The Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that was shown to have excellent performance for numerous optimization problems. In this paper, a wrapper-feature selection algorithm is proposed based on the Binary Dragonfly Algorithm (BDA). The key component of the BDA is the transfer function that maps a continuous search space to a discrete search space. In this study, eight transfer functions, categorized into two families (S-shaped and V-shaped functions) are integrated into the BDA and evaluated using eighteen benchmark datasets obtained from the UCI data repository. The main contribution of this paper is the proposal of time-varying S-shaped and V-shaped transfer functions to leverage the impact of the step vector on balancing exploration and exploitation. During the early stages of the optimization process, the probability of changing the position of an element is high, which facilitates the exploration of new solutions starting from the initial population. On the other hand, the probability of changing the position of an element becomes lower towards the end of the optimization process. This behavior is obtained by considering the current iteration number as a parameter of transfer functions. The performance of the proposed approaches is compared with that of other state-of-art approaches including the DA, binary grey wolf optimizer (bGWO), binary gravitational search algorithm (BGSA), binary bat algorithm (BBA), particle swarm optimization (PSO), and genetic algorithm in terms of classification accuracy, sensitivity, specificity, area under the curve, and number of selected attributes. Results show that the time-varying S-shaped BDA approach outperforms compared approaches.

/pdf/binary-dragonfly-optimization-for-feature-selection-using-qh5fhun0x7.pdf

Binary dragonfly optimization for feature selection using time-varying transfer functions

Climate change is altering the seasonal distribution, interannual variability and overall magnitude of precipitation. A new global measure of precipitation seasonality is proposed, and application of this method to observations from the tropics shows that increases in variability were accompanied by shifts in seasonal magnitude, timing and duration.

Changes in rainfall seasonality in the tropics

Aims Net primary productivity (NPP) is a key component of the terrestrial carbon cycle. Model simulation is commonly used to estimate regional and global NPP given difficulties to directly measure NPP at such spatial scales. A number of NPP models have been developed in recent years as research issues related to food security and biotic response to climatic warming have become more compelling. However, large uncertainties still exist because of the complexity of ecosystems and difficulties in determining some key model parameters. Methods We developed an estimation model of NPP based on geographic information system (GIS) and remote sensing (RS) technology. The vegetation types and their classification accuracy are simultaneously introduced to the computation of some key vegetation parameters, such as the maximum value of normalized difference vegetation index (NDVI) for different vegetation types. This can remove some noise from the remote sensing data and the statistical errors of vegetation classification. It also provides a basis for the sensitivity analysis of NPP on the classification accuracy. The maximum light use efficiency (LUE) for some typical vegetation types in China is simulated using a modified least squares function based on NOAA/AVHRR remote sensing data and field-observed NPP data. The simulated values of LUE are greater than the value used in the CASA model and less than the values simulated with the BIOME-BGC model. The computation of the water restriction factor is driven with ground meteorological data and remote sensing data, and complex soil parameters are avoided. Results are compared with other studies and models. Important findings The simulated mean NPP in Chinese terrestrial vegetation from 1989-1993 is 3.12 Pg C (1 Pg=1015 g). The simulated NPP is close to the observed NPP, and the total mean relative error is 4.5% for 690 NPP observation stations distributed in the whole country. This illustrates the utility of the model for the estimation of terrestrial primary production over regional scales.

Estimation of net primary productivity of chinese terrestrial vegetation based on remote sensing

Temporal trends of surface urban heat islands and associated determinants in major Chinese cities

Characteristics of Propagation From Meteorological Drought to Hydrological Drought in the Pearl River Basin

Spatial and temporal distribution of rainfall and drought characteristics across the Pearl River basin.

Feature selection is becoming a major component of object-based classification as numerous features of segmented object become available. Although common feature selection methods in object-based classification are acknowledged, wrapper-based methods remain an issue due to the diversity of accuracy assessment methods. This letter presents a new wrapper approach using polygon-based cross validation (CV) to overcome possible bias of object-based accuracy assessment for object-based classification. The new method is a two-step wrapper-based feature selection that involves the integration of: 1) feature importance rank using gain ratio and 2) feature subset evaluation using a polygon-based tenfold CV within a support vector machine (SVM) classifier. Several high-resolution images, including both unmanned aerial vehicle images and ISPRS (International Society for Photogrammetry and Remote Sensing) benchmark test data, were used to test the proposed method. Results show that, with the proposed polygon-based CV SVM wrapper, the mean overall accuracy is significantly higher than with an object-based CV SVM wrapper. Furthermore, the proposed method shows potential for comprehensively considering all types of features instead of only spectral features.

A Novel Wrapper Approach for Feature Selection in Object-Based Image Classification Using Polygon-Based Cross-Validation

Identifying the Determinants of Crop Yields in China Since 1952 and its Policy Implications

. A timely and accurate understanding of the spatial distribution of tea plantations is beneficial for agricultural management and regional sustainable development. However, obtaining detailed distribution data on large-area tea plantations remains challenging owing to limitations in computational capabilities, training data, and workflow design. Utilizing the Google Earth Engine, which provides a catalog of multisource data in a cloud-based environ-ment, we developed a methodology to generate a highly accurate tea plantation map, with a 10-m resolution, for Anhui Province, China, by integrating a random forest model with a progressive model. Our major contribution lies in this hybrid approach, which comprises two major com-ponents: (1) an optimal classification band combination derived from Sentinel-2 products and the digital elevation model filtered by the J-M distance model and (2) a progressive random forest method introduced for tea plantation classification. The experimental results show that our proposed workflow achieved an average classification accuracy of 89.27% for the entire Anhui Province. In addition, this approach is semiautomatic and can effectively reduce the labor required during the generation of training data compared with traditional classification approaches. These findings demonstrate the potential of integrating machine learning and progressive models to produce high-precision remote sensing classification maps. © The Authors. Published SPIE Creative Commons License.

https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-16/issue-2/024509/Mapping-large-area-tea-plantations-using-progressive-random-forest-and/10.1117/1.JRS.16.024509.pdf

Mapping large area tea plantations using progressive random forest and Google Earth Engine

Digital soil maps of different scales have been widely used in the estimates of soil organic carbon (SOC). However, exactly how the scale of the soil map impacts SOC dynamics and the key factors influencing SOC estimations during the map generalization process have rarely been assessed. In this research, a newly available soil database of Zhejiang Province in southeastern China, which contains 2154 geo-referenced soil profiles and six digital soil maps at scales of 1:50,000, 1:250,000, 1:500,000, 1:1,000,000, 1:4,000,000, and 1:10,000,000, and three different linkage methods (i.e., the mean, median, and pedological professional knowledge-based (PKB) methods) were used to evaluate their influence on the estimates of SOC. The findings of our study were as follows: (1) The scale of the soil map was identified as being of crucial importance for regional SOC estimations. (2) The linkage method played an important role in the accurate estimates of SOC, and the PKB method could provide the most detailed information on the spatial variability of SOC estimations. (3) The key factors affecting the estimates of SOC during the map generalization process as the soil map scale decreased from 1:50,000 to 1:10,000,000 were determined, including the changes in the number of soil profiles, the conversions between different soil types, the conversions from non-soils to soils, and the linkage methods of aggregating the SOC density values of soil profiles to represent map units. The results suggest that the most detailed 1:50,000-scale soil map coupled with the PKB method would be the optimal choice for regional SOC estimations in China.

/pdf/effects-of-soil-map-scales-on-estimating-soil-organic-carbon-2ehoqhzn.pdf

Effects of Soil Map Scales on Estimating Soil Organic Carbon Stocks in Southeastern China

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