Land cover mapping toward finer scales

Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network

Detailed and accurate urban landscape mapping, especially for urban blue-green-gray (UBGG) continuum, is the fundamental first step to understanding human-nature coupled urban systems. Nevertheless, the intricate spatial heterogeneity of urban landscapes within cities and across urban agglomerations presents challenges for large-scale and fine-grained mapping. In this study, we generated a 3 m high-resolution UBGG landscape dataset (UBGG-3m) for 36 Chinese metropolises using a transferable multi-scale high-resolution convolutional neural network and 336 Planet images. To train the network for generalization, we also created a large-volume UBGG landscape sample dataset (UBGGset) covering 2,272 km2 of urban landscape samples at 3 m resolution. The classification results for five cities across diverse geographic regions substantiate the superior accuracy of UBGG-3m in both visual interpretation and quantitative evaluation (with an overall accuracy of 91.2% and FWIoU of 83.9%). Comparative analyses with existing datasets underscore the UBGG-3m's great capability to depict urban landscape heterogeneity, providing a wealth of new data and valuable insights into the complex and dynamic urban environments in Chinese metropolises.

A Review on the Mechanism and Applications of CRISPR/Cas9/Cas12/Cas13/Cas14 Proteins Utilized for Genome Engineering

The clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein (CRISPR/Cas) system has altered life science research offering enormous options in manipulating, detecting, imaging, and annotating specific DNA or RNA sequences of diverse organisms. This system incorporates fragments of foreign DNA (spacers) into CRISPR cassettes, which are further transcribed into the CRISPR arrays and then processed to make guide RNA (gRNA). The CRISPR arrays are genes that encode Cas proteins. Cas proteins provide the enzymatic machinery required for acquiring new spacers targeting invading elements. Due to programmable sequence specificity, numerous Cas proteins such as Cas9, Cas12, Cas13, and Cas14 have been exploited to develop new tools for genome engineering. Cas variants stimulated genetic research and propelled the CRISPR/Cas tool for manipulating and editing nucleic acid sequences of living cells of diverse organisms. This review aims to provide detail on two classes (class 1 and 2) of the CRISPR/Cas system, and the mechanisms of all Cas proteins, including Cas12, Cas13, and Cas14 discovered so far. In addition, we also discuss the pros and cons and recent applications of various Cas proteins in diverse fields, including those used to detect viruses like severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This review enables the researcher to gain knowledge on various Cas proteins and their applications, which have the potential to be used in next-generation precise genome engineering.

Detecting Mountain Forest Dynamics in the Eastern Himalayas

Forest dynamics is critical to forested ecosystems, and considerable efforts have been devoted to monitoring long-term forest dynamics with the goals of sustainable management and conservation of forests. However, little attention has been given to mountain forests, which are more challenging to monitor due to complex topography, weather, and their distribution. We developed a 30-m resolution tree-canopy cover (TCC) and forest change dataset for the Eastern Himalayas from 1986 to 2021. The tree-canopy cover estimation was validated against estimates from the space-borne Global Ecosystem Dynamics Investigation (GEDI), demonstrating strong consistency (R-square greater than 0.81). A comprehensive assessment for the forest change dataset was performed using 448 visually interpreted points and reported high accuracy of the dataset, i.e., 97.7% and 95.9% for forest loss and gain, respectively. Higher producer and user accuracies were reported for forest loss (PA = 78.0%, UA = 60.9%) than these for forest gain (PA = 61.7%, UA = 56.7%). The results indicated that (1) the mean tree-canopy cover in the region increased by 2.76% over the past three decades, from 40.67% in 1990 to 43.43% in 2020, suggesting the forests have improved during the period; (2) forest loss was identified for a total area of 6990 km2 across the study area, which is less than the 10,700 km2 identified as forest gain; (3) stronger forest gains were found at elevations greater than 3000 m asl, indicating faster forest growth in high elevations likely influenced by the warming temperatures in the Eastern Himalayas.

Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

Appropriate crop type mapping to monitor and control land management is very important in developing countries. It can be very useful where digital cadaster maps are not available or usage of Remote Sensing (RS) data is not utilized in the process of monitoring and inventory. The main goal of the present research is to compare and assess the importance of optical RS data in crop type classification using medium and high spatial resolution RS imagery in 2018. With this goal, Landsat 8 (L8) and Sentinel-2 (S2) data were acquired over the Tashkent Province between the crop growth period of May and October. In addition, this period is the only possible time for having cloud-free satellite images. The following four indices “Normalized Difference Vegetation Index” (NDVI), “Enhanced Vegetation Index” (EVI), and “Normalized Difference Water Index” (NDWI1 and NDWI2) were calculated using blue, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands. Support-Vector-Machine (SVM) and Random Forest (RF) classification methods were used to generate the main crop type maps. As a result, the Overall Accuracy (OA) of all indices was above 84% and the highest OA of 92% was achieved together with EVI-NDVI and the RF method of L8 sensor data. The highest Kappa Accuracy (KA) was found with the RF method of L8 data when EVI (KA of 88%) and EVI-NDVI (KA of 87%) indices were used. A comparison of the classified crop type area with Official State Statistics (OSS) data about sown crops area demonstrated that the smallest absolute weighted average (WA) value difference (0.2 thousand ha) was obtained using EVI-NDVI with RF method and NDVI with SVM method of L8 sensor data. For S2-sensor data, the smallest absolute value difference result (0.1 thousand ha) was obtained using EVI with RF method and 0.4 thousand ha using NDVI with SVM method. Therefore, it can be concluded that the results demonstrate new opportunities in the joint use of Landsat and Sentinel data in the future to capture high temporal resolution during the vegetation growth period for crop type mapping. We believe that the joint use of S2 and L8 data enables the separation of crop types and increases the classification accuracy.

Investigation of Long-Term Forest Dynamics in Protected Areas of Northeast China Using Landsat Data

Forest dynamics, including forest loss and gain, are long-term complex ecological processes affected by nature and human activities. It is particularly important to understand the long-term forest dynamics of protected areas to evaluate their conservation efforts. This study adopted the Landsat tree-canopy cover (TCC) method to derive annual TCC data for the period 1984–2020 for the protected areas of northeast China, where protection policies have been carried out since the end of the 20th century, e.g., the Natural Forest Conversion Program (NFCP). A strong correlation was found between the TCC estimates derived from Landsat and LiDAR observations, suggesting the high accuracy of TCC. Forest loss and gain events were also identified from the time series of TCC estimates. High correlations were reported for both forest loss (Producer’s accuracy = 85.21%; User’s accuracy = 84.26%) and gain (Producer’s accuracy = 87.74%; User’s accuracy = 88.31%), suggesting that the approach can be used for monitoring and evaluating the effectiveness of the NFCP and other forest conservation efforts. The results revealed a fluctuating upward trend of the TCC of the protected area from 1986 to 2018. The increased area of TCC was much larger than the decreased area, accounting for 59.68% and 40.34%, respectively, suggesting the effectiveness of protection policies. Since the NFCP was officially implemented in 1998, deforestation was effectively curbed, the area of forest loss was significantly reduced (slope: −14.24%/year), and the area of forest gain significantly increased (slope: 4.13%/year). We found that regional forest changes were mainly manifested in “forest gain after loss (forest recovery)” and “forest gain with no loss (forest newborn)”, accounting for 40.29% and 37.28% of the total area of forest change, respectively. Moreover, the forest gain area far exceeds the forest loss area, reaching 11.24 million hectares, suggesting a successful forest recovery due to forest protection.

Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images

The type of algorithm employed to classify remote sensing imageries plays a great role in affecting the accuracy. In recent decades, machine learning (ML) has received great attention due to its robustness in remote sensing image classification. In this regard, random forest (RF) and support vector machine (SVM) are two of the most widely used ML algorithms to generate land cover (LC) maps from satellite imageries. Although several comparisons have been conducted between these two algorithms, the findings are contradicting. Moreover, the comparisons were made on local-scale LC map generation either from high or medium resolution images using various software, but not Python. In this paper, we compared the performance of these two algorithms for large area LC mapping of parts of Africa using coarse resolution imageries in the Python platform by the employing Scikit-Learn (sklearn) library. We employed a big dataset, 297 metrics, comprised of systematically selected 9-month composite FegnYun-3C (FY-3C) satellite images with 1 km resolution. Several experiments were performed using a range of values to determine the best values for the two most important parameters of each classifier, the number of trees and the number of variables, for RF, and penalty value and gamma for SVM, and to obtain the best model of each algorithm. Our results showed that RF outperformed SVM yielding 0.86 (OA) and 0.83 (k), which are 1–2% and 3% higher than the best SVM model, respectively. In addition, RF performed better in mixed class classification; however, it performed almost the same when classifying relatively pure classes with distinct spectral variation, i.e., consisting of less mixed pixels. Furthermore, RF is more efficient in handling large input datasets where the SVM fails. Hence, RF is a more robust ML algorithm especially for heterogeneous large area mapping using coarse resolution images. Finally, default parameter values in the sklearn library work well for satellite image classification with minor/or no adjustment for these algorithms.

Potential of CRISPR/Cas system in the diagnosis of COVID-19 infection

INTRODUCTION

Emerging novel infectious diseases and persistent pandemics with potential to destabilize normal life remain a public health concern for the whole world. The recent outbreak of pneumonia caused by Coronavirus infectious disease-2019 (COVID-19) resulted in high mortality due to a lack of effective drugs or vaccines. With a constantly increasing number of infections with mutated strains and deaths across the globe, rapid, affordable and specific detections with more accurate diagnosis and improved health treatments are needed to combat the spread of this novel pathogen COVID-19.

AREAS COVERED

Researchers have started to utilize the recently invented clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (CRISPR/Cas)-based tools for the rapid detection of novel COVID-19. In this review, we summarize the potential of CRISPR/Cas system for the diagnosis and enablement of efficient control of COVID-19.

EXPERT OPINION

Multiple groups have demonstrated the potential of utilizing CRISPR-based diagnosis tools for the detection of SARS-CoV-2. In coming months, we expect more novel and rapid CRISPR-based kits for mass detection of COVID-19-infected persons within a fraction of a second. Therefore, we believe science will conquer COVID-19 in the near future.

Mapping Global Urban Impervious Surface and Green Space Fractions Using Google Earth Engine

Urban impervious surfaces area (ISA) and green space (GS), two primary components of urban environment, are pivotal in detecting urban environmental quality and addressing global environmental change issues. However, the current global mapping of ISA and GS is not effective enough to accurately delineate in urban areas due to the mosaicked and complex structure. To address the issue, the hierarchical architecture principle and subpixel metric method were applied to map 30 m global urban ISA and GS fractions for the years 2015 and circa 2020. We use random forest algorithms for retrieval of the Normalized Settlement Density Index and Normalized Green Space Index from Landsat images using Google Earth Engine. The correlation coefficients of global urban ISA and GS fractions were all higher than 0.9 for 2015 and circa 2020. Our results show global urban ISA and GS areas in circa 2020 were 31.19 × 104 km2 and 17.16 × 104 km2, respectively. The novel ISA and GS fractions product can show potential applications in assessing the effects of urbanization on climate, ecology, and urban sustainability.

Towards an open and synergistic framework for mapping global land cover

Global land-cover datasets are key sources of information for understanding the complex inter-actions between human activities and global change. They are also among the most critical variables for climate change studies. Over time, the spatial resolution of land cover maps has increased from the kilometer scale to 10-m scale. Single-type historical land cover datasets, including for forests, water, and impervious surfaces, have also been developed in recent years. In this study, we present an open and synergy framework to produce a global land cover dataset that combines supervised land cover classification and aggregation of existing multiple thematic land cover maps with the Google Earth Engine (GEE) cloud computing platform. On the basis of this method of classification and mosaicking, we derived a global land cover dataset for 6 years over a time span of 25 years. The overall accuracies of the six maps were around 75% and the accuracy for change area detection was over 70%. Our product also showed good similarity with the FAO and existing land cover maps.

High agricultural water consumption led to the continued shrinkage of the Aral Sea during 1992-2015

The shrinkage of the Aral Sea started in the 1960s, and it has been continued for decades due to arguably both human and natural causes. However, the change of the Aral Sea in the post-Soviet era and its correlations with other changes in the extent of the basin have yet to be fully investigated. Here, we studied the land cover dynamics of the entire Aral Sea basin during 1992-2015 from the perspective of the surrounding environment, in order to investigate the causes of the Aral Sea further shrinkage in recent years. We used the annual Climate Change Initiative (CCI) land cover dataset to provide a spatiotemporally consistent delineation of land cover throughout the period. We found that: (1) In recent years, the Aral Sea continued shrinkage, approximately 50.38% of its water area in 1992 had dried out and turned into bare land by 2015. (2) The cultivated land area remained stable with a slight increase during the period, suggesting that no large-scale abandonment or expansion of farming extent occurred in the post-Soviet era. (3) Among other land types, urban areas are small and slightly expand at a rate of 0.024 × 10⁴ km²/year, suggesting urbanization, and likely contribute to more water consumption. Our investigation also found that climate warming increased the upstream runoff, which has a positive effect on the water supply of the Aral Sea. The impact of human activity on the Aral Sea is more pronounced than climate change. Therefore, the continued shrinkage of the Aral Sea was likely due to high water consumption of agriculture continues to exert the influence that existed in the 1960s. Other factors, such as urbanization have exacerbated this effect. The study examined the continued shrinkage of the Aral Sea in post-Soviet era, to provide an insight into the driving factors of the complex and still controversial Aral Sea crisis.