An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

Challenges for monitoring artificial turf expansion with satellite remote sensing

Urban green spaces are central components of urban ecosystems, providing refuge for wildlife while helping 'future proof' cities against climate change. Conversion of urban green spaces to artificial turf has become increasingly popular in various developed countries, such as the UK, leading to reduced urban ecosystem services delivery. To date, there is no established satellite remote sensing method for reliably detecting and mapping artificial turf expansion at scale. We here assess the combined use of very high-resolution multispectral satellite imagery and classical, open source, supervised classification approaches to map artificial lawns in a typical British city. Both object-based and pixel-based classifications struggled to reliably detect artificial turf, with large patches of artificial turf not being any more reliably identified than small patches of artificial turf. As urban ecosystems are increasingly recognised for their key contributions to human wellbeing and health, the poor performance of these standard methods highlights the urgency of developing and applying new, easily accessible approaches for the monitoring of these important ecosystems.

Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann-Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (- 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (- 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning

Hyperspectral remote sensing image classification has been widely employed for numerous applications, such as environmental monitoring, agriculture, and mineralogy. During such classification, the number of training samples in each class often varies significantly. This imbalance in the dataset is often not identified because most classifiers are designed under a balanced dataset assumption, which can distort the minority classes or even treat them as noise. This may lead to biased and inaccurate classification results. This issue can be alleviated by applying preprocessing techniques that enable a uniform distribution of the imbalanced data for further classification. However, it is difficult to add new natural features to a training model by artificial combination of samples by using existing preprocessing techniques. For minority classes with sparse samples, the addition of sufficient natural features can effectively alleviate bias and improve the generalization. For such an imbalanced problem, semi-supervised learning is a creative solution that utilizes the rich natural features of unlabeled data, which can be collected at a low cost in the remote sensing classification. In this paper, we propose a novel semi-supervised learning-based preprocessing solution called NearPseudo. In NearPseudo, pseudo-labels are created by the initialization classifier and added to minority classes with the corresponding unlabeled samples. Simultaneously, to increase reliability and reduce the misclassification cost of pseudo-labels, we created a feedback mechanism based on a consistency check to effectively select the unlabeled data and its pseudo-labels. Experiments were conducted on a state-of-the-art representative hyperspectral dataset to verify the proposed method. The experimental results demonstrate that NearPseudo can achieve better classification accuracy than other common processing methods. Furthermore, it can be flexibly applied to most typical classifiers to improve their classification accuracy. With the intervention of NearPseudo, the accuracy of random forest, k-nearest neighbors, logistic regression, and classification and regression tree increased by 1.8%, 4.0%, 6.4%, and 3.7%, respectively. This study addresses research a gap to solve the imbalanced data-based limitations in hyperspectral image classification.

Airborne HySpex Hyperspectral Versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping

Climate change and anthropopression significantly impact plant communities by leading to the spread of expansive and alien invasive plants, thus reducing their biodiversity. Due to significant elevation gradients, high-mountain plant communities in a small area allow for the monitoring of the most important environmental changes. Additionally, being a tourist attraction, they are exposed to direct human influence (e.g., trampling). Airborne hyperspectral remote sensing is one of the best data sources for vegetation mapping, but flight campaign costs limit the repeatability of surveys. A possible alternative approach is to use satellite data from the Copernicus Earth observation program. In our study, we compared multitemporal Sentinel-2 data with HySpex airborne hyperspectral images to map the plant communities on Tatra Mountains based on open-source R programing implementation of Random Forest and Support Vector Machine classifiers. As high-mountain ecosystems are adapted to topographic conditions, the input of Digital Elevation Model (DEM) derivatives on the classification accuracy was analyzed and the effect of the number of training pixels was tested to procure practical information for field campaign planning. For 13 classes (from rock scree communities and alpine grasslands to montane conifer and deciduous forests), we achieved results in the range of 76–90% F1-score depending on the data set. Topographic features: digital terrain model (DTM), normalized digital surface model (nDSM), and aspect and slope maps improved the accuracy of HySpex spectral images, transforming their minimum noise fraction (MNF) bands and Sentinel-2 data sets by 5–15% of the F1-score. Maps obtained on the basis of HySpex imagery (2 m; 430 bands) had a high similarity to maps obtained on the basis of multitemporal Sentinel-2 data (10 m; 132 bands; 11 acquisition dates), which was less than one percentage point for classifications based on 500–1000 pixels; for sets consisting of 50–100 pixels, Random Forest (RF) offered better accuracy.

NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia

Obtaining accurate, precise and timely spatial information on the distribution and dynamics of urban green space is crucial in understanding livability of the cities and urban dwellers. Inspired from the importance of spatial information in planning urban lives, and availability of state-of-the-art remote sensing data and technologies in open access forms, in this work, we develop a simple three-level hierarchical mapping of urban green space with multiple usability to various stakeholders. We utilize the established Normalized Difference Vegetation Index (NDVI) threshold on Sentinel-2A Earth Observation image data to classify the urban vegetation of each Victorian Local Government Area (LGA). Firstly, we categorize each LGA region into two broad classes as vegetation and non-vegetation; secondly, we further categorize the vegetation regions of each LGA into two sub-classes as shrub (including grassland) and trees; thirdly, for both shrub and trees classes, we further classify them as stressed and healthy. We not only map the urban vegetation in hierarchy but also develop Urban Green Space Index (UGSI) and Per Capita Green Space (PCGS) for the Victorian Local Government Areas (LGAs) to provide insights on the association of demography with urban green infrastructure using urban spatial analytics. To show the efficacy of the applied method, we evaluate our results using a Google Earth Engine (GEE) platform across different NDVI threshold ranges. The evaluation result shows that our method produces excellent performance metrics such as mean precision, recall, f-score and accuracy. In addition to this, we also prepare a recent Sentinel-2A dataset and derived products of urban green space coverage of the Victorian LGAs that are useful for multiple stakeholders ranging from bushfire modellers to biodiversity conservationists in contributing to sustainable and resilient urban lives.

Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques

Recent developments in computer hardware made it possible to assess the viability of permutation-based approaches in image classification. Such approaches sample a reference dataset multiple times in order to train an arbitrary number of machine learning models while assessing their accuracy. So-called iterative accuracy assessment techniques or Monte-Carlo-based approaches can be a useful tool when it comes to assessment of algorithm/model performance but are lacking when it comes to actual image classification and map creation. Due to the multitude of models trained, one has to somehow reason which one of them, if any, should be used in the creation of a map. This poses an interesting challenge since there is a clear disconnect between algorithm assessment and the act of map creation. Our work shows one of the ways this disconnect can be bridged. We calculate how often a given pixel was classified as given class in all variations of a multitude of post-classification images delivered by models trained during the iterative assessment procedure. As a classification problem, a mapping of Calamagrostis epigejos, Rubus spp., Solidago spp. invasive plant species using three HySpex hyperspectral datasets collected in June, August and September was used. As a classification algorithm, the support vector machine approach was chosen, with training hyperparameters obtained using a grid search approach. The resulting maps obtained F1-scores ranging from 0.87 to 0.89 for Calamagrostis epigejos, 0.89 to 0.97 for Rubus spp. and 0.99 for Solidago spp.

Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China

Large-scale land-cover classification using a supervised algorithm is a challenging task. Enormous efforts have been made to manually process and check the production of national land-cover maps. This has led to complex pre- and post-processing and even the production of inaccurate mapping products from large-scale remote sensing images. Inspired by the recent success of deep learning techniques, in this study we provided a feasible automatic solution for improving the quality of national land-cover maps. However, the application of deep learning to national land-cover mapping remains limited because only small-scale noisy labels are available. To this end, a mutual transfer network MTNet was developed. MTNet is capable of learning better feature representations by mutually transferring pre-trained models from time-series of data and fine-tuning current data. An interactive training strategy such as this can effectively alleviate the effects of inaccurate or noisy labels and unbalanced sample distributions, thus yielding a relatively stable classification system. Extensive experiments were conducted by focusing on several representative regions to evaluate the classification results of our proposed method. Quantitative results showed that the proposed MTNet outperformed its baseline model about 1%, and the accuracy can be improved up to 6.45% compared with the model trained by the training set of another year. We also visualized the national classification maps generated by MTNet for two different time periods to quantitatively analyze the performance gain. It was concluded that the proposed MTNet provides an efficient method for large-scale land cover mapping.

Hyperspectral Image Classification Based on Sparse Superpixel Graph

Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG). In the constructed sparse superpixels graph, each vertex represents a superpixel instead of a pixel, which greatly reduces the size of graph. Meanwhile, both spectral information and spatial structure are considered by using superpixel, local spatial connection and global spectral connection. To verify the effectiveness of the proposed method, three real hyperspectral images, Indian Pines, Pavia University and Salinas, are chosen to test the performance of our proposal. Experimental results show that the proposed method has good classification completion on the three benchmarks. Compared with several competitive superpixel-based HSI classification approaches, the method has the advantages of high classification accuracy (>97.85%) and rapid implementation (<10 s). This clearly favors the application of the proposed method in practice.

Influence of Spatial Resolution for Vegetation Indices’ Extraction Using Visible Bands from Unmanned Aerial Vehicles’ Orthomosaics Datasets

The consolidation of unmanned aerial vehicle (UAV) photogrammetric techniques for campaigns with high and medium observation scales has triggered the development of new application areas. Most of these vehicles are equipped with common visible-band sensors capable of mapping areas of interest at various spatial resolutions. It is often necessary to identify vegetated areas for masking purposes during the postprocessing phase, excluding them for the digital elevation models (DEMs) generation or change detection purposes. However, vegetation can be extracted using sensors capable of capturing the near-infrared part of the spectrum, which cannot be recorded by visible (RGB) cameras. In this study, after reviewing different visible-band vegetation indices in various environments using different UAV technology, the influence of the spatial resolution of orthomosaics generated by photogrammetric processes in the vegetation extraction was examined. The triangular greenness index (TGI) index provided a high level of separability between vegetation and nonvegetation areas for all case studies in any spatial resolution. The efficiency of the indices remained fundamentally linked to the context of the scenario under investigation, and the correlation between spatial resolution and index incisiveness was found to be more complex than might be trivially assumed.

A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery

Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models.

Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

Spatial information regarding the arrangement of land cover objects plays an important role in distinguishing the land use types at land parcel or local neighborhood levels. This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution remote sensing images, with particular interest in comparing land use classifications between different graph-based methods and between different remote sensing images. We examine three kinds of graph-based methods, i.e., feature engineering, graph kernels, and GCNs. Based upon the extracted arrangement features and features regarding the spatial composition of land cover objects, we formulated ten land use classifications. We tested those on two different remote sensing images, which were acquired from GaoFen-2 (with a spatial resolution of 0.8 m) and ZiYuan-3 (of 2.5 m) satellites in 2020 on Fuzhou City, China. Our results showed that land use classifications that are based on the arrangement features derived from GCNs achieved the highest classification accuracy than using graph kernels and handcrafted graph features for both images. We also found that the contribution to separating land use types by arrangement features varies between GaoFen-2 and ZiYuan-3 images, due to the difference in the spatial resolution. This study offers a set of approaches for effectively mapping land use types from (very) high resolution satellite images.