water surfaces often appear as similar colors—dark blue. Nevertheless, these indices are quite sensitive, so in each case, the selected range of values of each index designated to each class are different. The image preprocessing step is very important before calculating band indices.
- Segmentation procedure: This procedure requires a lot of time, computer RAM storage, and analyst experience. Sub-divided regions that are too small or too large may increase processing time, or lead to missing data or mixed classes.
- Decision tree: Constructing rules for the decision tree to categorize objects into classes is very important, and obviously not an easy mission. Each object has its own typical characteristics, and shares some with others. The more indicators there are, the more supportive and successful decision rules are. However, there is another constraint of cost and time consumption.
- Subset images helps to save time to generate classified rules and decision trees. We divided the study area into three parts regarding the administrative boundary.
- Manual editing is a necessary step to approach a better result. A significant difference between object-based classification and supervised classification is the smallest object. A pixel is the smallest object in a supervised method, so “salt and pepper” errors exist on the classified image. An object that may cover at least 50 or 100 pixels, depending on segmentation methods is the smallest one in the object-based classification. Thus, it is easier to detect mis-classified objects on results, and bring them to their true class. Ecognition Developer software allows users to process segmentation, and manual editing very conveniently.
4. Results and Discussion
4.1. Land Use—Land Cover Classification
4.2. Overall Changes in LULC during 2011–2016
4.3. Impacts of Drought and Human on LULC Change during 2011–2016
4.3.1. Agricultural Land Transition
4.3.2. Salterns and Shrimp Farming
4.3.3. Vegetative Areas
4.4. Agricultural Practicing in Phuoc The Commune
4.5. Limitations of the Study
- During drought periods, surface water resources reduced by one fourth, resulting in water shortage for irrigated and rain-fed rice fields, and a slight increase of bare land (unused land);
- To quantify impacts of drought, and water shortage on agricultural and aquacultural lands, we conducted a higher detailed image analysis; results showed a dramatic decrease of those land types (more than 50% and 44%, respectively). The extent of inactive agriculture and dry shrimp fields may promote land degradation, a spreading of sand into land, and desertification processes;
- Assessing interaction of local residents and droughts on LULC change, we observed local efforts to alternate rice and dry field by orchards growing drought-tolerant plants, such as dragon fruit or pitayas. Those plants not only adapt the prolonged dry conditions of the study area, but also bring higher income. Additionally, local residents and authorities were also active in promoting LULC transitions by vegetating bare soil and sand with stabilizing covering crops. Vegetating and crop rotation are examples of adaptive methods to combat drought.
Conflicts of Interest
Appendix A. Training Sample
|Land Types||Descriptions||Imagery Samples|
|Level 1||Level 2||Shape & distribution||Color||Indices||WV2||GE1||Ref img|
|1||Built up||1||Build up||Rectangle, dense, near main roads or coastlines.||White or light blue or red rooftops.||NDWI|
|2||Salterns and shrimp farming||2||Salterns certain size.||Rectangle, but white or brown.||Blue, dark blue NDWI||NDVI|
|3||Active shrimp farming||Square, certain size. 65 by 65 m.||Blue, dark blue.||NDVI|
|4||Inactive shrimp farming||Square, certain size 65 by 65 m.||Tan, brown, white.||NDSI|
|10||Others land for aquaculture||Routes or rectangle.||Brown, tan.||NDSI|
|3||Water||5||Water||Undefined shape.||Dark blue or green.||NDVI|
|4||Agriculture||6||Rice production||Undefined shape, smooth surface.||Dark to light green (natural color).||NDVI|
|Dark to bright red (false color).|
|11||Orchards||Rectangle, rows and columns, mixed with built up.||Green and/or mixing brown as soil background.||NDVI|
|12||Inactive agriculture||Rectangle or undefined shape.||Brown, tan with some greenness||NDSI|
|5||Vegetation||7||Vegetation||Undefined shape, narrow like borders.||Green to dark green||NDVI|
|6||Bare land||8||Bare land||Undefined shape, no or very spare vegetation.||Brown, tan, some greenness.||NDSI|
|7||Sand||9||Sand||Undefined shape, near coastlines, or dried fields.||Yellow or white, tan, some greyness.||NDVI|
|13||Vegetation on sand||Undefined shape, near coastlines.||Green||NDVI|
Appendix B. Land Use Change in Phuoc The community
Appendix C. The 2014 Land Use Land Cover Map
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|2||Blue||450–510 nm||450–510 nm|
|3||Green||510–580 nm||510–580 nm|
|5||Red||630–690 nm||655–690 nm|
|6||Red Edge||705–745 nm||NA|
|7||NIR1||770–895 nm||780–920 nm|
|1||Built up||1||Built up||Urban area, transportation, mining and windpower.|
|2||Salterns and shrimp farming||2||Salterns||Used for sea salt production; near coastline; different shape.|
|3||Active shrimp farming||Square objects; used for shrimp cultivation, well organized.|
|4||Inactive shrimp farming||Square shapes; no practice at time of observation.|
|10||Land in shrimp farming||Normally linear object;bright color (tan); used for transportation.|
|3||Water||5||Water||Lakes, ponds, streams, rivers.|
|4||Agriculture||6||Rice production/paddy fields||In different shape (rectangular); smooth surface; still practicing.|
|11||Orchards||Drought-tolerant plants; normally in rectangular; clear rows, and pots; mixing urban|
|12||Inactive agriculture||No/sparse vegetation; normally rectangular; clear/sharp borders.|
|5||Vegetation||7||Vegetation||Near urban area, or fields; dense; rough surface.|
|6||Bare land||8||Bare land||Bare soil, no/sparse vegetation (small and low bush).|
|7||Sand||9||Sand||No vegetation;yellow, or white; near the coastline.|
|13||Vegetation on sand||Small and low bush; near. coastline; dense or sparse.|
|ID||Land Types||2011 Map’s Accuracy||2016 Map’s Accuracy||Overall|
|1||Built up||86.2||92.6||98||84.5||2011’s overall = 85%|
|2||Salterns||100||100||100||93.8||2011’s kappa= 82.9%|
|3||Active shrimp farming||100||100||100||100||2016’s overall = 87.3%|
|4||Inactive shrimp farming||90||100||83.3||90.9||2016’s kappa=85.6%|
|5||Water||66.7||66.7||100||66.7||Total points = 521|
|10||Land for aquaculture||100||83.3||92.3||92.3|
|13||Vegetation on sand||83.5||94.7||71.8||87.1|
|No||Land Types||Area in 2011 (ha)||Area in 2014 (ha)||Area in 2016 (ha)||Difference 2011–2016 (ha)||% of Difference|
|2||Salterns and shrimp farming||311.9||321.9||322.4||↑10.5||↑3.4|
|No||Land Types||Area in 2011 (ha)||Area in 2016 (ha)||Area of Difference (ha)||% of Difference|
|5||Active shrimp farming||99.8||55.8|
|6||Inactive shrimp farming||79.5||114.7|
|7||Vegetation on sand||1071.9||937.7|
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Tran, H.T.; Campbell, J.B.; Wynne, R.H.; Shao, Y.; Phan, S.V. Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area. Remote Sens. 2019, 11, 333. https://doi.org/10.3390/rs11030333
Tran HT, Campbell JB, Wynne RH, Shao Y, Phan SV. Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area. Remote Sensing. 2019; 11(3):333. https://doi.org/10.3390/rs11030333Chicago/Turabian Style
Tran, Hoa Thi, James B. Campbell, Randolph H. Wynne, Yang Shao, and Son Viet Phan. 2019. "Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area" Remote Sensing 11, no. 3: 333. https://doi.org/10.3390/rs11030333