title: Quantitative landslide susceptibility assessment of the Waikato region using GIS

authors: Schicker, Renee Deborah

subjects: landslide

abstract: Areas that have experienced landslide events in the past and the conditioning factors present at these sites can be used to identify areas of the same or similar susceptibility. This can be achieved through a landslide susceptibility assessment using a landslide inventory, a set of predictor variables and specialised computer software.
A quantitative landslide susceptibility assessment was conducted for the Waikato Region using two statistical approaches and eleven predictor variables. A landslide inventory map was constructed from the GNS QMap landslide spatial data and GeoNet landslide catalogue. Parameter maps for slope, elevation, aspect, lithology, land cover, soil order, mean monthly rainfall, maximum monthly rainfall, distance from roads, distance from faults and distance from rivers, were constructed and compiled into a database with the landslide inventory. 
The compiled data underwent both bivariate (weights of evidence) and multivariate (logistic regression) statistical analysis, and a landslide susceptibility map was derived for each. In the weights of evidence approach, the presence and absence of each class in relation to landslide occurrence and non-occurrence was individually assessed for each predictive factor. Logistic regression involves fitting a generalised non-linear model to the data based on a binary predictor (presence or absence of a past landslide event). For each method, a landslide susceptibility map was derived, and the model fit assessed using the landslide inventory.
Both susceptibility maps underwent an evaluation to determine the better predictive model. An independent landslide data set was compiled from observations made in Google Earth, and used to establish a set of prediction rate curves and cumulative area curves. Both susceptibility maps resulted in very similar prediction rate curves. Weights of evidence gave a better prediction rate in the 10, 20 and 30% most susceptible pixels, but not in the 40% most susceptible pixels. Neither susceptibility map could be justified as being better than the other based on the prediction rate curves alone. The cumulative area curves for each susceptibility map resulted in very different outcomes. Logistic regression gave the best result with a large proportion of the landslide area within a small proportion of the total area in the high susceptibility classes. Weights of evidence had a larger proportion of the landslide area in high susceptibility classes than logistic regression, but this was associated with a large proportion of total area. Based on the evaluation, the susceptibility map derived using logistic regression was determined to be superior.

text: Quantitative landslide susceptibility assessment of the Waikato region using GIS Areas that have experienced landslide events in the past and the conditioning factors present at these sites can be used to identify areas of the same or similar susceptibility. This can be achieved through a landslide susceptibility assessment using a landslide inventory, a set of predictor variables and specialised computer software.
A quantitative landslide susceptibility assessment was conducted for the Waikato Region using two statistical approaches and eleven predictor variables. A landslide inventory map was constructed from the GNS QMap landslide spatial data and GeoNet landslide catalogue. Parameter maps for slope, elevation, aspect, lithology, land cover, soil order, mean monthly rainfall, maximum monthly rainfall, distance from roads, distance from faults and distance from rivers, were constructed and compiled into a database with the landslide inventory. 
The compiled data underwent both bivariate (weights of evidence) and multivariate (logistic regression) statistical analysis, and a landslide susceptibility map was derived for each. In the weights of evidence approach, the presence and absence of each class in relation to landslide occurrence and non-occurrence was individually assessed for each predictive factor. Logistic regression involves fitting a generalised non-linear model to the data based on a binary predictor (presence or absence of a past landslide event). For each method, a landslide susceptibility map was derived, and the model fit assessed using the landslide inventory.
Both susceptibility maps underwent an evaluation to determine the better predictive model. An independent landslide data set was compiled from observations made in Google Earth, and used to establish a set of prediction rate curves and cumulative area curves. Both susceptibility maps resulted in very similar prediction rate curves. Weights of evidence gave a better prediction rate in the 10, 20 and 30% most susceptible pixels, but not in the 40% most susceptible pixels. Neither susceptibility map could be justified as being better than the other based on the prediction rate curves alone. The cumulative area curves for each susceptibility map resulted in very different outcomes. Logistic regression gave the best result with a large proportion of the landslide area within a small proportion of the total area in the high susceptibility classes. Weights of evidence had a larger proportion of the landslide area in high susceptibility classes than logistic regression, but this was associated with a large proportion of total area. Based on the evaluation, the susceptibility map derived using logistic regression was determined to be superior.

year: 2010

title: Marine-derived nitrogen and carbon in freshwater-riparian food webs of the Copper River Delta, southcentral Alaska

authors: Hicks, Brendan J.

subjects: stable isotopes

abstract: After rearing to adulthood at sea, coho salmon (Oncorhynchus kisutch) return to freshwater to spawn once and then die on or near their spawning grounds. We tested the hypothesis that spawning coho salmon return marine N and C to beaver (Castor canadensis) ponds of the Copper River Delta (CRD), Cordova, southcentral Alaska, thereby enhancing productivity of the aquatic food webs that support juvenile coho salmon. We sampled three types of pond treatment: (1) natural enrichment by spawning salmon, (2) artificial enrichment via addition of salmon carcasses and eggs, and (3) ponds with no salmon enrichment. All ponds supported juvenile coho salmon. Seasonal samples of stable isotopes revealed that juvenile coho salmon, threespine sticklebacks (Gasterosteus aculeatus), caddisfly larvae, leeches, and chironomid midge larvae were enriched with marine N and C. The aquatic vascular plants bur reed (Sparganium hyperboreum), pondweed (Potamogeton gramineus), and mare’s tail (Hippuris vulgaris) were enriched with marine N only. Riparian vegetation (Sitka alder Alnus viridis ssp. sinuata and willow Salix spp.) did not show enrichment. Artificial additions of adult carcasses and eggs of coho salmon increased the δ15N and δ13C values of juvenile coho salmon. In this dynamic and hydrologically complex coastal environment, spawning coho salmon contributed marine N and C comprising 10–50% of the dietary needs of juvenile coho salmon through direct consumption of eggs and carcass material. Invertebrates that have assimilated marine N and C yield a further indirect contribution. This perennial subsidy maintains the productivity of the ecosystem of the coho salmon on the CRD.

text: Marine-derived nitrogen and carbon in freshwater-riparian food webs of the Copper River Delta, southcentral Alaska After rearing to adulthood at sea, coho salmon (Oncorhynchus kisutch) return to freshwater to spawn once and then die on or near their spawning grounds. We tested the hypothesis that spawning coho salmon return marine N and C to beaver (Castor canadensis) ponds of the Copper River Delta (CRD), Cordova, southcentral Alaska, thereby enhancing productivity of the aquatic food webs that support juvenile coho salmon. We sampled three types of pond treatment: (1) natural enrichment by spawning salmon, (2) artificial enrichment via addition of salmon carcasses and eggs, and (3) ponds with no salmon enrichment. All ponds supported juvenile coho salmon. Seasonal samples of stable isotopes revealed that juvenile coho salmon, threespine sticklebacks (Gasterosteus aculeatus), caddisfly larvae, leeches, and chironomid midge larvae were enriched with marine N and C. The aquatic vascular plants bur reed (Sparganium hyperboreum), pondweed (Potamogeton gramineus), and mare’s tail (Hippuris vulgaris) were enriched with marine N only. Riparian vegetation (Sitka alder Alnus viridis ssp. sinuata and willow Salix spp.) did not show enrichment. Artificial additions of adult carcasses and eggs of coho salmon increased the δ15N and δ13C values of juvenile coho salmon. In this dynamic and hydrologically complex coastal environment, spawning coho salmon contributed marine N and C comprising 10–50% of the dietary needs of juvenile coho salmon through direct consumption of eggs and carcass material. Invertebrates that have assimilated marine N and C yield a further indirect contribution. This perennial subsidy maintains the productivity of the ecosystem of the coho salmon on the CRD.

year: 2005

title: Place-making: Wānanga based photographic approaches

authors: Hill, Rodrigo

subjects: photography

abstract: The words above are from the poem Māori King Tawhiao wrote expressing his love for his homelands of the Waikato and the region known today as the King Country. The words translate to: “I look down on the valley of Waikato, As though to hold it in the hollow of my hand.” Now imagine a large-scale photograph depicting a close-up frame of cupped hands trying to hold something carefully.

text: Place-making: Wānanga based photographic approaches The words above are from the poem Māori King Tawhiao wrote expressing his love for his homelands of the Waikato and the region known today as the King Country. The words translate to: “I look down on the valley of Waikato, As though to hold it in the hollow of my hand.” Now imagine a large-scale photograph depicting a close-up frame of cupped hands trying to hold something carefully.

year: 2022-12-31