Optimal land cover mapping and change analysis in northeastern oregon using landsat imagery.

Abstract The necessity for the development of repeatable, efficient, and accurate monitoring of land cover change is paramount to successful management of our planet’s natural resources. This study evaluated a number of remote sensing methods for classifying land cover and land cover change throughout a two-county area in northeastern Oregon (1986 to 2011). In the past three decades, this region has seen significant changes in forest management that have affected land use and land cover. This study employed an accuracy assessment-based empirical approach to test the optimality of a number of advanced digital image processing techniques that have recently emerged in the field of remote sensing. The accuracies are assessed using traditional error matrices, calculated using reference data obtained in the field. We found that, for single-time land cover classification, Bayes pixel-based classification using samples created with scale and shape segmentation parameters of 8 and 0.3, respectively, resulted in the highest overall accuracy. For land cover change detection, using Landsat-5 TM band 7 with a change threshold of 1.75 standard deviations resulted in the highest accuracy for forest harvesting and regeneration mapping

Mapping and analysis of changes in the riparian landscape structure of the Lockyer Valley Catchment, Queensland, Australia

[Abstract]: A case study of the Lockyer Valley catchment in Queensland, Australia, was conducted to develop appropriate mapping and assessment techniques to quantify the nature and magnitude of riparian landscape structural changes within a catchment. The study employed digital image processing techniques to produce land cover maps from the 1973 and 1997 Landsat imagery. Fixed and variable width buffering of streams were implemented using a geographic information system (GIS) to estimate the riparian zone and to subsequently calculate the landscape patterns using the Patch Analyst (Grid) program (a FRAGSTATS interface). The nature of vegetation clearing was characterised based on land tenure, slope and stream order. Using the Pearson chi-square test and Cramer’s V statistic, the relationships between the vegetation clearing and land tenure were further assessed. The results show the significant decrease in woody vegetation areas mainly due to conversion to pasture. Riparian vegetation corridors have become more fragmented, isolated and of much smaller patches. Land tenure was found to be significantly associated with the vegetation clearing, although the strength of association was weak. The large proportion of deforested riparian zones within steep slopes or first-order streams raises serious questions about the catchment health and the longer term potential for land degradation by upland clearing. This study highlights the use of satellite imagery and geographic information systems in mapping and analysis of landscape structural change, as well as the identification of key issues related to sensor spatial resolution, stream buffering widths, and the quantification of land transformation processes

Research in geosciences policy

Various topics related to cases of difficult adaptation to global change are discussed. Topics include patterns in the ratification of global environmental treaties, the effects of global climate change on Southeast Asia, and global change and biodiversity loss

Crown-level mapping of tree species and health from remote sensing of rural and urban forests

Tree species composition and health are key attributes for rural and urban forest biodiversity, and ecosystem services preservation. Remote sensing has facilitated extraordinary advances in estimating and mapping tree species composition and health. Yet previous sensors and algorithms were largely unable to resolve individual tree crowns and discriminate tree species or health classes at this essential spatial scale due to the low image spectral and spatial resolution. However, current available very high spatial resolution (VHR) remote sensing data can begin to resolve individual tree crowns and measure their spectral and structural qualities with unprecedented precision. Moreover, various machine learning algorithms are now available to apply these new data sources toward the discrimination and the mapping of tree species and health classes. The dissertation includes an introductory chapter, three stand-alone manuscripts, and a concluding chapter, each of which support the overarching theme of mapping tree species composition and health using remote sensing images. The first manuscript, now published in the International Journal of Remote Sensing, confirms the utility of combining VHR multi-temporal satellite data with LiDAR datasets for tree species classification using machine learning classifiers at the crown level in a rural forest the Fernow Experimental Forest, West Virginia. This research also evaluates the contribution of each type of spectral, phenological and structural feature for discriminating four tree species: red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina). The second manuscript investigates the performance of tree species classification in urban settings with three contributions: 1) 12 very high resolution WorldView-3 images (WV-3), whose image acquisition date covering the growing season from April to November; 2) a large forest inventory providing sufficient calibration/validation datasets in Washington D.C.; 3) object-based tree species classification using the RandomForest machine learning algorithm. This manuscript identifies the incremental losses in classification accuracy caused by iteratively expanding the classification to 19 species and 10 genera. It also identifies the optimum pheno-phases and spectral bands for discriminating trees species in urban settings. Building on these promising results from the second manuscript, the third manuscript detect a signal of statistical difference among individual tree health conditions using WorldView-3 images from June 11th, July 30th and August 30th , 2017 in Washington D.C.. It examines six vegetation indices calculated from WorldView-3 images to describe three health condition levels in good, fair and poor, and discusses the effects of green-down phenology for tree health analysis. Overall, this dissertation research contributes to remote sensing research by combining data from both active and passive sensors to discriminate tree species in rural forest. For the species-rich urban settings, this dissertation illustrates the importance of phenology for tree species classification at crown level using VHR remote sensing images. Finally, this dissertation provides important insights on detecting statistical differences among tree health conditions at individual crown-level in the urban environment using VHR remote sensing images

Deep Learning Solutions for TanDEM-X-based Forest Classification

In the last few years, deep learning (DL) has been successfully and massively employed in computer vision for discriminative tasks, such as image classification or object detection. This kind of problems are core to many remote sensing (RS) applications as well, though with domain-specific peculiarities. Therefore, there is a growing interest on the use of DL methods for RS tasks. Here, we consider the forest/non-forest classification problem with TanDEM-X data, and test two state-of-the-art DL models, suitably adapting them to the specific task. Our experiments confirm the great potential of DL methods for RS applications

Parks, people and pixels: evaluating landscape effects of an East African national park on its surroundings

Landscapes surrounding protected areas, while still containing considerable biodiversity, have rapidly growing human populations and associated agricultural development in most of the developing world that tend to isolate them, potentially reducing their conservation value. Using field studies and multi-temporal Landsat imagery, we examine a forest park, Kibale National Park in western Uganda, its changes over time, and related land cover change in the surrounding landscape. We find Kibale has successfully defended its borders and prevents within-park deforestation and other land incursions, and has maintained tree cover throughout the time period of the study. Outside the park there was a significant increase in tea plantations and continued forest fragmentation and wetland loss. The question of whether the park is a conservation success because of the network of forest fragments and wetlands or in spite of them remains unanswered

Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

Improved resource use decisions and actions through remote sensing

Operational uses of remote sensing for improving management decisions and actions concerning resource uses are considered in terms of first generation, or direct-action; and second generation or indirect, delayed-action applications. From among applications completed during 1974-75, seven case studies are offered in illustration of the many contrasts which can be drawn between first and second generation application studies. These include: (1) multi-agency river basin planning; (2) corridor assessment and route location for highway location together with improvement of county-level planning decisions; (3) improving timber management practices; (4) enforcement of new state statutes; (5) county-wide open space preservation; (6) land value reappraisal relative to property tax equalization; and (7) optimizing agri-business processing plant locations