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The purpose of this paper is to develop user friendly software with the minimum error and maximum performance in a form of remote sensing satellites evaluation software for estimation of weights and ranks of the remote sensing satellite plans, to decrease risk of management decisions.

The analytic hierarchy process (AHP) as a comprehensive framework for strategic decision making is used to arrive at the weights of criteria and sub‐criteria of remote sensing satellites. The Ms‐Access software is written to compute the ranks of the remote sensing satellite plans based on the relative weights of inputs and then, the outputs from AHP are shown as a numerical graph and generates the Ms‐Access database.

One of the main objectives of this paper is an attempt to access this skill that compare several remote sensing satellite plans on quantity and quality point of view by several effective criteria such as mass, power consumption and cost of satellites, in addition to the remote sensing subsystem, communication subsystem, telemetry, tracking and control subsystem, attitude determination control subsystem and their own sub‐criteria.

It is hard in just one paper, to gather lots of information about remote sensing satellite systems, use a new methodology that is unknown for aerospace engineering, and talk about an innovative software.

This paper provides helpful evaluating software which has a data bank that it is very useful and impartial advice for space strategy's managing organization to compare several plans.

This study provides low cost, time‐saving, and high‐performance remote sensing satellite evaluation software and gives valuable information and guidelines which help management decisions of aerospace organization.

This paper aims to present a novel feature design that is able to precisely describe salient objects in images. With the development of space survey, sensor and information acquisition technologies, more complex objects appear in high-resolution remote sensing images. Traditional visual features are no longer precise enough to describe the images.

A novel remote sensing image retrieval method based on VSP (visual salient point) features is proposed in this paper. A key point detector and descriptor are used to extract the critical features and their descriptors in remote sensing images. A visual attention model is adopted to calculate the saliency map of the images, separating the salient regions from the background in the images. The key points in the salient regions are then extracted and defined as VSPs. The VSP features can then be constructed. The similarity between images is measured using the VSP features.

According to the experiment results, compared with traditional visual features, VSP features are more precise and stable in representing diverse remote sensing images. The proposed method performs better than the traditional methods in image retrieval precision.

This paper presents a novel remote sensing image retrieval method based on VSP features.

The importance of thoroughly monitoring the state of the environment in Mediterranean ecosystems has long been recognised. With regard to the spatial extension of large areas threatened by various degradation processes it becomes obvious that terrestrial observation alone is hardly able to cope with this task. Remote sensing with air‐ or spaceborne sensor systems provides a comprehensive spatial coverage, is intrinsically synoptic, and collects objective, repetitive data and is thus ideally suited for monitoring environmentally sensitive areas. The major problem associated with its use is to quantitatively interpret a measured signal that has interacted with remote objects in terms of the properties of these objects. In parallel to the advances in remote sensing geographical information systems (GIS) have emerged as a fully functional support for resource management tasks. As an example for tracing and analysing environmental change with coupled remote sensing and GIS approaches we present a case study on the island of Crete which was carried out in the framework of research programmes supported by the European Union. Although it is known that grazing in Crete dramatically increased during the last two decades, it was not well understood how grazing pressure differs spatially and in how far it altered the landscape of Crete. One of the major rangeland areas of central Crete, the Psiloritis Mountains, have been selected to serve as a test site for answering these questions. On the basis of an extended Landsat‐TM and ‐MSS data set acquired between 1977 and 1996 it has been shown that time series analysis techniques based on vegetation fractions derived from spectral unmixing can substantiate a spatio‐temporal interpretation of degradation processes. In areas under massive grazing pressure such processes can be linked to the respective driving forces by GIS‐based analyses of natural and socio‐economic boundary conditions.

This paper examines articles published between 1972‐1998 that utilize remote sensing in hazard and disaster research. Delineating trends in the production and content of the articles may encourage those who are performing or contemplating such research to alter and/or expand their analyses in new or alternate directions. The review of articles indicates that the technique is primarily used to detect, identify, map, survey and monitor existing hazards and/or their effects. Secondary goals are to provide damage assessments, improve planning, or provide data for mitigation, preparation, relief, response, and warning efforts. Articles addressing hazard/disaster process modeling have rarely been published. It is suggested that if remote sensing is to be used more effectively to reduce unnecessary suffering from damaging environmental events, hazard and/or disaster process models be incorporated into future research.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

This paper aims to analyze the application of remotely piloted aircraft (RPA) for remote oil spill sensing.

This paper is an analysis of RPA strong points.

To increase the accuracy and eliminate potentially false contamination detection, which can be caused by external factors, an oil thickness measurement algorithm is used with the help of the multispectral imaging that provides high accuracy and is versatile for any areas of water and various meteorological and atmospheric conditions.

SWOT analysis of implementation of RPA for remote sensing of oil spills.

The use of RPA will improve the remote sensing of oil spills.

The concept of oil spills monitoring needs to be developed for quality data collection, oil pollution control and emergency response.

The research covers the development of a method and design of a device intended for taking samples and determining the presence of oil contamination in an aquatorium area; the procedure includes taking a sample from the water surface, preparing it for transportation and delivering the sample to a designated location by using the RPA. The objective is to carry out the analysis of remote oil spill sensing using RPA. The RPA provides a reliable sensing of oil pollution with significant advantages over other existing methods. The objective is to analyze the use of RPA employing all of their strong points. In this paper, technical aspects of sensors are analyzed, as well as their advantages and limitations.

The purpose of this paper is to show the impact of digital agriculture on food supply chain, research trend, emphasis and implications for future research.

The paper analyzes how the digital technology reshapes the production, assembly, transaction, retail and logistics. Impact from each main technological progress is discussed.

First, digital agriculture develops quickly and changes all parts of the food supply chain. Second, while many technological progresses show their impacts in agriculture and food sector, e-commerce and progress of artificial intelligence show its comprehensive impact on the argi-food sector.

The paper shows the technological trend and progress in food and agriculture sector. Researchers focusing on agricultural economics and agribusiness should pay attention to recent developments in the real world, know the recent developments from other disciplines, get more data for empirical research and show the impact of digital agriculture on consumer's preference and social welfare.

The purpose of this paper is to analyze the characteristics of spatio-temporal dynamics and the evolution of land use change is essential for understanding and assessing the status and transition of ecosystems. Such analysis, when applied to Horqin sandy land, can also provide basic information for appropriate decision-making.

By integrating long time series Landsat imageries and geographic information system (GIS) technology, this paper explored the spatio-temporal dynamics and evolution-induced land use change of the largest sandy land in China from 1983 to 2016. Accurate and consistent land use information and land use change information was first extracted by using the maximum likelihood classifier and the post-classification change detection method, respectively. The spatio-temporal dynamics and evolution were then analyzed using three kinds of index models: the dynamic degree model to analyze the change of regional land resources, the dynamic change transfer matrix and flow direction rate to analyze the change direction, and the barycenter transfer model to analyze the spatial pattern of land use change.

The results indicated that land use in Horqin sandy land during the study period changed dramatically. Vegetation and sandy land showed fluctuating changes, cropland and construction land steadily increased, water body decreased continuously, and the spatial distribution patterns of land use were generally unbalanced. Vegetation, sandy land and cropland were transferred frequently. The amount of vegetation loss was the largest. Water body loss was 473.6 km², which accounted for 41.7 per cent of the total water body. The loss amount of construction land was only 1.0 km². Considerable differences were noted in the rate of gravity center migration among the land use types in different periods, and the overall rate of construction land migration was the smallest. Moreover, the gravity center migration rates of the water body and sandy land were relatively high and were related to the fragile ecological environment of Horqin sandy land.

The results not only confirmed the applicability and effectiveness of the combined method of remote sensing and GIS technology but also revealed notable spatio-temporal dynamics and evolution-induced land use change throughout the different time periods (1983-1990, 1990-2000, 2000-2010, 2010-2014, 2014-2016 and 1983-2016).

This article aims to compare the LiDAR handheld mobile laser scanner (HMLS) scans with traditional survey methods, as the tree gauge and the hypsometer, to study the efficiency of the new technology in relation to the accuracy of structural forest attributes estimation useful to support a sustainable forest management.

A case study was carried out in a high forest located in Tuscany (Italy), by considering 5 forest types, in 20 different survey plots. A comparative analysis between two survey methods will be shown in order to verify the potential limits and the viability of the LiDAR HMLS in the forest field.

This research demonstrates that LiDAR HMLS technology allows to obtain a large amount of valuable data on forest structural parameters in a short span of time with a high level of accuracy and with obvious impact in terms of organisational efficiency.

Findings could be useful for forest owners highlighting the importance of investing in science and technology to improve the overall efficiency of forest resources management.

This article adds to the current knowledge on the precision forestry topic by providing insight on the feasibility and effectiveness of using precision technologies for monitoring forest ecosystems and dynamics. In particular, this study fills the gap in the literature linked to the need to have practical examples of the use of innovative technologies in forestry.

The selection of wavelength region and number of bands is a research problem for remote sensing experts for utilization of data provided by the sensor system. The present study proposes to make an evaluation for optimum band selection and classification accuracy.

The entropy, brightness value overlap index (BVOI), optimum index factor (OIF) and spectral separability analysis, i.e. Euclidean distance (ED), divergence, transformed divergence (TD) and Jefferies‐Matusita (JM) distance and accuracy of MLC classification were carried out. For the present study Terra ASTER, Landsat ETM⁺ and IRS 1D LISS III dataset has been used. The first three methods were for the spectral evaluation of the three satellite data used and for determination of information content, variance and spectral overlap among the classes present in the natural and man‐made landscape. The fourth method is for selection of spectral band combinations with highest separability of classes using divergence matrices. These band combinations are selected for the classification and subsequent accuracy assessment.

The OIF values are clearly indicating that the performance of ASTER data is the best, having the lowest correlation between the bands; hence the separability of the feature is also highest, while LISS III have shown high correlation between the bands, with the poor separability of the features. Landsat ETM⁺ data are in between these two sensors, better than LISS III but poorer than ASTER. The BVOI outputs of the three datasets of man‐made landscape show that band 3 of ASTER has the least overlap of the classes, followed by band 4 of ETM⁺. Very high overlap of the classes has been found in LISS III data. It has been found from spectral separability analysis of all the three datasets for the man‐made landscape that ASTER data with band combination of spectral bands 123468 contains the highest value of all the measures of spectral separability, i.e. ED (291.72), divergence (2,133.37), TD (2,000.00) and JM distance (1,414.10).

It can be inferred from the present study that spectral resolution plays a very important role in discrimination of vegetation features. ASTER data which are with the highest number of the bands amongst the satellite data used had shown highest classification accuracy, while LISS III data with lowest number of bands had shown lowest accuracy, and Landsat ETM⁺ stood in between the two sensors.

It is important to evaluate the sensor systems and their spectral regions for discrimination of vegetation features. The number of bands present in a particular sensor and the spectral regions used in it are some of the crucial factors which decide the usefulness of the data for different applications, including vegetation‐related studies. The selection of spectral wavelength region, i.e. spectral bands and the sensor system, presents the research problem for remote sensing experts to suggest the best spectral regions and satellite sensor for the discrimination of the vegetation features in different landscapes, namely man‐made and natural.

In the present study all the three datasets are extensively examined and tested for their vegetation discrimination capabilities using well‐established methodologies. All the parameters applied on the datasets revealed that spectral resolution definitely plays a role in the performance of the data as far as discrimination of features is concerned both in natural and man‐made landscape with desirable accuracy.