Spatiotemporal analysis and monitoring of vegetation help us investigate ecological health and guide better forest conservation and land management practices for sustainable development. This paper proposes the use of spatial analysis approaches (i.e., ordinary least squares [OLS] and the Hurst exponent) combined with time-series analysis using enhanced vegetation index (EVI) data, derived from LANDSAT via the Google Earth Engine, to estimate the trends and sustainability of vegetation dynamics in the Tra Vinh Province in the Mekong River Delta. We also assessed the EVI changes connected to land change issues to examine the influence of land use conversion on vegetation dynamics. Results show that a large portion of the study area was covered by abundant vegetation (over 50% of the total area), and the increased EVI area was about 5.5-times greater than the area of EVI reduction. Additionally, vegetation sustainability was being seriously compromised (e.g., a decrease in the total area of 8,275 ha) due to several land conversion drivers such as shrimp farming, urbanisation, and industrialisation. Furthermore, results obtained from this research provide insight into the spatiotemporal dynamics of vegetation coverage and reveal the consistency of future vegetation trends. Moreover, the study also quantitatively assessed the positive impacts of Buddhist doctrines on reducing the negative trend of vegetation change in the study area. These findings can lay the ground to formulate sustainable land and environmental plans that meet the 11th, 13th and 15th Sustainable Development Goals (SDGs) (i.e., the sustainable cities and communities, the climate actions, and the life on land). Besides, the analytical procedure adopted in this study can also be applicable to any other coastal areas that require the accurate assessment of vegetation status over time.

Single-pixel cameras (SPCs) have been successfully used in different imaging applications during the last decade. In these techniques, the scene is illuminated with a sequence of microstructured light patterns codified onto a programmable spatial light modulator. The light coming from the scene is collected by a bucket detector, such as a photodiode. The image is recovered computationally from the photodiode electric signal. In this context, the signal quality is of capital value. One factor that degrades the signal quality is the noise, in particular, the photocurrent, the dark-current, and the thermal noise sources. In this chapter, we develop a numerical model of a SPC based on a photodiode, which considers the characteristics of the incident light, as well as the photodiode specifications. This model includes the abovementioned noise sources and infers the signal-to-noise ratio (SNR) of the SPCs in different contexts. In particular, we study the SNR as a function of the optical power of the incident light, the wavelength, and the photodiode temperature. The results of the model are compared with those obtained experimentally with a SPC.

We overview the performance of three dimensional (3D) integral imaging based human gesture recognition techniques under degraded environments. Using 3D integral imaging-based strategies we find substantial improvements over conventional 2D approaches for human gesture recognition in degraded conditions.

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