Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia.

This study develops a modelling framework by utilizing multi-sensor imagery for classifying different forest and land use types in the Phnom Kulen National Park (PKNP) in Cambodia. Three remote sensing datasets (Landsat optical data, ALOS L-band data and LiDAR derived Canopy Height Model (CHM)) were used in conjunction with three different machine learning (ML) regression techniques (Support Vector Machines (SVM), Random Forests (RF) and Artificial Neural Networks (ANN)). These ML methods were implemented on (a) Landsat spectral data, (b) Landsat spectral band & ALOS backscatter data, and (c) Landsat spectral band, ALOS backscatter data, & LiDAR CHM data. The Landsat-ALOS combination produced more accurate classification results (95% overall accuracy with SVM) compared to Landsat-only bands for all ML models. Inclusion of LiDAR CHM (which is a proxy for vertical canopy heights) improved the overall accuracy to 98%. The research establishes that majority of PKNP is dominated by cashew plantations and the nearly intact forests are concentrated in the more inaccessible parts of the park. The findings demonstrate how different RS datasets can be used in conjunction with different ML models to map forests that had undergone varying levels of degradation and plantations.

Evaluating the ability of community-protected forests in Cambodia to prevent deforestation and degradation using temporal remote sensing data.

Community forests are known to play an important role in preserving forests in Cambodia, a country that has seen rapid deforestation in recent decades. The detailed evaluation of the ability of community-protected forests to retain forest cover and prevent degradation in Cambodia will help to guide future conservation management. In this study, a combination of remotely sensing data was used to compare the temporal variation in forest structure for six different community forests located in the Phnom Kulen National Park (PKNP) in Cambodia and to assess how these dynamics vary between community-protected forests and a wider study area. Medium-resolution Landsat, ALOS PALSAR data, and high-resolution LiDAR data were used to study the spatial distribution of forest degradation patterns and their impacts on above-ground biomass (AGB) changes. Analysis of the remotely sensing data acquired at different spatial resolutions revealed that between 2012 and 2015, the community forests had higher forest cover persistence and lower rates of forest cover loss compared to the entire study area. Furthermore, they faced lower encroachment from cashew plantations compared to the wider landscape. Four of the six community forests showed a recovery in canopy gap fractions and subsequently, an increase in the AGB stock. The levels of degradation decreased in forests that had an increase in AGB values. However, all community forests experienced an increase in understory damage as a result of selective tree removal, and the community forests with the sharpest increase in understory damage experienced AGB losses. This is the first time multitemporal high-resolution LiDAR data have been used to analyze the impact of human-induced forest degradation on forest structure and AGB. The findings of this work indicate that while community-protected forests can improve conservation outcomes to some extent, more interventions are needed to curb the illegal selective logging of valuable timber trees.

The environmental impact of Cambodia's ancient city of Mahendraparvata (Phnom Kulen).

The Khmer kingdom, whose capital was at Angkor from the 9(th) to the 14(th)-15(th) century, was founded in 802 by king Jayavarman II in a city called Mahandraparvata, on Phnom Kulen. Virtually nothing more is known of Mahandraparvata from the epigraphic sources, but systematic archaeological survey and excavation have identified an array of cultural features that point to a more extensive and enduring settlement than the historical record indicates. Recent remote sensing data have revolutionized our view, revealing the remains of a city with a complex and spatially extensive network of urban infrastructure. Here, we present a record of vegetation change and soil erosion from within that urban network, dating from the 8(th) century CE. Our findings indicate approximately 400 years of intensive land use, punctuated by discrete periods of intense erosion beginning in the mid 9(th) century and ending in the late 11(th) century. A marked change in water management practices is apparent from the 12(th) century CE, with implications for water supply to Angkor itself. This is the first indication that settlement on Mahendraparvata was not only extensive, but also intensive and enduring, with a marked environmental impact.

Uncovering archaeological landscapes at Angkor using lidar.

Previous archaeological mapping work on the successive medieval capitals of the Khmer Empire located at Angkor, in northwest Cambodia (∼9th to 15th centuries in the Common Era, C.E.), has identified it as the largest settlement complex of the preindustrial world, and yet crucial areas have remained unmapped, in particular the ceremonial centers and their surroundings, where dense forest obscures the traces of the civilization that typically remain in evidence in surface topography. Here we describe the use of airborne laser scanning (lidar) technology to create high-precision digital elevation models of the ground surface beneath the vegetation cover. We identify an entire, previously undocumented, formally planned urban landscape into which the major temples such as Angkor Wat were integrated. Beyond these newly identified urban landscapes, the lidar data reveal anthropogenic changes to the landscape on a vast scale and lend further weight to an emerging consensus that infrastructural complexity, unsustainable modes of subsistence, and climate variation were crucial factors in the decline of the classical Khmer civilization.

Evolution and history of grapevine (Vitis vinifera) under domestication: new morphometric perspectives to understand seed domestication syndrome and reveal origins of ancient European cultivars.

BACKGROUND AND AIMS: In spite of the abundance of archaeological, bio-archaeological, historical and genetic data, the origins, historical biogeography, identity of ancient grapevine cultivars and mechanisms of domestication are still largely unknown. Here, analysis of variation in seed morphology aims to provide accurate criteria for the discrimination between wild grapes and modern cultivars and to understand changes in functional traits in relation to the domestication process. This approach is also used to quantify the phenotypic diversity in the wild and cultivated compartments and to provide a starting point for comparing well-preserved archaeological material, in order to elucidate the history of grapevine varieties. METHODS: Geometrical analysis (elliptic Fourier transform method) was applied to grapevine seed outlines from modern wild individuals, cultivars and well-preserved archaeological material from southern France, dating back to the first to second centuries. KEY RESULTS AND CONCLUSIONS: Significant relationships between seed shape and taxonomic status, geographical origin (country or region) of accessions and parentage of varieties are highlighted, as previously noted based on genetic approaches. The combination of the analysis of modern reference material and well-preserved archaeological seeds provides original data about the history of ancient cultivated forms, some of them morphologically close to the current 'Clairette' and 'Mondeuse blanche' cultivars. Archaeobiological records seem to confirm the complexity of human contact, exchanges and migrations which spread grapevine cultivation in Europe and in Mediterranean areas, and argue in favour of the existence of local domestication in the Languedoc (southern France) region during Antiquity.