Remote sensing-based inventories are essential in estimating forest cover in tropical and subtropical countries, where ground inventories cannot be performed periodically at a large scale owing to high costs and forest inaccessibility (e.g. REDD projects) and are mandatory for constructing historical records that can be used as forest cover baselines. Given the conditions of such inventories, the survey area is partitioned into a grid of imagery segments of pre-fixed size where the proportion of forest cover can be measured within segments using a combination of unsupervised (automated or semi-automated) classification of satellite imagery and manual (i.e. visual on-screen) enhancements. Because visual on-screen operations are time expensive procedures, manual classification can be performed only for a sample of imagery segments selected at a first stage, while forest cover within each selected segment is estimated at a second stage from a sample of pixels selected within the segment. Because forest cover data arising from unsupervised satellite imagery classification may be freely available (e.g. Landsat imagery) over the entire survey area (wall-to-wall data) and are likely to be good proxies of manually classified cover data (sample data), they can be adopted as suitable auxiliary information. The question is how to choose the sample areas where manual classification is carried out. We have investigated the efficiency of one-per-stratum stratified sampling for selecting segments and pixels, where to carry out manual classification and to determine the efficiency of the difference estimator for exploiting auxiliary information at the estimation level. The performance of this strategy is compared with simple random sampling without replacement. Our results were obtained theoretically from three artificial populations constructed from the Landsat classification (forest/non forest) available at pixel level for a study area located in central Italy, assuming three levels of error rates of the unsupervised classification of satellite imagery. The exploitation of map data as auxiliary information in the difference estimator proves to be highly effective with respect to the Horvitz-Thompson estimator, in which no auxiliary information is exploited. The use of one-per-stratum stratified sampling provides relevant improvement with respect to the use of simple random sampling without replacement. The use of one-per-stratum stratified sampling with many imagery segments selected at the first stage and few pixels within at the second stage - jointly with a difference estimator - proves to be a suitable strategy to estimate forest cover by remote sensing-based inventories.
Abstract Mountains are strongly seasonal habitats, which require special adaptations in wildlife species living on them. Population dynamics of mountain ungulates are largely determined by the availability of rich food resources to sustain lactation and weaning during summer. Increases of temperature affect plant phenology and nutritional quality. Cold-adapted plants occurring at lower elevations will shift to higher ones, if available. We predicted what could happen to populations of mountain ungulates based on how climate change could alter the distribution pattern and quality of high-elevation vegetation, using the “clover community-Apennine chamois Rupicapra pyrenaica ornata ” system. From 1970 to 2014, increasing spring temperatures (2 °C) in our study area led to an earlier (25 days) onset of green-up in Alpine grasslands between 1700 and 2000 m, but not higher up. For 1970–2070, we have projected trends of juvenile winter survival of chamois, by simulating trajectories of spring temperatures and occurrence of clover, through models depicting four different scenarios. All scenarios have suggested a decline of Apennine chamois in its historical core range, during the next 50 years, from about 28% to near-extinction at about 95%. The negative consequences of climate changes presently occurring at lower elevations will shift to higher ones in the future. Their effects will vary with the species-specific ecological and behavioural flexibility of mountain ungulates, as well as with availability of climate refugia. However, global shifts in distributional ranges and local decreases or extinctions should be expected, calling for farsighted measures of adaptive management of mountain-dwelling herbivores.
Forest area in the year 1990 is a figure of great interest under the Kyoto Protocol. This note is devoted to a scientific exercise for the probabilistic ex post assessment of such a figure in Italy. Estimation was performed by two-phase point sampling, which made use of historical remotely sensed imagery. In the first phase, a sample of 12089 points was selected according to an unaligned systematic sampling and the selected points were classified in land-use categories by Landsat 5 TM imagery. In the second phase, a sample of 3000 points was selected by stratified sampling in which the strata were determined by the satellite classification and the selected points were classified by aerial photos, assumed as ground truth. A two-phase estimate of land-use coverage partitioning the Italian territory was obtained together with a conservative estimate of the sampling variance-covariance. The procedure has proved to be of relatively easy implementation and objective repeatability.
