Nonlocal Multiscale Single Image Statistics From Sentinel-1 SAR Data for High Resolution Bitemporal Forest Wind Damage Detection

Change detection of synthetic aperture radar (SAR) data is a challenge for high-resolution applications. This study presents a new nonlocal averaging approach (STAl'SAR) to reduce the speckle of single Sentinel-1 SAR images and statistical parameters derived from the image. The similarity of SAR pixels is based on the statistics of 3 x 3 window as represented by the mean, standard deviation, median, minimum, and maximum. K-means clustering is used to divide the SAR image in 30 similarity clusters. The nonlocal averaging is carried out within each cluster separately in magnitude order of the 3 x 3 window averages. The nonlocal filtering is applicable not only to the original pixel backscattering values but also to statistical parameters, such as standard deviation. The statistical parameters to be filtered can represent any window size, according to the need of the application. The nonlocally averaged standard deviation derived in two spatial resolutions, 3 x 3 and 7 x 7 windows, are demonstrated here for improving the resolution in which the forest damages can be detected using the VH polarized backscattering spatial variation change.Peer reviewe

Comparing conservation value maps and mapping methods in a rural landscape in southern Finland

Peer reviewe

A constructive review of the State Forest Inventory in the Russian Federation

The State Forest Inventory (SFI) in the Russian Federation is a relatively new project that is little known in the English-language scientific literature. Following the stipulations of the Forest Act of 2006, the first SFI sample plots in this vast territory were established in 2007. The 34 Russian forest regions were the basic geographical units for all statistical estimates and served as a first-level stratification, while a second level was based on old inventory data and remotely sensed data. The sampling design was to consist of a simple random sample of 84,700 circular 500m(2) sample plots over forest land. Each sample plot consists of three nested concentric circular subplots with radii of 12.62, 5.64 and 2.82m and additional subplots for assessing and describing undergrowth, regeneration and ground vegetation. In total, 117 variables were to be measured or assessed on each plot.Although field work has begun, the methodology has elicited some criticism. The simple random sampling design is less efficient than a systematic design featuring sample plot clusters and a mix of temporary and permanent plots. The second-level stratification is mostly ineffective for increasing precision. Qualitative variables, which are not always essential, are dominant, while important quantitative variables are under-represented. Because of very slow progress, in 2018 the original plan was adjusted by reducing the number of permanent sample plots from 84,700 to 68,287 so that the first SFI cycle could be completed by 2020.Peer reviewe

Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories

For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed.For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed.Peer reviewe

Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods-A Case Study from Dak Nong, Vietnam

Information on land use and land cover (LULC) including forest cover is important for the development of strategies for land planning and management. Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to produce estimates with a greater degree of confidence than traditional inventory estimates. However, use of these data has always been a challenge in tropical regions owing to the complexity of the biophysical environment, clouds, and haze, and atmospheric moisture content, all of which impede accurate LULC classification. We tested a parametric classifier (logistic regression) and three non-parametric machine learning classifiers (improved k-nearest neighbors, random forests, and support vector machine) for classification of multi-temporal Sentinel 2 satellite imagery into LULC categories in Dak Nong province, Vietnam. A total of 446 images, 235 from the year 2017 and 211 from the year 2018, were pre-processed to gain high quality images for mapping LULC in the 6516 km(2) study area. The Sentinel 2 images were tested and classified separately for four temporal periods: (i) dry season, (ii) rainy season, (iii) the entirety of the year 2017, and (iv) the combination of dry and rainy seasons. Eleven different LULC classes were discriminated of which five were forest classes. For each combination of temporal image set and classifier, a confusion matrix was constructed using independent reference data and pixel classifications, and the area on the ground of each class was estimated. For overall temporal periods and classifiers, overall accuracy ranged from 63.9% to 80.3%, and the Kappa coefficient ranged from 0.611 to 0.813. Area estimates for individual classes ranged from 70 km(2) (1% of the study area) to 2200 km(2) (34% of the study area) with greater uncertainties for smaller classes.Peer reviewe

A century of national forest inventories - informing past, present and future decisions

In 2019, 100 years had elapsed since the first National Forest Inventory (NFI) was established in Norway. Motivated by a fear of over-exploitation of timber resources, NFIs today enable informed policy making by providing data vital to decision support at international, national, regional, and local scales. This Collection of articles celebrates the 100th anniversary of NFIs with a description of past, present, and future research aiming at improving the monitoring of forest and other terrestrial ecosystems.Non peer reviewe

Outlining multi-purpose forest inventories to assess the ecosystem approach in forestry

A summary and discussion of selected published results on the current and potential role of forest inventories (with particular reference to the national ones) are presented in the light of the challenges posed by society and policy decisions in the environmental sector. The analysis concentrates mainly on the ecological and socio-economic aspects of the question and on forest inventories’ potential contribution to achieving sustainable forest management.L'articolo è diponibile sul sito dell'editore wwww.tandf.co.uk/journals