Research in interactive scene analysis

Cooperative (man-machine) scene analysis techniques were developed whereby humans can provide a computer with guidance when completely automated processing is infeasible. An interactive approach promises significant near-term payoffs in analyzing various types of high volume satellite imagery, as well as vehicle-based imagery used in robot planetary exploration. This report summarizes the work accomplished over the duration of the project and describes in detail three major accomplishments: (1) the interactive design of texture classifiers; (2) a new approach for integrating the segmentation and interpretation phases of scene analysis; and (3) the application of interactive scene analysis techniques to cartography

New tools for the classification and filtering of historical maps

6openInternationalBothHistorical maps constitute an essential information for investigating the ecological and landscape features of a region over time. The integration of heritage maps in GIS models requires their digitalization and classification. This paper presents a semi-automatic procedure for the digitalization of heritage maps and the successive filtering of undesirable features such as text, symbols and boundary lines. The digitalization step is carried out using Object-based Image Analysis (OBIA) in GRASS GIS and R, combining image segmentation and machine-learning classification. The filtering step is performed by two GRASS GIS modules developed during this study and made available as GRASS GIS add-ons. The first module evaluates the size of the filter window needed for the removal of text, symbols and lines; the second module replaces the values of pixels of the category to be removed with values of the surrounding pixels. The procedure has been tested on three maps with different characteristics, the “Historical Cadaster Map for the Province of Trento” (1859), the “Italian Kingdom Forest Map” (1926) and the “Map of the potential limit of the forest in Trentino” (1992), with an average classification accuracy of 97%. These results improve the performance of classification of heritage maps compared to more classical methods, making the proposed procedure that can be applied to heterogeneous sets of maps, a viable approachopenGobbi, Stefano; Ciolli, Marco; La Porta, Nicola; Rocchini, Duccio; Tattoni, Clara; Zatelli, PaoloGobbi, S.; Ciolli, M.; La Porta, N.; Rocchini, D.; Tattoni, C.; Zatelli, P

Remote Sensing for Land Administration 2.0

The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information

Fine spatial scale modelling of Trentino past forest landscape and future change scenarios to study ecosystem services through the years

Ciolli, MarcoCantiani, Maria Giulia1openLandscape in Europe has dramatically changed in the last decades. This has been especially true for Alpine regions, where the progressive urbanization of the valleys has been accom- panied by the abandonment of smaller villages and areas at higher elevation. This trend has been clearly observable in the Provincia Autonoma di Trento (PAT) region in the Italian Alps. The impact has been substantial for many rural areas, with the progressive shrinking of meadows and pastures due to the forest natural recolonization. These modifications of the landscape affect biodiversity, social and cultural dynamics, including landscape perception and some ecosystem services. Literature review showed that this topic has been addressed by several authors across the Alps, but their researches are limited in space coverage, spatial resolution and time span. This thesis aims to create a comprehensive dataset of historical maps and multitemporal orthophotos in the area of PAT to perform data analysis to identify the changes in forest and open areas, being an evaluation of how these changes affected land- scape structure and ecosystems, create a future change scenario for a test area and highlight some major changes in ecosystem services through time. In this study a high resolution dataset of maps covering the whole PAT area for over a century was developed. The earlier representation of the PAT territory which contained reliable data about forest coverage was considered is the Historic Cadastral maps of the 1859. These maps in fact systematically and accurately represented the land use of each parcel in the Habsburg Empire, included the PAT. Then, the Italian Kingdom Forest Maps, was the next important source of information about the forest coverage after World War I, before coming to the most recent datasets of the greyscale images of 1954, 1994 and the multiband images of 2006 and 2015. The purpose of the dataset development is twofold: to create a series of maps describing the forest and open areas coverage in the last 160 years for the whole PAT on one hand and to setup and test procedures to extract the relevant information from imagery and historical maps on the other. The datasets were archived, processed and analysed using the Free and Open Source Software (FOSS) GIS GRASS, QGIS and R. The goal set by this work was achieved by a remote sensed analysis of said maps and aerial imagery. A series of procedures were applied to extract a land use map, with the forest categories reaching a level of detail rarely achieved for a study area of such an extension (6200 km2 ). The resolution of the original maps is in fact at a meter level, whereas the coarser resampling adopted is 10mx10m pixels. The great variety and size of the input data required the development, along the main part of the research, of a series of new tools for automatizing the analysis of the aerial imagery, to reduce the user intervention. New tools for historic map classification were as well developed, for eliminating from the resulting maps of land use from symbols (e.g.: signs), thus enhancing the results. Once the multitemporal forest maps were obtained, the second phase of the current work was a qualitative and quantitative assessment of the forest coverage and how it changed. This was performed by the evaluation of a number of landscape metrics, indexes used to quantify the compaction or the rarefaction of the forest areas. A recurring issue in the current Literature on the topic of landscape metrics was identified along their analysis in the current work, that was extensively studied. This highlighted the importance of specifying some parameters in the most used landscape fragmentation analy- sis software to make the results of different studies properly comparable. Within this analysis a set of data coming from other maps were used to characterize the process of afforestation in PAT, such as the potential forest maps, which were used to quantify the area of potential forest which were actually afforested through the years, the Digital Ele- vation Model, which was used to quantify the changes in forest area at a different ranges of altitude, and finally the forest class map, which was used to estimate how afforestation has affected each single forest type. The output forest maps were used to analyse and estimate some ecosystem services, in par- ticular the protection from soil erosion, the changes in biodiversity and the landscape of the forests. Finally, a procedure for the analysis of future changes scenarios was set up to study how afforestation will proceed in absence of external factors in a protected area of PAT. The pro- cedure was developed using Agent Based Models, which considers trees as thinking agents, able to choose where to expand the forest area. The first part of the results achieved consists in a temporal series of maps representing the situation of the forest in each year of the considered dataset. The analysis of these maps suggests a trend of afforestation across the PAT territory. The forest maps were then reclassi- fied by altitude ranges and forest types to show how the afforestation proceeded at different altitudes and forest types. The results showed that forest expansion acted homogeneously through different altitude and forest types. The analysis of a selected set of landscape met- rics showed a progressive compaction of the forests at the expenses of the open areas, in each altitude range and for each forest type. This generated on one hand a benefit for all those ecosystem services linked to a high forest cover, while reduced ecotonal habitats and affected biodiversity distribution and quality. Finally the ABM procedure resulted in a set of maps representing a possible evolution of the forest in an area of PAT, which represented a similar situation respect to other simulations developed using different models in the same area. A second part of the result achieved in the current work consisted in new open source tools for image analysis developed for achieving the results showed, but with a potentially wider field of application, along with new procedure for the evaluation of the image classification. The current work fulfilled its aims, while providing in the meantime new tools and enhance- ment of existing tools for remote sensing and leaving as heritage a large dataset that will be used to deepen he knowledge of the territory of PAT, and, more widely to study emerging pattern in afforestation in an alpine environment.openGobbi, S

Modeling of forest landscape evolution at regional level: a FOSS4G approach

In the last decades the Alpine landscape has dramatically changed due to social and economic factors. The most visible impact has been the reduction of the population for mid and high altitude villages and the shrinking of the part of the land used for agriculture and grazing, with a progressive reduction of pastures and meadows and the expansion of the forested areas. For these reasons, a dataset describing the forest, meadows and pasture coverage for the Trentino region, in the eastern Italian Alps, has been created. A set of heterogeneous sources has been selected so that maps and images cover the longest possible time span on the whole Trentino region with comparable quality, creating a Land Use/Land Cover (LULC) map based on historical maps from 1859 to 1936 and aerial images from 1954 to 2015. The achieved accuracy ranges from 98% for historical maps to 94% for aereal imagery. The analysis of selected landscape metrics provided preliminary results about the forest distribution and patterns of recolonization during the last 155 years. It has been possible to create future scenarios for the forest evolution for the next 85 years. Given the large number of maps involved, the great flexibility provided by FOSS for spatial analysis, such as GRASS, R, QGIS and GAMA and the possibility of scripting all the operations have played a pivotal role in the success both in the creation of the dataset and in the extraction and modeling of land use change

Automated Crop Field Extraction from Multi-Temporal Web Enabled Landsat Data

An automated computational methodology to extract agricultural crop fields from 30 m Web Enabled Landsat data (WELD) time series is presented. The results for three 150 × 150 kmWELD tiles encompassing rectangular, circular (center-pivot irrigation) and irregularly shaped fields in Texas, California and South Dakota are presented and compared to independent United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) cropland data layer (CDL) classifications. Coherent fields that are visually apparent were extracted with relatively limited apparent errors of omission or commission compared to the CDL classifications. This is due to several factors. First, the use ofmulti-temporal Landsat data, as opposed to single Landsat acquisitions, that enables crop rotations and inter-annual variability in the state of the vegetation to be accommodated for and provides more opportunities for cloud-free, non-missing and atmospherically uncontaminated surface observations. Second, the adoption of an object-based approach, namely the variational region-based geometric active contour method that enables robust segmentation with only a small number of parameters and that requires no training data. Third, the use of a watershed algorithmto decompose connected segments belonging to multiple fields into coherent isolated field segments and a geometry-based algorithm to detect and associate parts of circular fields together. A preliminary validation is presented to gain quantitative insights into the field extraction accuracy and to prototype a validation protocol including new geometric measures that quantify the accuracy of individual field objects. Implications and recommendations for future research and large-area applications are discussed