Deforestation by Afforestation: Land Use Change in the Coastal Range of Chile

In southern Chile, an establishment of a plantation-based forest industry occurred early in the industrial era. Forest companies claim that plantations were established on eroded lands. However, the plantation industry is under suspicion to have expanded its activities by clearing near-natural forests since the early 1970s. This paper uses a methodologically complex classification approach from own previously published research to elucidate land use dynamics in southern Chile. It uses spatial data (extended morphological profiles) in addition to spectral data from historical Landsat imagery, which are fusioned by kernel composition and then classified in a multiple classifier system (based on support, import and relevance vector machines). In a large study area (~67,000 km2), land use change is investigated in a narrow time frame (five-year steps from 1975 to 2010) in a two-way (prospective and retrospective) analysis. The results are discussed synoptically with other results on Chile. Two conclusions can be drawn for the coastal range. Near-natural forests have always been felled primarily in favor of the plantation industry. Vice versa, industrial plantations have always been primarily established on sites, that were formerly forest covered. This refutes the claim that Chilean plantations were established primarily to restore eroded lands; also known as badlands. The article further shows that Chile is not an isolated case of deforestation by afforestation, which has occurred in other countries alike. Based on the findings, it raises the question of the extent to which the Chilean example could be replicated in other countries through afforestation by market economy and climate change mitigation

Intensive development of New Zealand’s indigenous grasslands: Rates of change, assessments of vulnerability and priorities for protection

Research was undertaken in the indigenous tussock grasslands of South Island of New Zealand in order to quantify past rates of conversion to agricultural land use and to develop vulnerability models to predict future conversion spatially and temporally. The study area was delineated using the median spectral reflectance of indigenous grasslands and included the largest extent of unprotected contiguous grasslands concentrated in the central South Island. Conversion from indigenous grasslands to a non-indigenous cover was quantified by comparative mapping over three intervals (1840-1990, 1990-2001, and 2001-2008). The basic premise in using satellite imagery to detect changes in land-use/cover is that these are revealed by changes in spectral signature. However, New Zealand’s indigenous and non-indigenous grasslands have overlapping spectral trajectories and high inter-annual variability, therefore contextual information was needed in order accurately map conversion from indigenous grassland cover to exotic pasture. Within the study area around the time of European settlement (1840) there were approximately 3.3 million hectares of indigenous grasslands. Between 1840 and 1990 around 1 million hectares of indigenous grasslands were converted to a non-indigenous cover. The extent of conversion during the preceding time period (1990-2008) was approximately 71,261 ha, of which 72% was converted to pasture and cropland and the remaining 28% to mining, urban settlements and exotic forestry. Although the overall rate of grassland conversion decreased relative to the period of European settlement and 1990, the proportion of remaining indigenous grasslands converted each year increased. Almost two-thirds of post-1990 conversion has occurred in environments with less than 30% indigenous cover remaining, and much is in land classified as non-arable with moderate to extreme limitations to crop, pasture and forestry growth. To assess the relative vulnerability of remaining areas of indigenous grassland to intensive land use (mainly intensive pasture production but also exotic conifer plantations, urban use and mining), spatial predictions using Generalized Additive Models (GAMs) were used to establish relationships between two different types of dependent (response) variables (presence or absence of conversion) and potential environmental and proxy socio-economic explanatory variables. The chosen predictors for the final model were used to map conversion probabilities in geographic space. The selected GAMs showed the mean probability of conversion in remaining indigenous grasslands was 0.15 and the mean area of conversion was 116 ha. Habitat that was most vulnerable to conversion was at moderate elevations and on medium slopes, and had previously been classified as being of low suitability for production. To interpret the regression models, plots of the partial response curves resulting from the model, and overall contributions of variables to the model, were used. The most important explanatory variables for predicting the probability of conversion in order of ‘alone contribution (the potential for each variable alone to explain conversion) was slope, rainfall, land tenure, distance to roads, proximity to existing agricultural, regional council, and mean annual temperature. Interpretation of the GAMs showed that conversion was negatively related to: slope, rainfall and distance roads; positively related to mean annual temperature; higher in the Otago and Canterbury regions and on privately owned or recently privatized lands, and peaked at intermediate proximity to roads. The prediction of the probability of conversion model was cross-validated both spatially and temporally. Temporal cross-validation compared predicted probabilities of conversion against reference maps of observed ‘current’ conversion. Spatial cross-validation evaluated model discrimination between ‘converted’ and ‘not converted’. Temporal and spatial performance was measured using the Receiver Operating Characteristic (ROC), a graphical plot of the true positive rate (sensitivity) as a function of the false positive (1-specificity) for different probability thresholds. For temporal cross-validation there was high correlation between ‘predicted’ and ‘observed’ (ROC = 0.913), and for spatial validation the relationship between the fitted and observed was also high (ROC= 0.921), indicating there was good discrimination between ‘converted’ and ‘not converted’. Integrating validated estimates of the probability of conversion (vulnerability) into conservation planning tools is an important component of conservation planning. Comparison of conservation prioritisation outputs with validated estimates of vulnerability of New Zealand’s indigenous grasslands showed variable effectiveness of vulnerability surrogates; one surrogate performed most poorly where vulnerability of grasslands to conversion was greatest and realized probability of protection was lowest. Furthermore, estimates of vulnerability using surrogates underestimated vulnerability on flat land that was closer to roads and overestimated areas on steeper land that was topographically invulnerable to conversion. There is an increased disparity between patterns of protection and patterns of conversion indicating that existing conservation planning tools are not effectively targeting the most vulnerable areas of remaining indigenous grasslands. An up-to-date validated vulnerability assessment offered a practical and a responsive technical bridge for the gap between science and implementation. This approach can be applied more widely to provide national models of vulnerability from representative samples of conversion

Earth resources: A continuing bibliography with indexes, issue 50

This bibliography lists 523 reports, articles and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

Multi-Risk Climate Mapping for the Adaptation of the Venice Metropolitan Area

Climate change risk reduction requires cities to undertake urgent decisions. One of the principal obstacles that hinders effective decision making is insufficient spatial knowledge frameworks. Cities climate adaptation planning must become strategic to rethink and transform urban fabrics holistically. Contemporary urban planning should merge future threats with older and unsolved criticalities, like social inequities, urban conflicts and \u201cdrosscapes\u201d. Retrofitting planning processes and redefining urban objectives requires the development of innovative spatial information frameworks. This paper proposes a combination of approaches to overcome knowledge production limits and to support climate adaptation planning. The research was undertaken in collaboration with the Metropolitan City of Venice and the Municipality of Venice, and required the production of a multi-risk climate atlas to support their future spatial planning efforts. The developed tool is a Spatial Decision Support System (SDSS), which aids adaptation actions and the coordination of strategies. The model recognises and assesses two climate impacts: Urban Heat Island and Flooding, representing the Metropolitan City of Venice (CMVE) as a case study in complexity. The model is composed from multiple assessment methodologies and maps both vulnerability and risk. The atlas links the morphological and functional conditions of urban fabrics and land use that triggers climate impacts. The atlas takes the exposure assessment of urban assets into account, using this parameter to describe local economies and social services, and map the uneven distribution of impacts. The resulting tool is therefore a replicable and scalable mapping assessment able to mediate between metropolitan and local level planning systems

Multi-Scale Remote Sensing of Tornado Effects

To achieve risk-based engineered structural designs that provide safety for life and property from tornadoes, sufficient knowledge of tornado wind speeds and wind flow characteristics is needed. Currently, sufficient understanding of the magnitude, frequency, and velocity structure of tornado winds remain elusive. Direct measurements of tornado winds are rare and nearly impossible to acquire, and the pursuit of in situ wind measurements can be precarious, dangerous, and even necessitating the development of safer and more reliable means to understand tornado actions. Remote-sensing technologies including satellite, aerial, lidar, and photogrammetric platforms, have demonstrated an ever-increasing efficiency for collecting, storing, organizing, and communicating tornado hazards information at a multitude of geospatial scales. Current remote-sensing technologies enable wind-engineering researchers to examine tornado effects on the built environment at various spatial scales ranging from the overall path to the neighborhood, building, and ultimately member and/or connection level. Each spatial resolution contains a unique set of challenges for efficiency, ease, and cost of data acquisition and dissemination, as well as contributions to the body of knowledge that help engineers and atmospheric scientists better understand tornado wind speeds. This paper examines the use of remote sensing technologies at four scales in recent tornado investigations, demonstrating the challenges of data collection and processing at each level as well as the utility of the information gleaned from each level in advancing the understanding of tornado effects

Development of inventory datasets through remote sensing and direct observation data for earthquake loss estimation

This report summarizes the lessons learnt in extracting exposure information for the three study sites, Thessaloniki, Vienna and Messina that were addressed in SYNER-G. Fine scale information on exposed elements that for SYNER-G include buildings, civil engineering works and population, is one of the variables used to quantify risk. Collecting data and creating exposure inventories is a very time-demanding job and all possible data-gathering techniques should be used to address the data shortcoming problem. This report focuses on combining direct observation and remote sensing data for the development of exposure models for seismic risk assessment. In this report a summary of the methods for collecting, processing and archiving inventory datasets is provided in Chapter 2. Chapter 3 deals with the integration of different data sources for optimum inventory datasets, whilst Chapters 4, 5 and 6 provide some case studies where combinations between direct observation and remote sensing have been used. The cities of Vienna (Austria), Thessaloniki (Greece) and Messina (Italy) have been chosen to test the proposed approaches.JRC.G.5-European laboratory for structural assessmen