Selecting appropriate variables for detecting grassland to cropland changes using high resolution satellite data

Abstract

Grassland is one of the most represented, while at the same time, ecologically endangered, land cover categories in the European Union. In view of the global climate change, detecting its change is growing in importance from both an environmental and a socio-economic point of view. A well-recognised tool for Land Use and Land Cover (LULC) Change Detection (CD), including grassland changes, is Remote Sensing (RS). An important aspect affecting the accuracy of change detection is finding the optimal indicators of LULC changes (i.e., variables). Inappropriately selected variables can produce inaccurate results burdened with a number of uncertainties. The aim of our study is to find the most suitable variables for the detection of grassland to cropland change, based on a pair of high resolution images acquired by the Landsat 8 satellite and from the vector database Land Parcel Identification System (LPIS). In total, 59 variables were used to create models using Generalised Linear Models (GLM), the quality of which was verified through multi-temporal object-based change detection. Satisfactory accuracy for the detection of grassland to cropland change was achieved using all of the statistically identified models. However, a three-variable model can be recommended for practical use, namely by combining the Normalised Difference Vegetation Index (NDVI), Wetness and Fifth components of Tasselled Cap. Increasing number of variables did not significantly improve the accuracy of detection, but rather complicated the interpretation of the results and was less accurate than detection based on the original Landsat 8 images. The results obtained using these three variables are applicable in landscape management, agriculture, subsidy policy, or in updating existing LULC databases. Further research implementing these variables in combination with spatial data obtained by other RS techniques is needed.

Change detection work-flow for mapping changes from arable lands to permanent grasslands with advanced boosting methods

“…On the other hand, once this is managed, our results show the effectiveness of our proposed methodology. There are other similar studies to our work and (Helmholz et al, 2014;Klouček et al, 2018;Yang et al 2017).…”

“…The results show that it is good to do a feature selection (Ma et al, 2017) especially for the OBIA approach in order to reduce the computation time and improve the accuracy because less is sometimes more (Georganos et al, 2018). Therefore, selecting the most important variables is a necessary step similar to how as Klouček et al (2018) showed. They demonstrated that a combination of different vegetations indices brings redundant information for the change detection from grasslands to arable lands when the bi-temporal Landsat scenes were tested as well, as in our study.…”

“…They appear to be quite popular in discriminating crops and grasslands (Helmholz et al, 2014;Smith and Buckley, 2001;Müller et al, 2015). On the other hand, studies specially dedicated to mapping change transitions between crops and grassland are not so common (Weeks et al, 2014;Klouček et al, 2018;Yang et al, 2017).…”

“…However, the most recent studies focus on the utilisation of multitemporal data (Chen et al, 2018;Yin et al, 2018;Pflugmacher et al, 2019;Xu et al, 2018). Mapping transitions between crops and grasslands that only utilises bitemporal data are quite rare (Helmholz et al, 2014;Yang et al, 2017;Klouček et al, 2018). The advantage of using bi-temporal images is simplicity in the amount of data acquisition and speed of processing in comparison to image time series.…”

The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation

“…The accuracy of the MLC classification in distinguishing healthy trees from the infested ones was associated with the actual increase in the differences between the vegetation indices; the differences in variability did not play a major role (see Table A1 in Appendix B). The effect of the time point of image acquisition on the detection accuracy was more significant than the effect of the selection of a variable as vegetation indices are strongly correlated [42].…”