Land2014, 3(4), 1270-1283; doi:10.3390/land3041270 - published 14 November 2014 Show/Hide Abstract
Abstract: The objectives of this study are to: (1) evaluate accuracy of tree height measurements of manual stereo viewing on a computer display using digital aerial photographs compared with airborne LiDAR height measurements; and (2) develop an empirical model to estimate stand-level aboveground biomass with variables derived from manual stereo viewing on the computer display in a Cambodian tropical seasonal forest. We evaluate observation error of tree height measured from the manual stereo viewing, based on field measurements. RMSEs of tree height measurement with manual stereo viewing and LiDAR were 1.96 m and 1.72 m, respectively. Then, stand-level aboveground biomass is regressed against tree height indices derived from the manual stereo viewing. We determined the best model to estimate aboveground biomass in terms of the Akaike’s information criterion. This was a model of mean tree height of the tallest five trees in each plot (R2 = 0.78; RMSE = 58.18 Mg/ha). In conclusion, manual stereo viewing on the computer display can measure tree height accurately and is useful to estimate aboveground stand biomass.
Land2014, 3(4), 1251-1269; doi:10.3390/land3041251 - published 5 November 2014 Show/Hide Abstract
Abstract: This philosophical paper explores the aesthetic argument for landscape conservation. The main claim is that the experience of beautiful landscapes is an essential part of the good human life. Beautiful landscapes make us feel at home in the world. Their great and irreplaceable value lies therein. To establish this claim, the concepts of landscape and “Stimmung” are clarified. It is shown how “Stimmung” (in the sense of mood) is infused into landscape (as atmosphere) and how we respond to it aesthetically. We respond by resonating or feeling at home. The paper ends by indicating how art can help us to better appreciate landscape beauty. This is done by way of an example from contemporary nature poetry, Michael Donhauser’s Variationen in Prosa, which begins with “Und was da war, es nahm uns an” (“And what was there accepted us”).
Land2014, 3(4), 1232-1250; doi:10.3390/land3041232 - published 10 October 2014 Show/Hide Abstract
Abstract: In-depth understanding about the vertical distribution of soil organic carbon (SOC) density is crucial for carbon (C) accounting, C budgeting and designing appropriate C sequestration strategies. We examined the vertical distribution of SOC density under different land use/land cover (LULC) types, altitudinal zones and aspect directions in a montane ecosystem of Bhutan. Sampling sites were located using conditioned Latin hypercube sampling (cLHS) scheme. Soils were sampled based on genetic horizons. An equal-area spline function was fitted to interpolate the target values to predetermined depths. Linear mixed model was fitted followed by mean separation tests. The results show some significant effects of LULC, altitudinal zone and slope aspect on the vertical distribution of SOC density in the profiles. Based on the proportion of mean SOC density in the first 20 cm relative to the cumulative mean SOC density in the top meter, the SOC density under agricultural lands (34%) was more homogeneously distributed down the profiles than forests (39%), grasslands (59%) and shrublands (43%). Similarly, the SOC density under 3500–4000 m zone (35%) was more uniformly distributed compared to 3000–3500 m zone (43%) and 1769–2500 m and 2500–3000 m zones (41% each). Under different aspect directions, the north and east-facing slopes (38% each) had more uniform distribution of SOC density than south (40%) and west-facing slopes (49%).
Land2014, 3(4), 1214-1231; doi:10.3390/land3041214 - published 26 September 2014 Show/Hide Abstract
Abstract: Land cover change impacts ecosystem function across the globe. The use of land cover data is vital in the detection of these changes over time; however, most available land cover products, such as the National Land Cover Dataset (NLCD), are produced relatively infrequently. The most recent NLCD at the time of this research was produced in 2006 and does not adequately reflect the impact of land cover changes that have occurred since, including the occurrence of two large wildfires in 2008 in our study area. Therefore, there is a need for the classification of historical remotely sensed data, such as Landsat scenes, through replicable methods. While it is possible to collect field data coinciding with current or future Landsat acquisitions, it is impossible to retrospectively collect data for previous years; thus, fewer studies have focused on the classification of historical scenes. Using a single year of field reference and multi-year aerial photography data, we applied a simple decision tree classifier to accurately classify historic satellite data and produced maps of land cover to incorporate the effects of 2008 wildfires occurring between NLCD production dates. Overall accuracy ranged from 76 to 90 percent and was assessed using conventional error matrices.
Land2014, 3(3), 1180-1213; doi:10.3390/land3031180 - published 19 September 2014 Show/Hide Abstract
Abstract: In the context of sustainable urban development, the application of selected indicators integrated with scenario simulation and analysis can contribute to evidence-based decision making. This paper discusses the application of land use modelling and opportunity mapping approaches to evaluate regional development scenarios for the Greater Dublin Region in the period to 2026 evolving from research initially developed with the Dublin Regional Authority. This involved the simulation of four different future regional development scenarios using an adapted version of the MOLAND model with opportunity maps based on combined spatial indicators corresponding to these scenarios. The results produce valuable information for policy makers and planners assisting the evaluation of the consequences of their decisions in both a spatial and temporal context. This paper aims to show how current and future planning and economic policy can make targeted and evidence-based policy interventions and achieve resource efficiencies through the use of scenario analysis.
Land2014, 3(3), 1158-1179; doi:10.3390/land3031158 - published 17 September 2014 Show/Hide Abstract
Abstract: We build upon much of the accumulated knowledge of the widely used SLEUTH urban land change model and offer advances. First, we use SLEUTH’s exclusion/attraction layer to identify and test different urban land cover change drivers; second, we leverage SLEUTH’s self-modification capability to incorporate a demographic model; and third, we develop a validation procedure to quantify the influence of land cover change drivers and assess uncertainty. We found that, contrary to our a priori expectations, new development is not attracted to areas serviced by existing or planned water and sewer infrastructure. However, information about where population and employment growth is likely to occur did improve model performance. These findings point to the dominant role of centrifugal forces in post-industrial cities like Baltimore, MD. We successfully developed a demographic model that allowed us to constrain the SLEUTH model forecasts and address uncertainty related to the dynamic relationship between changes in population and employment and urban land use. Finally, we emphasize the importance of model validation. In this work the validation procedure played a key role in rigorously assessing the impacts of different exclusion/attraction layers and in assessing uncertainty related to population and employment forecasts.