Timely Plastic-Mulched Cropland Extraction Method from Complex Mixed Surfaces in Arid Regions

Abstract

Plastic mulch is extensively applied in agricultural production in arid regions. It significantly influences the interactions between land and atmosphere by altering underlying surface characteristics. An accurate and timely extraction method for Plastic-Mulched Cropland (PMC) is required to understand land surface energy transfer processes, eco-hydrological cycle, the climate effect of PMC, and in the management of water resources. In this study, we proposed a Timely Plastic-mulched cropland Extraction Method (TPEM) from complex mixed surfaces with multi-source remote sensing data in the Shiyanghe River Basin (SRB), a typical representation of a complex and inhomogeneous arid region in the northwest of China. We defined TPEM in three phases; in the first phase, the spectral characteristic curves were drawn from ground object points labeled by visual interpretation with multi-source remote sensing data. In the second phase, a spectral characteristic analysis of the modified index was proposed to amplify the difference between PMC and non-PMC ground objects. Finally, the Classification and Regression Tree (CART) classifier was used to generate thresholds of indices as PMC extraction rules. The results showed that it can extract the boundary of PMC in large-scale farmland, distinguish PMC from ground objects in complex mixed surfaces, and separate the PMC from desert land that shares same spectral characteristics with PMC. The TPEM is verified to be efficient and robust, with an overall accuracy of 0.9234, quantity disagreement of 0.0541, and allocation disagreement of 0.0224, and outperformed two extensively used PMC extraction methods, especially for timely PMC extraction when satellite data only during the period that ground surface incomplete covered by plastic mulch is available. This study will provide us with an accurate and timely method to extract PMC, especially in the widely distributed complex mixed surfaces.

Prediction of flash flood susceptibility using integrating analytic hierarchy process (AHP) and frequency ratio (FR) algorithms

“…Human is the major manipulator of the ecosystem since their origin on the surface of the earth. Land use methods and land manipulation like agriculture use and deforestation are some of the man-induced causes that affect recurrent floods (Bera et al, 2022;Fu et al, 2022;Haq et al, 2022;Prasad et al, 2022;Ullah et al, 2022). Therefore, awareness of the manipulations made by humans, susceptibility, and risk analysis is pivotal and essential to be done on an earlier basis (Costache et al, 2022;Tariq et al, 2022c;Ghaderizadeh et al, 2022;Jalayer et al, 2022).…”

Rainfall in the Urban Area and Its Impact on Climatology and Population Growth

“…Northeastern Brazil (NEB) has a high spatiotemporal variability of rainfall, with a direct influence on various human activities, mainly in the agricultural sector, tourism, and industry, as well as on the dynamics and phytophysiognomy of existing biomes [1][2][3]. Due to its geographic location, several multiscale meteorological systems, from local to large scale, contribute to rainfall dynamics in the NEB [4][5][6].…”

“…Among the large-scale mechanisms that organize convection on a synoptic scale are the Intertropical Convergence Zone (ITCZ), the South Atlantic Convergence Zone (SACZ), Frontal Systems (FS), High-Level Cyclonic Vortices (HLCV), South Atlantic Subtropical High (SASH), and the Madden-Julian Oscillation (MJO) [10][11][12][13][14]. Mesoscale mechanisms include Trade Wave Disturbances (TWD), Easterly Wave Disturbances (EWD), Mesoscale Convective Complexes (MCC), and sea/land, valley/mountain, and lake/lagoon circulations [5,13,15]. Orographic circulations and local convection constitute microscale phenomena [8].…”

“…In addition, there are modes of climate variability, for example, the El Niño-Southern Oscillation (ENSO) [3,5] and the Atlantic Dipole, currently called Interhemispheric Gradient of Atlantic Sea Surface Temperature (GIASST) [11], which influences the interannual variability of rainfall in the NEB [12,16,17]. Studies show that in El Niño/La Niña years, the NEB has less and/or more annual rainfall compared to climatology [3,6,7].…”

Landslide susceptibility mapping (LSM) of Swat District, Hindu Kush Himalayan region of Pakistan, using GIS-based bivariate modeling

“…An LSM demarcates the region exposed to landslide hazards and classifies the area into different potential hazard zones by using various approaches to manage the landslide hazard in the region of interest. Researchers have developed various GIS-based techniques for landslide modeling (Pourghasemi et al, 2005;Reichenbach et al, 2018;Tian et al, 2021a;Wang P. et al, 2021Wang P. et al, , 2022Fu et al, 2022).…”