Landuse and NDVI change analysis of Sperchios river basin (Greece) with different spatial resolution sensor data by Landsat/MSS/TM and OLI

Abstract

A B S T R A C TThe assessment of land use-land cover (LULC) and normalized difference vegetation index (NDVI) changes on hydrology is essential for the development of sustainable water resource strategies. This study focuses on Sperchios river catchment, a complex and very heterogeneous area made up of numerous land covers that are difficult to map due to spectral similarities. Difficulties such as multi-seasonal spectral variables corresponding to different stages of land cover phenological development were addressed by choosing an unsupervised classification algorithm called k-means in combination with the Corine land cover database and past scientific studies. Available remote sensing data included three Landsat images (MSS, TM, OLI) all of which were resampled to a common 30-m resolution. Final LULC classifications in the period 1972-2013 separated forest, agricultural land and heath land and detected unified bare with urban land and pastures with areas of low vegetation. The recorded results include a significant reduction (-80%) of vegetation (NDVI values), 28 and 26% reduction rates in forests and pastures-low vegetation, respectively, a strong increase (114%) in heath land and an 47% increase, as concerns the agricultural land. Using of random points estimated the classification accuracy to be 75.4 and 85.2% for LULC of 1972-2013, respectively. This particular study constitutes a preliminary stage of an attempted integrated basin management of Sperchios river intending to contribute to overall ecological quality assessment of Sperchios river.

Normalized Difference Vegetation Index Continuity of the Landsat 4-5 MSS and TM: Investigations Based on Simulation

“…Meanwhile, for the MSS, which has two NIR channels located in 700-800 nm (NIR1, B3) and in 800-1100 nm (NIR2, B4) respectively, with a Red channel (B2) ( Table 1 and Figure 1), two MSS NDVIs labeled as MSS32 and MSS42 were estimated accordingly. In previous investigations (e.g., in [10,15,21]), the MSS NIR2 was used in NDVI calculation.…”

“…To our knowledge, comparisons of the MSS and TM were previously shown [6][7][8][9][10]; however, practical applications of the products were relatively limited due mainly to the difficulty in data accessibility [3]. Thanks to the data open policy [1][2][3], the Landsat archive with continuing inputs is freely accessible to all users, which enriched related investigations for historical issues especially by incorporating the archive of MSS and TM [11][12][13][14][15][16]. Although the MSS and TM have been consistently calibrated (with small discrepancy), it must be emphasized that they still differ significantly from the viewpoint of their spectral properties [17].…”

“…Although the MSS and TM have been consistently calibrated (with small discrepancy), it must be emphasized that they still differ significantly from the viewpoint of their spectral properties [17]. For example, characterization differences between the Landsat sensors result in discrepancies in land use classification [7,8,15], water quality retrievals [18], and vegetation indices [5,10,19,20]. Nevertheless, the sensor related bias in the normalized difference vegetation index (NDVI) was not considered in several investigations, such as in [15,21].…”

Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation

“…However, comprehensive investigations on between-sensor comparability have still been relatively limited when compared against the increasing use of multi-temporal Landsat records for time series analyses, as mentioned in [3]. Meanwhile, sensor-related difference has not been considered, for example, in spectral reflectance or indices [9,10]. In terms of spectral reflectance indices and classification, there have been comparisons separately done between different Landsat sensors.…”

“…In terms of spectral reflectance indices and classification, there have been comparisons separately done between different Landsat sensors. Resulting from the differences in sensors' characterization, discrepancies were generally observable in spectral reflectance or vegetation indices [7,8,[11][12][13][14][15] and in land use classification [9,[16][17][18]. Nevertheless, there has been no detailed investigation simultaneously on the consistency issue of all Landsat sensors.…”