Use of Spectral Landscape Indices for Obstacle Detection in the Tasks of Mobile Robotic Platforms Navigation in Agricultural Areas

Purpose or research is to develop an algorithm for detecting obstacles on the orthophotomap based on the analysis of the spectral landscape indices in the tasks of mobile robotic equipment navigation in agricultural areas.Methods. The following landscape indices characterizing objects of various types on a map obtained by spectral aerial photography have been considered in the paper: normalized difference vegetation index (NDVI), normalized building difference index (NDBI), normalized difference water index (NDWI), and soil-adjusted vegetation index (SAVI). These indices provide an assessment of the four main classes of objects on the map: vegetation, buildings, water bodies, and soil cover. An algorithm that provides the segmentation of zones on the map which are impassable for ground robotic means using multispectral images and the considered indices was proposed.Results. Each image is presented in the form of a colour map based on the pixel-by-pixel calculation of the indicated indices. In this case, three indices, i.e. SAVI, NDWI, NDBI, are combined (superimposed on each other), and then the NDVI layer is subtracted from the resulting image to highlight the passable zones. Thus, a formula to obtain a mask of obstacles in the image was obtained. Hence, this algorithm allows generalizing the results of calculations for all selected indices and constructing a mask of obstacles in the image. For quantitative assessment the of the algorithm execution, the area of obstacles was calculated using the indices on a sample of manually marked images. The experiments conducted show that the developed algorithm provides, on average, detection of 85.47 % of the area of all impassable zones in the images in the above classes of land cover.Conclusion. An algorithm for the automated detection of obstacles on a map obtained from a spectral orthophotomap of the area for use in the tasks of mobile robotic equipment navigation in agricultural areas has been developed and tested. In the further research, to determine flat soil areas, it is planned to modify the developed solution using the improved modified soil-adjusted vegetation index (MSAVI).

Effects of Patchiness on Surface Soil Moisture of Alpine Meadow on the Northeastern Qinghai-Tibetan Plateau: Implications for Grassland Restoration

Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0–40 cm) with a satisfactory accuracy (R2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000–2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

Spatio-Temporal Evolution of Urban Morphology in the Yangtze River Middle Reaches Megalopolis, China

The Yangtze River Middle Reaches Megalopolis (YRMRM) is the primary urban cluster in central China, which is of vital ecological and economic importance over the Yangtze River basin. To fill the gap on updated evidence needed to support sustainable spatial planning and development in the YRMRM, we systematically characterise its urban spatial patterns and analyse their changes from 2000 to 2015 from two levels. At the regional level, landscape indices are used to depict urban morphology from four aspects, including fragmentation, complexity, contiguity and dispersion. At the local level, spatial autocorrelation analysis is conducted to detect whether the urban morphological patterns, as described by the four landscape indices, are clustered locally. The results showed an increasingly accelerated urban expansion in the YRMRM (approximately 250 km−2/a), contributed mainly by agricultural land conversion (60–80%, depending on subdivisions). An uneven spatial development pattern is identified in the three key metropolitan areas in the YRMRM. The Wuhan Metropolitan Area develops in a continuous and less fragmented fashion, with increased shape complexity and local dispersion. The spatial pattern of the Changsha-Zhuzhou-Xiangtan Metropolitan Area seems to be increasingly fragmented, complex and dispersed. The spatial development pattern in the Poyang Lake Metropolitan Area is overall continuous with an increasingly complex shape and severe local dispersion. Using landscape indices as indicators of sustainability, we discuss the potential environmental and climatic challenges in the YRMRM and the three metropolitan areas. Our results could help to raise awareness and concern for well-targeted management and planning in specific areas.

Characterizing the Landscape Structure of Urban Wetlands Using Terrain and Landscape Indices

Several studies have shown human impacts on urban wetlands. These impacts are mostly studied at broad scales, which may generalize and aggregate important information needed for landscape quantification or terrain analysis. This situation can weakly or inappropriately address the structure of wetland landscapes, thus affecting the assessment of the quantities and qualities of terrestrial wetland habitats. To address these issues for urban wetland dynamics, this study proposes the use of landscape and terrain indices to characterize the landscape structure of urban wetlands at a fine scale in order to assess its usefulness in contributing to wildlife sustainability. To achieve this goal, secondary terrain attribute data are integrated with an object-based satellite image classification at the wetland and watershed level. The result reveals a general swell in wetland coverage at the watershed level. Further analysis shows the size and shape complexities, and edge irregularities are increased significantly at the patch level but slightly at the watershed level. Terrain analysis further reveals a potential increase in wetness and decrease in stream power vulnerability for most of the major wetlands under study. These results suggest that terrain and landscape indices are effective in characterizing the structure of urban wetlands that supports socio-ecological sustainability.