Land cover change
Satellite(s)Sentinel-1, Sentinel-2, Landsat, VHR. | Monitoring elementLand surface reflectance, SAR backscatter. |
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Description techniqueMany possible techniques exist within the land use land cover (LULC) literature. Land cover change can be achieved in several ways, the most popular being image differencing (incl. Change Vector Analysis (CVA) and ratioing) and post classification comparison (Alqurashi and Kumar, 2013). Post classification comparison requires multiple observations classified with a consistent scheme, with the resulting comparison describing the location and size of transition between different classes (e.g. Ayele et al, 2018, Liping et al, 2018). This can be undertaken per-pixel, or at a patch level when object-based image analysis (OBIA, a process in which pixels with similar properties are grouped to form objects) is introduced (Attri et al, 2015). Such techniques have been traditionally applied to moderate resolution multispectral data captured by Sensors such as Landsat, where the range of bands allows for a range of landcovers to be accurately resolved at large scales, albeit with relatively coarse resolution (Zhu and Woodcock, 2014). While post classification is usually applied synoptically, image differencing is generally used to highlight specific types of change by comparing the values or two or more observations with image bands sensitive to the type of cover in question (Alqurashi and Kumar, 2013). One of the most common techniques is differencing multiple observations processed to NDVI or EVI (Enhanced Vegetation Index) to highlight changes in vegetation cover, while similar indices are intended for other land cover changes are also available. To date most land cover change techniques have used multispectral optical imagery, but a growing number are substituting or combining Synthetic Aperture Radar (SAR) data, which offers a number of benefits, namely all weather capture (microwave penetrates cloud) allowing for continuous change detection (Hagensieker and Waske, 2018). | Accuracy / ResolutionVariable spatial and temporal resolution according to sensors. |
Case studyDou and Chen (2017) show a range of classifiers applied to 11 Landsat images of Shenzen, China from 1988 to 2015 for land cover change assessment. When combined with post classification average accuracy was above 90%, with rapid urbanisation well captured. Thonfeld et al (2020) also conducted a long term land cover change assessment at the catchment scale in Tanzania, an area with persistent cloud cover. By combining random forest classification with change vector analysis, the authors produced 4 landscape classifications and analyzed the change trends between them. Results demonstrated significant conversion to agriculture from savanna and floodplain grassland classes. | |
Benefits
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Applicability for NorthlandYes, this type of approaches are applicable to Northland. Use of cloud and cloud shadow masking techniques will be necessary and will significantly improves change analysis. Techniques applying optical data will be limited in coverage and temporal granularity by the persistent cloud cover in the region, particularly during the winter months. Mature cloud-masking techniques are directly available for open access multispectral data (e.g. Landsat and Sentinel-2). When using commercial data, care must be taken to ensure that there is sufficiently cloud free imagery available, as cloud masking is not as mature and ordering a large volume of imagery to ensure complete cloud free coverage between multiple observations can become cost prohibitive. | |
Publications references Alqurashi, A. and Kumar, L., 2013. Investigating the use of remote sensing and GIS techniques to detect land use and land cover change: A review. Advances in Remote Sensing. Ayele, G.T., Tebeje, A.K., Demissie, S.S., Belete, M.A., Jemberrie, M.A., Teshome, W.M., Mengistu, D.T. and Teshale, E.Z., 2018. Time series land cover mapping and change detection analysis using geographic information system and remote sensing, Northern Ethiopia. Air, Soil and Water Research, 11, p.1178622117751603. Liping, C., Yujun, S. and Saeed, S., 2018. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PloS one, 13(7), p.e0200493. Attri, P., Chaudhry, S. and Sharma, S., 2015. Remote sensing & GIS based approaches for LULC change detection–a review. International Journal of Current Engineering and Technology, 5(5), pp.3126-3137. Zhu, Z. and Woodcock, C.E., 2014. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment, 152, pp.217-234. Hagensieker, R. and Waske, B., 2018. Evaluation of multi-frequency SAR images for tropical land cover mapping. Remote Sensing, 10(2), p.257. De Alban, J.D.T., Connette, G.M., Oswald, P. and Webb, E.L., 2018. Combined Landsat and L-band SAR data improves land cover classification and change detection in dynamic tropical landscapes. Remote Sensing, 10(2), p.306. Dou, P. and Chen, Y., 2017. Dynamic monitoring of land-use/land-cover change and urban expansion in Shenzhen using Landsat imagery from 1988 to 2015. International Journal of Remote Sensing, 38(19), pp.5388-5407. Thonfeld, F., Steinbach, S., Muro, J. and Kirimi, F., 2020. Long-term land use/land cover change assessment of the Kilombero catchment in Tanzania using random forest classification and robust change vector analysis. Remote sensing, 12(7), p.1057. Daudt, R.C., Le Saux, B., Boulch, A. and Gousseau, Y., 2018, July. Urban change detection for multispectral earth observation using convolutional neural networks. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 2115-2118). IEEE. | |
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