Estimating the leaf area index of Eucalyptus dunnii in the Midlands area, KwaZulu-Natal province over two seasons using vegetation indices and image texture measures derived from Worldview-3 imagery.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF

Quantifying mangrove leaf area index from Sentinel-2 imagery using hybrid models and active learning

Environmental Biolog

Towards scalable estimation of plant functional diversity from Sentinel-2: in-situ validation in a heterogeneous (semi-)natural landscape

Environmental BiologyConservation Biolog

Reviewing the potential of Sentinel-2 in assessing the drought

This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated

A fast Fourier convolutional deep neural network for accurate and explainable discrimination of wheat yellow rust and nitrogen deficiency from Sentinel-2 time series data

Introduction: Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms; and 2) the poor interpretability of the host-stress interaction. Methods: In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series. Results and discussion: The proposed model has been evaluated with ground truth data under both controlled and natural conditions. The results demonstrate that the high-level vector features interpret the influence of the host-stress interaction/response and the proposed model achieves competitive advantages in the detection and discrimination of yellow rust and nitrogen deficiency on Sentinel-2 time series in terms of classification accuracy, robustness, and generalization

Analisis Spasio-Temporal Perubahan Luas Lahan Garam di Pesisir Kabupaten Rembang

The increase in salt consumption is not proportional to the growth of salt production land. So far, the mapping of salt ponds has been carried out on a small to medium scale, so the accuracy is not too good. This study aimed to analyze changes in large-scale salt ponds in Rembang Regency during the period of 2005-2015. The data source used is a Very High-Resolution Satellite Image (CSRST) which has been corrected and other secondary data. Salt area was calculated through visual interpretation and manual detection. CSRST data usage by visual interpretation methods and manual detection had advantages in terms of geometric accuracy compared with the use of medium-sized image of automation methods. The analysis showed that the area of salt land in Rembang Regency increased during 2005-2015. The biggest increase occurred in 2005-2011, which increased by 546,255 ha or by 32%. The increase in area during 2011-2015 was not significant by 198.45 ha (11.46%). Area expansion was expected to occur in the next few years, but it was not expected to be significant because of the optimal land use in the most of Rembang already. Expansion of salt ponds remained possible by converting the existing rice fields on the coast of Rembang Regency. However, a comparative study of the economic value of rice fields and salt ponds must be carried out before converting the paddy fields to salt ponds

Exploring open-source multispectral satellite remote sensing as a tool to map long-term evolution of salt marsh shorelines

From an ecological and socio-economic perspective, salt marshes are one of the most valuable natural assets on Earth. As external pressures are causing their extensive degradation and loss globally, the ability to monitor salt marshes on a long-term scale and identify drivers of change is essential for their conservation. Remote sensing has been demonstrated to be one of the most adept methods for this purpose and open-source multispectral satellite remote sensing missions have the potential to provide worldwide long-term time-series coverage that is non-cost-prohibitive. This study derives the long-term lateral evolution of four salt marsh patches in the Ria Formosa coastal lagoon (Portugal) using data from the Sentinel-2 and Landsat missions as well as from aerial photography surveys to quantitatively examine the accuracy and associated uncertainty in using open-source multispectral satellite remote sensing for this purpose. The results show that these open-source satellite archives can be a useful tool for tracking long-term salt marsh extent dynamics. During 1976-2020, there was a net loss of salt marsh in the study area, with erosion rates reaching an average of-3.3 m/yr opposite a tidal inlet. The main source of error in the satellite results was the dataset spatial resolution limits, but the specific salt marsh shoreline environment contributed to the relative magnitude of that error. The study notes the influence of eco-geomorphological dynamics on the mapping of sedimentary environments, so far not extensively discussed in scientific literature, highlighting the difference between mapping a morphological process and a sedimentary environment.info:eu-repo/semantics/publishedVersio