Carbon Stocks in Peri-Urban Areas: A Case Study of Remote Sensing Capabilities

Abstract

Abstract-Peri-urban areas are the extension of cities into contiguous areas, where households and farms coexist. Carbon stocks (CSs) assessment, a concept here extended to urban features, has not yet been studied in depth over peri-urban areas due to uncertainties in such CSs quantification, level of detail required about construction materials, and the high spatial variability of those stocks. Remote sensing (RS)-based techniques have been successfully utilized in urban areas for assessing phenomena such as soil sealing, sprawl patterns, and dynamics of surface imperviousness, especially focusing on land cover classification at high to medium spatial scales. Over the floodplain study area of Emilia-Romagna region (Italy), we compared mapping products derived from Landsat multiseasonal data with different CSs, in soils and impervious surfaces, such as buildings and roads. A multiscale correlation analysis and regression assessment between CSs layers and satellite products were run at different grid cell sizes (100, 250, 500, and 1000 m). Results show that RS products from processing of midresolution satellite data can effectively perform well enough to estimate CSs in peri-urban areas, especially at 500-1000 m scale. Urban Fraction Cover method, derived through weighting urban land cover classes (including dense, sparse, and industrial urban features) can represent a good proxy of the ratio of anthropogenic over natural CSs (R 2 up to 0.75). Imperviousness Index (II) product scored high positive correlation with CSs over built-up areas (R 2 up to 0.77), and strong negative correlation with organic carbon density in soil (R 2 up to 0.73).

Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

“…This information can be Remote Sens. 2016, 8,927 3 of 17 (R 2 = 0.71 and 0.67) in both studies. Another study conducted by Hively et al [20], where PLSR was also used, provided decent model accuracies for all three compartments.…”

“…The model for sand turned out to be the most robust one of the three grain size fraction models. Mulder et al [8] presented comparable results for predicting sand content under laboratory conditions. Compared to other studies focusing on image data, the modeling results correspond with a study from Hively et al [20], who used similar input data.…”

Increasing the Accuracy of Mapping Urban Forest Carbon Density by Combining Spatial Modeling and Spectral Unmixing Analysis

“…Based on the study by Scalenghe et al [97], in a populated area the proportion of anthropogenic carbon stock increased and was accompanied by a large increase in the carbon emission per capita per unit area, while the proportions of soil and vegetation carbon stock decreased. Moreover, Villa et al [27] also quantified the ratio of anthropogenic to natural carbon stocks for a peri-urban area in Italy using Landsat multi-temporal data. However, this study concentrated only on the estimation and mapping of the forest carbon density in Shenzhen and did not deal with assessing the impacts of urbanization on the amounts of other carbon pool stocks because of a lack of data.…”

“…MODIS imagery has been widely used for global, national and regional vegetation carbon modeling, but is generally not applicable for estimating urban forest carbon density due to its coarse spatial resolution. An exception is the study conducted by Villa et al [27] who found that remotely sensed images with spatial resolutions of 500 m × 500 m to 1000 m × 1000 m could be used to effectively estimate carbon stocks for peri-urban areas. This exception can be attributed to the low intensity of urbanization in the peri-urban areas.…”

Prediction of Soil Moisture Content and Soil Salt Concentration from Hyperspectral Laboratory and Field Data

“…In addition, with the development of computing and remote sensing technology, the costs and time required for applying our models have become less expensive and more acceptable. These predictive SMC and SSC models can likely also be used for other soil properties such as soil organic carbon [50][51][52][53]. …”