Abstract
This paper presents a method for employing satellite data to evaluate spatial and temporal patterns in environmental indices of interest. In the first step, linear regression coefficients are extracted for each area in the image. These coefficients are then employed as a response variable in a boosted regression tree with geographic coordinates as explanatory variables. Here, a two-step approach is described in the context of a substantive case study comprising 30 years of satellite derived fractional green vegetation cover for a large region in Queensland, Australia. In addition to analysis of the entire image and timeframe, separate analyses are undertaken over decades and over sub-regions of the study region. The results demonstrate both the utility of the approach and insights into spatio-temporal trends in green vegetation for this site. These findings support the feasibility of using the proposed two-step approach and geographic coordinates in the analysis of satellite derived indices over space and time.
Highlights
Sensed data are available from a wide range of sources, ranging from satellites to drones, and have been used for a very wide range of environmental applications and analysis of spatial and temporal trends
We evaluated the performance of a popular machine learning technique, namely Boosted Regression Tree (BRT), and concluded that it can perform well in high-dimensional and complex problems, deal with missing data by default without the need for interpolation/infilling, describe complex nonlinearity and interactions between variables, deal with spatial and non-spatial data and different data granularities, and reduce data complexity without negatively affecting prediction performance [20]
Data Pre-Processing In Colin et al [12], we investigated spatial aggregation schemes that are best suited for this study site with regard to up-scaling Fractional cover (FCover) data and maintaining sufficient local characteristics for accurate prediction of green vegetation
Summary
Sensed data are available from a wide range of sources, ranging from satellites to drones, and have been used for a very wide range of environmental applications and analysis of spatial and temporal trends. Landsat data are freely available [1]; the imagery covers a wide geographical area, and it avoids expensive, extensive and often impractical in situ measurement. There is a strong advantage in using remotely sensed Landsat imagery for land use and land cover (LULC) analyses in detecting and estimating the magnitude of spatio-temporal trends in measures of the quantity of green vegetation [2,3]. Monitoring long-term trends of green vegetation in a semi-arid region gives valuable insight into dependencies and changing quantities influenced by climate variability. A satellite pixel combines the reflected radiation from different objects on the earth surface, and this spectral mixing effect results in a so called mixed pixel, or Mixel [5]
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