基于Sentinel-1/2数据特征优选的冬小麦种植区识别方法研究

为了提高冬小麦种植区识别精度,本文基于谷歌地球引擎(Google Earth Engine, GEE)平台和随机森林算法,对比雷达和光学遥感数据对冬小麦提取效果的差异,并对多类特征变量进行重要性分析,研究特征优选对冬小麦识别精度的影响。选取2019年3—5月冬小麦关键生育期的Sentinel-1和Sentinel-2影像为数据源,构建Sentinel-1的极化特征和纹理特征以及Sentinel-2的光谱特征、植被指数特征、植被指数变化率特征共5类特征变量;设置不同数据源和不同特征组合的冬小麦种植区提取方案;对方案中特征变量进行优选,得出最优特征组合,利用最优特征组合对河南省驻马店市冬小麦种植区进行提取。结果表明,无论是否进行特征优选,基于多源遥感数据的冬小麦识别精度均优于仅采用光学或雷达数据的精度;经过特征优选后,各方案的分类精度均有不同程度的提升,说明多源数据特征变量组合和特征优选均能够提高分类精度。不同月份和类型的特征变量对分类精度的贡献率不同,贡献率由大到小为4月、3月和5月;贡献率由大到小的特征类型为极化特征、植被指数变化率特征、植被指数特征、光谱特征和纹理特征。基于多源数据特...     

新疆裕民县夏季草地合理放牧强度监测报告

通过开放式的定点样方笼移动法,监测当前放牧强度下草地的产草量与不同阶段的存量,以此估算夏草地的合理放牧强度。监测结果显示：裕民县禾草＋苔草＋杂类草类型夏草地的牧草产量为鲜草5238kg／hm^2,干草2017kg／hm^2实际利用率约为84％,超载率约40％,合理放牧强度为9．96羊单位／hm^2,放牧90d。     

An assessment of Sentinel-1 synthetic aperture radar,geophysical and topographical covariates for estimating topsoil particle-size fractions

Data derived from synthetic aperture radar (SAR) are widely employed to predict soil properties, particularly soil moisture and soil carbon content. However, few studies address the use of microwave sensors for soil texture retrieval and those that do are typically constrained to bare soil conditions. Here, we test two statistical modelling approaches—linear (with and without interaction terms) and tree-based models, namely compositional linear regression model (LRM) and random forest (RF)—and both nongeophysical (e.g., surface soil moisture, topographic, etc) and geophysical-based (electromagnetic, magnetic and radiometric) covariates to estimate soil texture (sand %, silt % and clay %), using microwave remote sensing data (ESA Sentinel-1). The statistical models evaluated explicitly consider the compositional nature of soil texture and were evaluated with leave-one-out cross-validation (LOOCV). Our findings indicate that both modelling approaches yielded better estimates when fitted without the geophysical covariates. Based on the Nash–Sutcliffe efficiency coefficient (NSE), LRM slightly outperformed RF, with NSE values for sand, silt and clay of 0.94, 0.62 and 0.46, respectively; for RF, the NSE values were 0.93, 0.59 and 0.44. When interaction terms were included, RF was found to outperform LRM. The inclusion of interactions in the LRM resulted in a decrease in NSE value and an increase in the size of the residuals. Findings also indicate that the use of radar-derived variables (e.g., VV, VH, RVI) alone was not able to predict soil particle size without the aid of other covariates. Our findings highlight the importance of explicitly considering the compositional nature of soil texture information in statistical analysis and regression modelling. As part of the continued assessment of microwave remote sensing data (e.g., ESA Sentinel-1) for predicting topsoil particle size, we intend to test surface scattering information derived from the dual-polarimetric decomposition technique and integrate that predictor into the models in order to deal with the effects of vegetation cover on topsoil backscattering.     

Airborne multispectral imagery and deep learning for biosecurity surveillance of invasive forest pests in urban landscapes

Urban and peri-urban trees in major cities provide a gateway for exotic pests and diseases (hereafter “pests”) to establish and spread into new countries. Consequently, they can be used as sentinels for early detection of exotic pests that could threaten commercial, environmental and amenity forests. Biosecurity surveillance for exotic forest pests relies on monitoring of host trees — or sentinel trees — around high-risk sites, such as airports and seaports. There are few publicly available spatial databases of urban street and park trees, so locating and mapping host trees is conducted via ground surveys. This is time-consuming and resource-intensive, and generally does not provide complete coverage. Advances in remote sensing technologies and machine learning provide an opportunity for semi-automation of tree species mapping to assist in biosecurity surveillance. In this study, we obtained high resolution (≥12 cm), 10-band, multispectral imagery using the ArborCam™ system mounted to a fixed-wing aircraft over Sydney, Australia. We mapped 630 Pinus trees and 439 Platanus trees on-foot, validating their exact location on the airborne imagery using an in-field mapping app. Using a machine learning, convolutional neural network workflow, we were able to classify the two target genera with a high level of accuracy in a complex urban landscape. Overall accuracy was 92.1% for Pinus and 95.2% for Platanus, precision (user’s accuracy) ranged from 61.3% to 77.6%, sensitivity (producer’s accuracy) ranged from 92.7% to 95.2%, and F1-score ranged from 74.6% to 84.4%. Our study validates the potential for using multispectral imagery and machine learning to increase efficiencies in tree biosecurity surveillance. We encourage biosecurity agencies to consider greater use of this technology.