No abstract

No abstract

No abstract

Topography and relief variability play a key role in ecosystem functioning and structuring. However, the most commonly used concept to relate pattern to process in landscape ecology, the so-called patch-corridor-matrix model, perceives the landscape as a planimetric surface. As a consequence, landscape metrics, used as numerical descriptors of the spatial arrangement of landscape mosaics, generally do not allow for the examination of terrain characteristics and may even produce erroneous results, especially in mountainous areas. This brief methodological study provides basic approaches to include relief properties into large-scale landscape analyses, including the calculation of standard landscape metrics on the basis of "true" surface geometries and the application of roughness parameters derived from surface metrology. The methods are tested for their explanatory power using neutral landscapes and simulated elevation models. The results reveal that area and distance metrics possess a high sensitivity to terrain complexity, while the values of shape metrics change only slightly when surface geometries are considered for their calculation. In summary, the proposed methods prove to be a valuable extension of the existing set of metrics mainly in "rough" landscape sections, allowing for a more realistic assessment of the spatial structure.

Urbanization in China has been rapid over the past three decades causing substantial replacement of the natural landscape by built-up land. In this paper, we present a comparison of Sentinel-2A MSI (S2A) and Landsat-8 OLI (L8) data in the retrieval of five built-up indices, namely Urban Index (UI), Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI) and two visible based indices, i.e. VgNIR-BI and VrNIR-BI. All the built-up indices maps water-masked were classified into built-up and non-built-up land using Otsu's method. Simultaneously, the support vector machine (SVM) algorithm was employed to classify the two imageries into three respective classes. The accuracy assessment results show that all built-up indices had higher Overall Accuracy for S2A (up to 98.14% for VrNIR-BI) and L8 (up to 98.42% for VrNIR-BI) imageries compared to SVM. The percentage differences demonstrate that L8 estimates higher built-up area compared to S2A between 1.48% and 8.45% via the built-up indices and 13.40% compared to the SVM. Cross-checking with the Statistical Yearbook, S2A is superior to L8 in built-up land mapping capability, especially utilizing built-up indices. The difference caused by spatial resolution and spectral response functions should be taken into consideration in synergistic scientific application.