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Despite advancements in remote sensing applications for grassland management, studies following the 2011 Fukushima Daiichi nuclear disaster have often been constrained by limited satellite imagery with insufficient focus on pasture changes. Utilizing different resolutions of optical satellite data is essential for monitoring spatiotemporal changes in grasslands. High resolutions provide detailed spatial information, whereas medium-resolution satellites offer an increased frequency and wider availability over time. This study had two objectives. First, we investigated the temporal changes in a mountainous pasture in Japan from 2007 to 2022 using high-resolution data from QuickBird, WorldView-2, and SPOT-6/7, along with readily available medium-resolution data from Sentinel-2 and Landsat-5/7/8. Second, we assessed the efficacy of different satellite image resolutions in capturing these changes. Grazing ceased in the target area after the 2011 Fukushima Daiichi nuclear accident owing to radiation. We categorized the images as grasses, broadleaf trees, and conifers. The results showed a 36% decline using high-resolution satellite image analysis and 35% using Landsat image analysis in the unused pasture area since grazing suspension in 2011, transitioning primarily to broadleaf trees, and relative stabilization by 2018. Tree encroachment was prominent at the eastern site, which has a lower elevation and steeper slope facing north, east, and south. WorldView-2 consistently outperformed Landsat-8 in accuracy. Landsat-8's classification variation impedes its ability to capture subtle distinctions, particularly in zones with overlapping or neighboring land covers. However, Landsat effectively detected area reductions, similar to high-resolution satellites. Combining high- and medium-resolution satellite data leverages their respective strengths, compensates for their individual limitations, and provides a holistic perspective for analysis and decision-making. [ABSTRACT FROM AUTHOR]

Detecting field diversities via remote sensing is becoming important to monitor vegetation dynamics at large scale. The characteristics of the remotely sensed image, depending on the study organism and habitat, affect the efficiency of measuring α-and β-diversities. Therefore, we examined the impact of image resolutions and spectral plot extents on the accuracy of estimating forest α-diversities and compositional variances on the active volcano Mount Usu, northern Japan. Low- (3.2 m) and high-resolution (0.8 m) IKONOS multispectral images were used to create spectral indicators from pixels covering the field plots (narrow extent) and from pixels including neighbouring area (wide extent). Six forest diversity indices were obtained for canopy and for canopy-herb layer (total diversity): species richness (S), Shannon (H'), evenness (J'), Gini-Simpson (D), and true diversity of order 1 (N1 = expH') and order 2 (N2 = 1/D). Changes in species composition were assessed by dissimilarity matrices. The spectral diversity indicators were calculated from the combination of image resolutions and spectral plot extents, and then compared with field diversities. The low-resolution–narrow extent based spectral indicators showed the highest correlations with canopy and total diversities. The best spectral indicators were derived from the scores of the first axis of principal component analysis and from the near infrared band, reaching high correlations with both canopy and total field diversity indices. Of the six field diversities, J' showed the highest correlations with single spectral indicators, and N1, 2 showed the highest correlations with pairs of spectral indicators. The correlations between spectral and field dissimilarities were lower than the correlations between α-diversities and spectral indicators, and were unaffected by the resolution and extent. In conclusion, the best spectral indicators were obtained from the low-resolution–narrow extent combination, and the indicators estimated canopy and total field diversity indices of temperate forests equally. [ABSTRACT FROM AUTHOR]