Global Forest Monitoring from Earth Observation

Covering recent developments in satellite observation data undertaken for monitoring forest areas from global to national levels, this book highlights operational tools and systems for monitoring forest ecosystems. It also tackles the technical issues surrounding the ability to produce accurate and consistent estimates of forest area changes, which are needed to report greenhouse gas emissions and removals from land use changes. Written by leading global experts in the field, this book offers a launch point for future advances in satellite-based monitoring of global forest resources. It gives readers a deeper understanding of monitoring methods and shows how state-of-art technologies may soon provide key data for creating more balanced policies

Mapping and Monitoring Forest Cover

This book is a compilation of six papers that provide some valuable information about mapping and monitoring forest cover using remotely sensed imagery. Examples include mapping large areas of forest, evaluating forest change over time, combining remotely sensed imagery with ground inventory information, and mapping forest characteristics from very high spatial resolution data. Together, these results demonstrate effective techniques for effectively learning more about our very important forest resources

Land cover change from national to global scales:A spatiotemporal assessment of trajectories, transitions and drivers

Changes in global land cover (LC) have significant consequences for global environmental change, impacting the sustainability of biogeochemical cycles, ecosystem services, biodiversity, and food security. Different forms of LC change have taken place across the world in recent decades due to a combination of natural and anthropogenic drivers, however, the types of change and rates of change have traditionally been hard to quantify. This thesis exploits the properties of the recently released ESA-CCI-LC product – an internally consistent, high-resolution annual time-series of global LC extending from 1992 to 2018. Specifically, this thesis uses a combination of trajectories and transition maps to quantify LC changes over time at national, continental and global scales, in order to develop a deeper understanding of what, where and when significant changes in LC have taken place and relates these to natural and anthropogenic drivers. This thesis presents three analytical chapters that contribute to achieving the objectives and the overarching aim of the thesis. The first analytical chapter initially focuses on the Nile Delta region of Egypt, one of the most densely populated and rapidly urbanising regions globally, to quantify historic rates of urbanisation across the fertile agricultural land, before modelling a series of alternative futures in which these lands are largely protected from future urban expansion. The results show that 74,600 hectares of fertile agricultural land in the Nile Delta (Old Lands) was lost to urban expansion between 1992 and 2015. Furthermore, a scenario that encouraged urban expansion into the desert and adjacent to areas of existing high population density could be achieved, hence preserving large areas of fertile agricultural land within the Nile Delta. The second analytical chapter goes on to examine LC changes across sub-Saharan Africa (SSA), a complex and diverse environment, through the joint lenses of political regions and ecoregions, differentiating between natural and anthropogenic signals of change and relating to likely drivers. The results reveal key LC change processes at a range of spatial scales, and identify hotspots of LC change. The major five key LC change processes were: (i) “gain of dry forests” covered the largest extent and was distributed across the whole of SSA; (ii) “greening of deserts” found adjacent to desert areas (e.g., the Sahel belt); (iii) “loss of tree-dominated savanna” extending mainly across South-eastern Africa; (iv) “loss of shrub-dominated savanna” stretching across West Africa, and “loss of tropical rainforests” unexpectedly covering the smallest extent, mainly in the DRC, West Africa and Madagascar. The final analytical chapter considers LC change at the global scale, providing a comprehensive assessment of LC gains and losses, trajectories and transitions, including a complete assessment of associated uncertainties. This chapter highlights variability between continents and identifies locations of high LC dynamism, recognising global hotspots for sustainability challenges. At the national scale, the chapter identifies the top 10 countries with the largest percentages of forest loss and urban expansion globally. The results show that the majority of these countries have stabilised their forest losses, however, urban expansion was consistently on the rise in all countries. The thesis concludes with recommendations for future research as global LC products become more refined (spatially, temporally and thematically) allowing deeper insights into the causes and consequences of global LC change to be determined

Development of cloud removal and land cover Change extraction algorithms for remotely-sensed Landsat imagery

Land cover change monitoring requires the analysis of remotely-sensed data. In the tropics this is difficult because of persistent cloud cover, and data availability. This research focuses on the elimination of cloud cover as an important step towards addressing the issue of change detection. The result produced clearer images, whereas some persistent cloud remains. This persistent cloud and the cloud adjacency effects diminish the quality of image product and affect the change detection quality

Impact of land use and land cover change on land surface temperature in Iskandar Malaysia using remote sensing technique

Iskandar Malaysia is one of the impressive development projects ever undertaken in Malaysia that has been experiencing rapid rate of land use change since 2006. Land use change is due to the urban expansion and reduction in natural green areas resulted from enhanced economic growth. The three objectives of this study are (i) to estimate the land use and land cover changes (LULC) in Iskandar Malaysia from 1989 to 2014, (ii) to investigate the effect of LULC changes on land surface temperature (LST) change in the study area and (iii) to predict the LST by 2025. Remote sensing data namely Landsat (Landsat 5, 7 and 8) and Moderate Resolution Imaging Spectroradiometer (MODIS) of Terra product (MOD11A1) were used to classify various LULC and to calculate the LST in Iskandar Malaysia. There are two digital classification techniques used to classify and test the different LULC in this study area. Maximum Likelihood Classification (MLC) technique provided higher accuracies compared to the Support Vector Machine (SVM) technique. Consequently, the classified satellite images using the MLC technique were used to monitor changes in LULC in Iskandar Malaysia. LST was extracted using mono window. The mean LST using Geographic Information System (GIS) analysis according to LULC shows that water areas recorded the highest night time LST value, while forest recorded the lowest day time LST value. Urban areas are the warmest land use during the day and the second warmest land use during the night time. Moreover, the weighted average used to predict the mean LST of entire Iskandar Malaysia, it was found that if green space increases LST value would decrease by 0.5○C. To predict the effect of LULC changes on mean LST of each LULC types linear curve fitting model was used. According to the results, the mean night LST from 2000 to 2025 will increase in Iskandar Malaysia as urban (20.89°C to 22.39°C±0.45), mangrove (20.88°C to 22.59°C±0.50), forest (20.39°C to 21.04°C±0.18), oil palm (20.39°C to 21.25±0.25), rubber (20.34°C to 22.36°C ± 0.57), and water (21.61 °C to 23.31°C ± 0.51). The results show increment in day time at urban (29.26°C to 32.78°C±1.07), mangrove (26.23°C to 28.82 °C±0.89), forest (25.76°C to 27.54°C±0.49), oil palm (27.02°C to 29.54±0.70), rubber (26.49°C to 27.24°C ±0.29), and water (26.10 °C to 28.77 °C ± 0.8) respectively. Moreover, the relationship between LST and several impervious and vegetation indexes show that there is a strong relationship between impervious indexes and LST, and an inverse relationship between vegetation indexes and LST. Finally, this study concluded that replacing green natural area with improvise surface can increase the land surface temperature and have negative effect on urban thermal comfort

LAND USE AND LAND COVER CHANGE AS A DRIVER OF ECOSYSTEM DEGRADATION ACROSS BIOMES

The expansion and intensification of agricultural production in human-dominated landscapes threaten efforts to sustain natural ecosystems and maintain agricultural production in a changing climate. Long-term use of agricultural lands, combined with conversion of natural ecosystems for agricultural production, can rapidly degrade the health of remaining natural ecosystems. The fundamental goal of this dissertation was to assess the impacts of anthropogenic degradation on stocks and sequestration of carbon. Although degradation alters a range of ecosystem services, case studies of ecosystem degradation in this dissertation focus on reductions in vegetation productivity, carbon stocks, and the extent of natural forest cover as a result of human activity. Time series of satellite remote sensing data were used to track forest and rangeland degradation in the southwestern United States, forest carbon emissions from cropland expansion in the Brazilian Cerrado, and fire-driven forest conversion for oil palm plantations in Southeast Asia. Three major themes link the regional case studies: expansion and intensification of agricultural production, market demand and certification, and agricultural management in response to climate variability. Conclusions from the dissertation underscore the widespread influence of land management on vegetation productivity and forest carbon stocks. In the Southwest United States, reductions in net primary production on managed lands were higher in forested landscapes than other cover types. In contrast, Native American Indian Reservations, often considered to be more degraded, actually had smaller absolute reductions in net primary productivity during 2000-2011. Multi-year droughts in the southwest present new challenges for managing forests and rangelands, and climate projections suggest dry conditions will intensify in the coming century. In Southeast Asia, industry-led efforts to certify sustainable palm oil production were evaluated using satellite data on fires and forest loss. Rates of fire-driven deforestation and total fire activity declined following certification, highlighting the potential for certification to reduce ignitions during El Niño years and protect remaining fragments of lowland and peat forest. Aligning certification criteria for sustainable palm oil with satellite monitoring capabilities may help accelerate compliance with environmental legislation and market demands for deforestation-free products. In Brazil, government and industry actions to limit Amazon deforestation have largely overlooked the neighboring Cerrado biome. Forest carbon emissions from deforestation for soy expansion in the Cerrado increased substantially after the implementation of the Soy Moratorium in the Brazilian Amazon, partially offsetting recent reductions in Amazon deforestation carbon emissions. The success of policies to support sustainable agricultural production therefore depends on efforts to minimize cross-biome leakage and the ability to monitor compliance and unintended consequences. Solutions for management must also confront the growing influence of climate variability. Time series of satellite data may allow early detection of degradation impacts and support efforts to mitigate the influence of sustained agricultural production on natural systems. Changes in vegetation carbon stocks from ecosystem degradation varied across case studies, underscoring the diverse nature of direct and indirect drivers of degradation across different land use systems. Direct human drivers of ecosystem degradation in the southwest United States from management of livestock grazing resulted in gradual changes in vegetation productivity, whereas mining and oil extraction areas showed large and permanent reductions. Forest carbon emissions from agriculture expansion in the Cerrado were a one-time process, as native vegetation is cleared for cropland expansion. In contrast, the carbon emissions from Southeast Asia’s forest and peatland conversion involve both sudden and gradual processes, as carbon accumulation in oil palm plantations partially compensates for emissions from forest conversion. Overall, this research made contributions to understanding of the regional impacts of human activity and the potential for climate change mitigation from sustainable land use practices in human-dominated landscapes

Triennial Report: 2012-2014

Triennial Report Purpose [Page] 3 Geographical Information Science Center of Excellence [Page] 5 SDSU Faculty [Page] 6 EROS Faculty [Page] 13 Research Professors [Page] 19 Postdoctoral Fellows [Page] 24 GSE Ph.D Program [Page] 36 Ph.D. Fellowships [Page] 37 Ph.D. Students [Page] 38 Recent Ph.D. Graduates [Page] 46 Masters Students [Page] 56 Previous Ph.D. Students [Page] 58 Center Scholars Program [Page] 59 Research Staff [Page] 60 Administrative and Information Technology Staff [Page] 62 Computer Resources [Page] 66 Research Funding [Page] 67 Glancing Back, Looking Forward [Page] 68 Appendix I Alumni Faculty and Staff Appendix II Cool Faculty Research and Locations Appendix III Non-Academic Fun Things To Do Appendix IV Publications 2012-2014 Appendix V Directory Appendix VI GIScCE Birthplace Map Appendix VII How To Get To The GIScC

Atmospheric effects on land classification using satellites and their correction.

Haze occurs almost every year in Malaysia and is caused by smoke which originates from forest fire in Indonesia. It causes visibility to drop, therefore affecting the data acquired for this area using optical sensor such as that on board Landsat - the remote sensing satellite that have provided the longest continuous record of Earth's surface. The work presented in this thesis is meant to develop a better understanding of atmospheric effects on land classification using satellite data and method of removing them. To do so, the two main atmospheric effects dealt with here are cloud and haze. Detection of cloud and its shadow are carried out using MODIS algorithms due to allowing optimal use of its rich bands. The analysis is applied to Landsat data, in which shows a high agreement with other methods. The thesis then concerns on determining the most suitable classification scheme to be used. Maximum Likelihood (ML) is found to be a preferable classification scheme due to its simplicity, objectivity and ability to classify land covers with acceptable accuracy. The effects of haze are subsequently modelled and simulated as a summation of a weighted signal component and a weighted pure haze component. By doing so, the spectral and statistical properties of the land classes can be systematically investigated, in which showing that haze modifies the class spectral signatures, consequently causing the classification accuracy to decline. Based on the haze model, a method of removing haze from satellite data was developed and tested using both simulated and real datasets. The results show that the removal method is able clean up haze and improve classification accuracy, yet a highly non-uniform haze may hamper its performance

FCD Application of Landsat for Monitoring Mangrove in Central Kalimantan

A large amount of tropical mangrove forest in Indonesia has been lost due to rapid development in coastal areas, such as, aquaculture, industry, housing, and etc. Assessment of mangrove still mostly used conventional methods. It involves labor intensive, time consuming, high costs and impractical for use in large area. To answer these problems, this study aims to study accuracy and effectiveness of forest canopy density (FCD) model of Landsat for monitoring mangrove changes with large area ±2.600 hectares during periods 2002 and 2014 in Central Kalimantan. The result showed that FCD is capable to classified mangrove changes with overall accuracy 89.75%, and known that mangrove changes during approximately 12 years divided into four groups, i.e. deforested areas 11.11%, degraded areas 12.98%, regrowth areas 23.29% and not change areas 52.62%. Concluded that FCD model is quite accurate and effective used to monitor mangrove changes such as deforestation, degradation and regrowth

Using GIS to Predict Corn Yields in Colombia

Crop yield prediction can play an important role in developing the agriculture sector in Colombia. Remote sensing and GIS have proven to be an effective mechanism for this purpose in developed economies. This project created a proof-of-concept application for the Colombian Ministry of Agriculture and other related governmental institutions. The project used existing methodologies including the classification of satellite imagery, interpolation of climate data into continuous surfaces, the extraction of Normalized Difference Vegetation Index, and the computation of multiple linear regressions. ESRI ArcGIS provided the interface, software, tools and functions to build the application, and to integrate and automate the application‟s functionalities. Cloud coverage in the imagery and the lack of specialized data affected the accuracy of the crop yields estimates. Nevertheless, the application predicts corn yields with an estimated accuracy of 71% when cloud coverage is minimal. The application can use both Landsat and Spot preprocessed images, and in less than six minutes yield predictions for areas inside Cordoba, a major corn producing state in Colombia