Jian-Jun Wang

Estimation of suspended sediment concentrations using Terra MODIS: an example from the Lower Yangtze River, China.

Traditional measurements of suspended sediment concentrations (SSC) through in-situ sampling in rivers are expensive and time-consuming to perform. Thus, these methods cannot provide continuous SSC records. Although remote sensing has been used for SSC estimation, little research has been undertaken on inland rivers, especially for highly turbid rivers like the Yangtze. Previous studies have proposed Landsat TM/ETM+ Band 4 as a spectral SSC indicator for the Yangtze, but its limitation on temporal resolution is insufficient for the study of dynamic changes of sediment. This paper presents a method of estimating SSC of the Yangtze at Jiujiang using time-series satellite data of high temporal resolution Terra MODIS. It was found that differences in water reflectance between Band 2 and Band 5 could provide relatively accurate SSC estimation even when in-situ atmospheric conditions were unknown. After cross-validation, mean absolute relative error (ARE) and relative root mean square error (RRMSE) were relatively low (i.e., 25.5% and 36.5%, respectively). This empirical relationship was successfully applied to the estimation of SSC at Datong in the Lower Yangtze River, although the SSC values were generally underestimated. This study suggests that Terra MODIS could be used to estimate SSC in large turbid rivers, although some influencing factors require further study to improve the accuracy of SSC estimation.

Sediment loads response to climate change: A preliminary study of eight large Chinese rivers

Abstract Climate change characterized by increasing temperature is able to affect precipitation regime and thus surface hydrology. However, the manner in which river sediment loads respond to climate change is not well understood, and related assessment regarding the effect of climate change on sediment loads is lacking. We present a quantitative estimate of changes in sediment loads (from 1.5 Gt yr−1 pre-1990 to 0.6 Gt yr−1 from 1991–2007) in response to climate change in eight large Chinese rivers. Over the past decades, precipitation change coupled with rising temperatures has played a significant role in influencing the sediment delivery dynamics, although human activities, such as reservoir construction, water diversion, sand mining and land cover change, are still the predominant forces. Lower precipitation coupled with rising temperatures has significantly reduced sediment loads delivered into the sea in semi-arid climates (4–61%). In contrast, increasingly warmer and wetter climates in subtropical zones has yielded more sediment (0.4–11%), although the increase was offset by human impact. Our results indicate that, compared with mechanical retention by reservoirs, water reduction caused by climate change or human withdrawals has contributed more sediment reduction for the rivers with abundant sediment supply but limited transport capacity (e.g., the Huanghe). Furthermore, our results indicate that every 1% change in precipitation has resulted in a 1.3% change in water discharge and a 2% change in sediment loads. In addition, every 1% change in water discharge caused by precipitation has led to a 1.6% change in sediment loads, but the same percentage of water discharge change caused largely by humans would only result in a 0.9% change in sediment loads. These figures can be used as a guideline for evaluating the responses of sediment loads to climate change in similar climate zones because future global warming will cause dramatic changes in water and sediment in river basins worldwide at rates previously unseen.

Remote sensing of suspended sediment concentrations of large rivers using multi-temporal MODIS images: an example in the Middle and Lower Yangtze River, China

Conventional measurements of suspended sediment concentration (SSC) are expensive, especially for large river systems. This study aims to examine the potential of estimating SSC of large rivers using high temporal resolution Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data. In contrast to a small number of the samples used by previous studies on remote sensing of SSC, a large number of samples (n = 153) obtained on 63 dates at five sites across the entire Middle and Lower Yangtze River were employed to investigate the relationship between SSC and the water reflectance of MODIS images. The water reflectance difference between Bands 2 and 5 provided a relatively accurate SSC estimate after atmospheric correction, with 25% mean absolute relative error and 29.7% relative root mean square error. The errors were lower for larger SSC values. Thus, there is a potential that the Terra MODIS could be employed to estimate SSC frequently for large turbid river systems.

Suspended sediment concentrations estimate in highly turbid rivers: a field spectral survey

Construction projects such as dams on large international turbid rivers often attract wide attention due to their possible negative environmental impacts. However, accurate evaluation of environmental effects is often hindered by the lack of sediment measurements. This study aimed to investigate whether suspended sediment concentrations (SSCs) could be estimated from spectral measurements and this may provide critical instructional assistance for estimating SSC directly from remote-sensing data. Although high SSC values are common in large Asian rivers, they have not been involved in any previous studies yet. Hence, this study investigated the spectral signature of highly turbid waters through a field spectral survey along two international rivers in Southeast Asia. Subsequently, SSC was correlated with measured spectral data, and optimal spectral SSC indicators that could provide relatively accurate SSC estimates in turbid waters were identified.

Unsupervised multiple endmember spectral mixture analysis-based detection of opium poppy fields from an EO-1 Hyperion image in Helmand, Afghanistan

Since 1992, Afghanistan has gradually become the primary illicit opium producer in the entire world. To efficiently eradicate the opium poppy, it is crucial for the United Nations Office on Drugs and Crime (UNODC) and Ministry of Counter Narcotics of Afghanistan to monitor opium poppy cultivation timely. In situ detection of opium fields, however, is often expensive, time-consuming and dangerous in Afghanistan. To overcome the constraints of inaccessibility of opium fields, high-resolution (≤1 m) images, like pan-sharpened IKONOS, have been applied in previous studies. Unfortunately, these high-resolution images are expensive when monitoring a large area. In contrast, EO-1 Hyperion imagery, the only source of spaceborne hyperspectral data, has a coarse resolution (30 m), but it is free of charge. Moreover, Hyperions large number of channels may increase the detection capability of subpixel size targets. Until now, however, little research has been found that identified opium fields from spaceborne or aerial hyperspectral images. Therefore, this study attempts to detect opium fields from a Hyperion image covering a study area in Southwest Afghanistan in a situation where training samples were not available. A proposed methodology based on unsupervised endmember-selection and multiple-endmember spectral mixture analysis can detect opium fields directly from the Hyperion image. The number of poppy pixels was overestimated by 12%.

An unsupervised mixture-tuned matched filtering-based method for the remote sensing of opium poppy fields using EO-1 Hyperion data: an example from Helmand, Afghanistan

ABSTRACT Remote sensing has special advantages to monitor drug production that causes serious problems to global society. The widely used high spatial resolution images are too costly to make the full coverage of the opium poppy fields in a large area. Although the hyperspectral imagery acquired by Earth Observing-1 (EO-1) Hyperion that is free with medium spatial resolution has been employed, the used unsupervised multiple endmember spectral mixture analysis (MESMA)-based method is time-consuming for a large area. The present study used an unsupervised mixture-tuned matched filtering (MTMF)-based method to detect poppy fields from a Hyperion image covering a study area in Helmand, Afghanistan, and it achieved the producer’s, user’s and overall accuracies of 61%, 73% and 76%, as well as the kappa coefficient of 0.48. This method worked over 10 times faster than the MESMA-based method with similar detection accuracies. This MTMF-based method provides a potential alternative for the United Nations and the Afghanistan government to monitor opium poppy cultivation in Afghanistan.