Due to the unique feature of the three-dimensional convolution neural network, it is used in image classification. There are some problems such as noise, lack of labeled samples, the tendency to overfitting, a lack of extraction of spectral and spatial features, which has challenged the classification. Among the mentioned problems, the lack of experimental samples is the main problem that has been used to solve the methods in recent years. Among them, convolutional neural network-based algorithms have been proposed as a popular option for hyperspectral image analysis due to their ability to extract useful features and high performance. The traditional convolutional neural network (CNN) based methods mainly use the two-dimensional CNN for feature extraction, which makes the interband correlations of HSIs underutilized. The 3-D-CNN extracts the joint spectral-spatial information representation, but it depends on a more complex model. To address these issues, the report uses a 3-D fast learning block (depthwise separable convolution block and a fast convolution block) followed by a 2-D convolutional neural network was introduced to extract spectral-spatial features. Using a hybrid CNN reduces the complexity of the model compared to using 3-D-CNN alone and can also perform well against noise and a limited number of training samples. In addition, a series of optimization methods including batch normalization, dropout, exponential decay learning rate, and L2 regularization are adopted to alleviate the problem of overfitting and improve the classification results. To test the performance of this hybrid method, it is performed on the Salinas, University Pavia and Indian Pines datasets, and the results are compared with 2-D-CNN and 3-D-CNN deep learning models with the same number of layers.

We employed the Cellular Automata Markov (CA-Markov) integrated technique to study land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab, Pakistan. We plotted the distribution of the LULC throughout the desert terrain for the years 1990, 2006, and 2022. The Random Forest methodology was utilized to classify the data obtained from Landsat 5 (TM), Landsat 7 (ETM+), and Landsat 8 (OLI/TIRS), as well as ancillary data. The LULC maps generated using this method have an overall accuracy of more than 87%. CA-Markov was utilized to forecast changes in land usage in 2022, and changes were projected for 2038 by extending the patterns seen in 2022. A CA-Markov-Chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2038. Analysis of urban sprawl was carried out by using the Random Forest (RF). Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 8.12 to 12.26 km² and from 18.10 to 28.45 km² in 2022 and 2038, as inferred from the changes occurred from 1990 to 2022. The LULC projected for 2038 showed that there would be increased urbanization of the terrain, with probable development in the croplands westward and northward, as well as growth in residential centers. The findings can potentially assist management operations geared toward the conservation of wildlife and the eco-system in the region. This study can also be a reference for other studies that try to project changes in arid are as undergoing land-use changes comparable to those in this study.

Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field. The purpose of this study is to study the shortcomings and strengths of combined radar data and optical images to identify the type of crops in Tarom region (Iran). For this purpose, Sentinel 1 and Sentinel 2 images were used to create a map in the study area. The Sentinel 1 data came from Google Earth Engine’s (GEE) Level-1 Ground Range Detected (GRD) Interferometric Wide Swath (IW) product. Sentinel 1 radar observations were projected onto a standard 10-m grid in GRD output. The Sen2Cor method was used to mask for clouds and cloud shadows, and the Sentinel 2 Level-1C data was sourced from the Copernicus Open Access Hub. To estimate the purpose of classification, stochastic forest classification method was used to predict classification accuracy. Using seven types of crops, the classification map of the 2020 growth season in Tarom was prepared using 10-day Sentinel 2 smooth mosaic NDVI and 12-day Sentinel 1 back mosaic. Kappa coefficient of 0.75 and a maximum accuracy of 85% were reported in this study. To achieve maximum classification accuracy, it is recommended to use a combination of radar and optical data, as this combination increases the chances of examining the details compared to the single-sensor classification method and achieves more reliable information.

Recent continuous droughts and decreasing ground water tables have prompted efforts to improve irrigation schedules and introduce crops that need less water. A study was recently conducted to determine suitable zones for saffron in Miyaneh using Landsat-8 images and the weighted linear combination (WLC) method. Climatic and geographical indices for saffron cultivation in the region were for soil type, slope, soil moisture, and soil salinity. Parameters such as 30 years of data on climate, soil, and water conditions were collected from synoptic and climatologic stations such as Tabriz. Then, parameters were weighted using WLC for importance in each region. The data were transferred to expert choice and clustered, rated, and integrated to produce the last layer. The results showed that the southeastern and northwestern regions of Miyaneh, especially the banks of rivers and catchments, were identified as suitable places for saffron cultivation and that 28% of the area is in the suitable class, 36% in the relatively moderately suitable class, 20% in the critical suitability class, and the rest of the area, which covers about 16% of the area, is in the non-suitable class. Therefore, if it is possible to identify favorable areas for saffron cultivation according to the climatic requirements and it is possible in practice to achieve higher performance per unit area, that in itself will contribute to improved economic conditions and levels of income for farmers. Due to the special characteristics of saffron, substituting it for the cultivation of crops with high water requirements, such as onions, potatoes, tomatoes, etc., will help reduce water consumption.

The increasing exposure to extreme heatwaves in urban areas from both climate change and the urban heat island (UHI) effect poses multiple threats and challenges to human society. Despite a growing number of studies focusing on extreme exposure, research advances are still limited in some aspects such as oversimplification of human exposure to heatwaves and neglect of perceived temperature as well as actual body comfort, resulting in unreliable and unrealistic estimates of future results. In addition, little research has performed comprehensive and fine-resolution global analyses in future scenarios. In this study, we present the first global fine-resolution projection of future changing urban population exposure to heatwaves by 2100 under four shared socioeconomic pathways (SSPs) considering urban expansion at global, regional, and national scales. Overall, global urban population exposure to heatwaves is rising under the four SSPs. Temperate and tropical zones predictably have the greatest exposure among all climate zones. Coastal cities are projected to have the greatest exposure, followed closely by cities at low altitudes. Middle-income countries have the lowest exposure and the lowest inequality of exposure among countries. Individual climate effects contributed the most (approximately 46.4%) to future changes in exposure, followed by the interactive effect between climate and urbanization (approximately 18.5%). Our results indicate that more attention needs to be paid to policy improvements and sustainable development planning of global coastal cities and some low-altitude cities, especially in low- and high-income countries. Meanwhile, this study also highlights the impact of continued future urban expansion on population exposure to heatwaves.

This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers’ citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.