title: Forest change mapping using remote sensing and Google Earth Engine

authors: Walawe Durage, Kalpanie Vimarshana Nandasena

subjects: Google Earth Engine

abstract: With advancements in data sources, software, and image analysis techniques, remote sensing has become an efficient method for forest classification. However, access to this technology has been limited for developing countries due to the high cost of high resolution images and analysis software. A potential solution is that NASA and the European Space Agency provide free access to mid-low resolution satellite images. In addition, Google Earth Engine (GEE), a free cloud-based geospatial analysis platform, has allowed researchers from developing countries to conduct research without relying on costly remote sensing software. This study evaluates the suitability of the freely available images and the GEE platform for different forest management applications in Sri Lanka. The research focused on three different scenarios in vegetation mapping: identifying home gardens, monitoring invasive pines, and forest cover mapping in a tropical montane region.
Home garden is an agroforestry class seen in tropical countries often overshadowed by global land cover classifications. This study used a random forest classification algorithm to classify the home garden, utilizing terrain data and Sentinel-2A images as the dataset. The results confirmed that the red band of Sentinel-2 and textural metrics derived from grey-level co-occurrence matrix analysis are effective in identifying home gardens from other forestry classes.
Monitoring the spread of invasive pines is important for forest management in Sri Lanka. This study used Landsat satellite images for the years 2000 and 2021 to detect the exotic Pinus caribaea species invasion of native habitats. By using an image difference technique, this research identified that the extent of these invasive pines had an overall decline over the past 21 years. Further, results showed that broadleaved forests and grasslands located within 100m of the pine plantations’ borders were susceptible to invasion.

In tropical regions, analysing forests can be difficult due to cloud cover. This research also focused on methods to enhance classification accuracy using multiple data sources: Sentinel-1, Sentinel-2, PALSAR-2, and elevation. Six different methods were tested. It was found that combining radar data with optical and topographical data improved vegetation classification accuracy. In addition, feature importance analysis using the random forest algorithm indicated that radar data plays a significant role in tropical land use classification.
The three case studies used in this research demonstrate that GEE and the freely available mid-low resolution satellite images make the application of remote sensing in Sri Lanka a viable solution for the monitoring and mapping of landcover.

text: Forest change mapping using remote sensing and Google Earth Engine With advancements in data sources, software, and image analysis techniques, remote sensing has become an efficient method for forest classification. However, access to this technology has been limited for developing countries due to the high cost of high resolution images and analysis software. A potential solution is that NASA and the European Space Agency provide free access to mid-low resolution satellite images. In addition, Google Earth Engine (GEE), a free cloud-based geospatial analysis platform, has allowed researchers from developing countries to conduct research without relying on costly remote sensing software. This study evaluates the suitability of the freely available images and the GEE platform for different forest management applications in Sri Lanka. The research focused on three different scenarios in vegetation mapping: identifying home gardens, monitoring invasive pines, and forest cover mapping in a tropical montane region.
Home garden is an agroforestry class seen in tropical countries often overshadowed by global land cover classifications. This study used a random forest classification algorithm to classify the home garden, utilizing terrain data and Sentinel-2A images as the dataset. The results confirmed that the red band of Sentinel-2 and textural metrics derived from grey-level co-occurrence matrix analysis are effective in identifying home gardens from other forestry classes.
Monitoring the spread of invasive pines is important for forest management in Sri Lanka. This study used Landsat satellite images for the years 2000 and 2021 to detect the exotic Pinus caribaea species invasion of native habitats. By using an image difference technique, this research identified that the extent of these invasive pines had an overall decline over the past 21 years. Further, results showed that broadleaved forests and grasslands located within 100m of the pine plantations’ borders were susceptible to invasion.

In tropical regions, analysing forests can be difficult due to cloud cover. This research also focused on methods to enhance classification accuracy using multiple data sources: Sentinel-1, Sentinel-2, PALSAR-2, and elevation. Six different methods were tested. It was found that combining radar data with optical and topographical data improved vegetation classification accuracy. In addition, feature importance analysis using the random forest algorithm indicated that radar data plays a significant role in tropical land use classification.
The three case studies used in this research demonstrate that GEE and the freely available mid-low resolution satellite images make the application of remote sensing in Sri Lanka a viable solution for the monitoring and mapping of landcover.

year: 2023

title: Nanosecond protein dynamics:  First detection of a neutron incoherent spin−echo signal

authors: Bellissent-Funel, Marie-Claire

subjects: biology

abstract: Motions in proteins occur over a wide range of time scales, from femtoseconds to seconds or longer.¹⁻³ Therefore, their characterization requires the application of a correspondingly wide range of experimental probes. Among these incoherent neutron scattering (INS) is a particularly direct source of information on time scales and forms of hydrogen motions.⁴⁻⁸ However, due to limited accuracy in the measurement of energy changes of scattered neutrons, INS work has been limited to the 10⁻¹⁵-10⁻¹⁰s time scales. The neutron spin-echo technique was developed to overcome this limitation.⁹ Neutron spin-echo allows precise measurement of velocity changes of neutrons via Larmor precession of the neutron’s spin. This has led to the possibility of extracting information on atomic motions on nanosecond and longer time scales.

text: Nanosecond protein dynamics:  First detection of a neutron incoherent spin−echo signal Motions in proteins occur over a wide range of time scales, from femtoseconds to seconds or longer.¹⁻³ Therefore, their characterization requires the application of a correspondingly wide range of experimental probes. Among these incoherent neutron scattering (INS) is a particularly direct source of information on time scales and forms of hydrogen motions.⁴⁻⁸ However, due to limited accuracy in the measurement of energy changes of scattered neutrons, INS work has been limited to the 10⁻¹⁵-10⁻¹⁰s time scales. The neutron spin-echo technique was developed to overcome this limitation.⁹ Neutron spin-echo allows precise measurement of velocity changes of neutrons via Larmor precession of the neutron’s spin. This has led to the possibility of extracting information on atomic motions on nanosecond and longer time scales.

year: 1998

title: Māori leaders wellbeing: A self-determination perspective

authors: Roche, Maree A.

subjects: Positive psychology

abstract: This research draws on interviews with 18 Māori leaders from various leadership positions within business, community, political and marae organisations, to garner an understanding of how their leadership roles interact with their own well-being. Analysis of interviews revealed that crosscultural developments in self-determination theory could be gained by incorporating Māori tikanga and values into a model of well-being for Māori leaders. Largely, the principles of tino rangatiratanga (autonomy and self-determination), mana (respect and influence), whānau (extended family), whakapapa (shared history) and whanaungatanga (kin relations, consultation and engagement), were united into a model of leader well-being. This ensured that mātauranga Māori (Māori knowledge) informed our model of Māori leader well-being, while also drawing on the burgeoning Western research in the area of well-being, specifically self-determination theory. Overall, we find that similarities exist with self-determination theory and Māori tikanga and values. However, in contrast to self-determination theory, autonomy and competence are developed within relationships, which means that ‘others’ underpin Māori leaders’ well-being. From this perspective, we present a view of the psychological and well-being resources that Māori leaders draw on to guide them through complex times.

text: Māori leaders wellbeing: A self-determination perspective This research draws on interviews with 18 Māori leaders from various leadership positions within business, community, political and marae organisations, to garner an understanding of how their leadership roles interact with their own well-being. Analysis of interviews revealed that crosscultural developments in self-determination theory could be gained by incorporating Māori tikanga and values into a model of well-being for Māori leaders. Largely, the principles of tino rangatiratanga (autonomy and self-determination), mana (respect and influence), whānau (extended family), whakapapa (shared history) and whanaungatanga (kin relations, consultation and engagement), were united into a model of leader well-being. This ensured that mātauranga Māori (Māori knowledge) informed our model of Māori leader well-being, while also drawing on the burgeoning Western research in the area of well-being, specifically self-determination theory. Overall, we find that similarities exist with self-determination theory and Māori tikanga and values. However, in contrast to self-determination theory, autonomy and competence are developed within relationships, which means that ‘others’ underpin Māori leaders’ well-being. From this perspective, we present a view of the psychological and well-being resources that Māori leaders draw on to guide them through complex times.

year: 2015