Refining forest classifications in the western Amazon using an intra‐annual multitemporal approach

Abstract

Seasonal dynamics in the north-eastern Peruvian Amazon were assessed within a multitemporal LULC framework informed by Landsat 7 ETM + imagery of the study area for 2001 that coincided with seasonal flooding dynamics. Three images (12 March, 31 May, and 20 September 2001) were classified separately using a hybrid classification method that combined unsupervised and supervised techniques, and attributed with a classification scheme consisting of 18 LULC classes. A multitemporal classification that included 25 LULC classes was created from the three single-date classifications using a panel analysis technique. While some of the classes describe LULC 'changes' (land-use and land-cover change (LULCC)) that were stable over time (e.g. low sediment water during March, May, and September 2001), others were complex and included multiple trajectories of change. Panel analysis extracts pixel histories of change over three or more observations as a trend or trajectory, rather than segmenting those changes into piecemeal periods as normally done with from-to change detection. This technique was then assessed by testing hypothesized forest trajectories of LULCC. Traditional quantitative accuracy assessment techniques are less appropriate for panel analysis, and so a qualitative accuracy assessment was performed to evaluate the validity of the multitemporal classification. This study suggests that there may not be one typical year-round behaviour for seasonal environments, and that population-environment interaction studies would benefit from incorporating this knowledge into future research. This analysis further demonstrates the effectiveness of a multitemporal remote-sensing approach for gauging landscape fluctuations in seasonal environments.

Object-Based Image Analysis in Wetland Research: A Review

“…Finally, a special challenge should be noted for wetlands with high magnitude of short-term variation, where between-date changes may represent both long-term transitions and short-term effects of plant phenology and disturbance [45,53,74,107]. Investigating long-term shifts in such systems requires disentangling surface trends from transient phenomena, and in case of small isolated wetlands-accounting for seasonal hydrological processes that are not entirely "visible" to remote sensors [16,86].…”

Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification

“…As noted earlier, pixel speckle may increase variation in class trajectories and obscure meaningful transitions (McCleary et al, 2008). This variation was addressed by integrating pixel values within small objects assumed to be spatially invariant multi-temporal DCT units during one flood cycle.…”

“…Complex surface composition and dynamics may result in a large number of detected unique change pathways, some of which may not be physically plausible (Hess et al, 2003;Liu & Cai, 2011;Villa, Boschetti, Morse and Politte, 2012), representing error and noise (McCleary et al, 2008). This issue may be addressed by using ancillary information in class definition, transforming the images to highlight relevant patterns (Neeti & Eastman, 2014) and by reducing pixellevel local heterogeneity using primitive objects as mapping units (Chen, Hay, Carvalho and Wulder, 2012).…”

“…Such analysis may be implemented by comparing inter-and intra-annual land cover changes as a panel approach described by Crews-Meyer (2008) and McCleary, Crews-Meyer and Young (2008), or by fitting regression to long-term data series and analyzing residuals for signals of non-recurring disturbance and unusual events (Neuenschwander & Crews, 2008).…”

Forefronting the Socio-Ecological in Savanna Landscapes through Their Spatial and Temporal Contingencies

“…Multi-temporal analysis from within one calendar year (intra-annual) could be used to characterize the size and distribution of land classes changing due to seasonality. Those factors could originate from biophysical and/or policy regimes (including hunting, land tenure, and conservation policy), while seasonal shifts could be due to cyclic factors, such as phenology of vegetation [143][144][145][146] and/or migration for seasonal employment [54,147]. The degree of change due to yearly (inter-annual) seasonality could be compared to temporal change over intra-decadal cycles (e.g., via panel analysis as in [16,54] or via harmonic regression and wavelet analysis [144]).…”