Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.     

Estimating fractional cover of crop,crop residue,and soil in cropland using broadband remote sensing data and machine learning

The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.     

The 2019 Brumadinho tailings dam collapse: Possible cause and impacts of the worst human and environmental disaster in Brazil

On 25th January 2019, the tailings dam of the Brumadinho iron mine operated by Vale S/A failed catastrophically. The death toll stood at 259 and 11 people remained missing as of January 2020. This tragedy occurred three years after Mariana’s tailings dam rupture – the most significant tailing dam disaster in Brazilian history. Thus far, a systematic investigation on the cause and effect of the failure has yet to be conducted. Here, we use satellite-driven soil moisture index, multispectral high-resolution imagery and Interferometric Synthetic Aperture Radar (InSAR) products to assess pre-disaster scenarios and the direct causes of the tailings dam collapse. A decreasing trend in the moisture content at the surface and the full evanescence of pond water through time (2011–2019) suggest that the water was gradually penetrating the fill downwards and caused the seepage erosion, saturating the tailings dam. Large-scale slumping of the dam (extensional failure) upon the rupture indicates that the materials of the fill were already saturated. InSAR measurements reveal a dramatic, up to 30 cm subsidence in the dam (at the rear part) within the past 12 months before the dam collapse, signifying that the sediments had been removed from the fill. Although the information on the resistance level of the tailings dam to infiltrations is not available, these pieces of evidence collectively indicate that the seepage erosion (piping) is the primary cause for the chronic weakening of the structure and, hence, the internal “liquefaction” condition. Upon the collapse, the fully saturated mud tailings flowed down the gentle slope area (3.13 × 10⁶ m²), where 73 % were originally covered by tree, grass or agricultural tracts. The toxic mud eventually reached the Paraopeba River after travelling 10 km, abruptly increasing the suspended particulate matter (SPM) concentration and the toxic chemical elements in the river, immediately affecting the local livelihoods that depend on its water. The Paraopeba River is a major tributary of the San Francisco River, the second-longest river in Brazil reaching the Atlantic Ocean. We anticipate that the environmental repercussions of this toxic seepage will be felt throughout the entire basin, especially riverine communities located downstream.     

Satellite-based monitoring and statistics for raft and cage aquaculture in China’s offshore waters

Based on Landsat 8 remote sensing images, a combination of an unsupervised classification algorithm and artificial review was used to extract areas for Chinese offshore raft and cage aquaculture in 2018. The results of the extraction showed that China’s 2018 coastal zone raft aquaculture area comprised 194,110 ha, of which the province having the largest raft aquaculture area was Jiangsu (28.77 %), followed by Fujian (20.42 %) and Shandong (13.11 %). The cage aquaculture area covered 57,847,799 square meters, of which the provinces with the largest cage aquaculture area were Fujian (64.81 %), Guangdong (17.45 %), and Liaoning (5.63 %). In addition, by combining high-resolution remote sensing image visual interpretation and field investigation, the classification of 1200 sample points in four regions was determined, and the accuracy of the aquaculture area extraction was found to be 87.35 %. The extraction results can be used not only to evaluate China’s aquaculture production but also offer significant reference value for scientific planning related to sea use, ecological environmental protection, and marine disaster prevention and mitigation.     

GIS与遥感支持下的南黄海辐射沙脊群现代演变趋势分析

南黄海辐射沙脊群是世界罕见的大型水下泥沙堆积体，具有独特的动力地貌组合。利用1988-1995年的LANDSATTM遥感影像，ERS1SAR影像及1979年编绘地形资料，综合应用GIS与遥感方法，对这一区域在20世纪80-90年代的变化及演变趋势作出分析，其基本演变特征与趋势表现为；沙脊群枢纽地区处于不断的增长，扩张过程；南部区域堆积与侵蚀作用较弱，沿岸潮滩向海淤进；而北部区域变化较强烈，并继续脊槽相间的模式，所取得的结果与该区域动力地貌研究结果相吻合。     

黄、东海陆架区悬浮体输运的时空变化规律

利用ＮＯＡＡ卫星１９９５年１月－１９９７年２月的海洋遥感资料系统的分析和研究了黄、东海陆架区悬浮体向深海输运的时间和空间变化规律,并对其动力机制进行了分析和探讨,研究结果表明,黄、东海悬浮体在１１－３月份向外海输运阶段,５－９月份则同近海为内储阶段,４月份为输变退的月份,１０月份则是退变输的月份,其中３月份黄、东海悬浮体向东海深海输运得最远,９月份则向岸退得最远;黄、东海表面悬浮体向东海的输运范围一般皆位于１００ｍ等深线以内,并很难越过黑潮水系,而输向深海的悬浮体又大都在黄海暖流的作用下回输黄海,其大部输向韩国西南海域。     

一种基于GPU和内存映射文件的高分辨率遥感图像快速处理方法

高分辨率遥感图像处理经常面临程序执行时间过长和内存空间不足的问题,虽然并行计算技术可以提高遥感图像的处理速度,但是无法降低算法占用的巨大内存空间。为了解决这一问题,本文提出了一种利用CUDA和内存映射文件的高分辨率遥感图像快速处理方法,并以K-Means算法为例进行了实现。其中,CUDA技术可以有效利用GPU强大的并行计算能力,而内存映射文件技术降低了磁盘I/O速度较慢对算法性能的影响。实验结果表明,本文方法比传统K-Means聚类算法计算速度提高了30倍左右,内存使用量降低了90%以上。     

基于GIS/RS的海岸带生态环境变化研究综述

近年来,随着沿海地区经济建设的脚步的不断加快,人们逐渐加大了对海岸带环境资源的开发利用。海岸带填海造陆、滩涂围垦等人类活动的增多使得区域生物及非生物物质发生了剧烈的改变,进而对海岸带生态环境产生了重要的影响。基于此,各国学者尝试通过一系列的技术手段与方法模型对海岸带生态环境时空变化进行相关探究。其中,遥感与地理信息系统技术作为生态环境的重要分析工具,在区域生态环境变化的评估与监测中有着较为广泛的应用。在海岸带生态环境变化的探究中,由于研究目的、角度以及方向的不同,相关学者所使用的研究方法及模型也存在着一定的差别。本文结合海岸带生态环境研究现状,将海岸带生态环境变化研究分成海岸带土地利用/覆被变化研究、景观格局变化研究、海岸线变迁变迁研究、植被变化研究、水土环境表征因子变化研究以及基于指标体系的生态环境变化研究6个主要部分,并分别从这6个部分出发,对海岸带生态环境变化研究的技术、方法与模型进行归纳总结,从而提出未来我国海岸带生态环境变化研究中需要解决的问题以及重点研究方向,为今后海岸带生态环境的深入研究打下坚实的基础。     

基于遥感水边线的潮滩面积提取方法研究

潮滩作为动态变化的后备土地资源,对其研究具有重要意义。文章以江苏省如东县为研究区,使用环境资源卫星(HJ1A/1B)影像为数据源,应用面向对象技术创建分类规则,实现批量半自动提取水边线,随后利用DSAS软件处理水边线集获得潮位点集,基于最外边界法实现高(低)潮位点的提取,进而获得潮滩范围,估算的如东县潮滩面积为55 182hm~2。最后利用误差矩阵进行精度验证(Kappa系数为0.945)。结果表明:该方法对于潮滩面积提取具有适用性,无论在空间域还是时间域都具有一定的推广性。     

基于长时间序列遥感数据的阿伯德尔国家公园 生态环境质量评价

阿伯德尔国家公园位于号称肯尼亚“水塔”的阿伯德尔山脉上,是肯尼亚重要的旅游景区和珍稀动植物保护区,开展该地生态环境质量评价对肯尼亚水资源保护与可持续发展具有重要的意义。本研究基于1987-2018年的Landsat系列遥感数据,通过计算综合遥感生态指数RSEI,结合趋势线分析法对阿伯德尔国家公园近32年间生态环境质量变化进行客观定量分析。研究结果显示：① 1987-2018年研究区RSEI均值从0.62下降到0.51,生态环境质量退化的区域面积为551.52 km ²,占总面积的71.85%,表明在气候变化和人类活动影响下,1987-2018年研究区的环境状况呈恶化趋势;②环境质量显著恶化的区域主要分布于亚高山植被区和东部低海拔森林覆盖区,亚高山地区植被生态系统结构单一,稳定性差,对气候变化和人类活动更为敏感;而东部区域存在过度开发,非法砍伐森林等现象,且旅游开发强度大,环境恶化与人类活动密切相关。