Research

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Open AccessArticle A Regional Land Use Drought Index for Florida

by Chi-Han Cheng, Fidelia Nnadi and Yuei-An Liou

Remote Sens. 2015, 7(12), 17149-17167; doi:10.3390/rs71215879

Received: 13 August 2015 / Revised: 4 December 2015 / Accepted: 6 December 2015 / Published: 18 December 2015

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Abstract

Drought index is a useful tool to assess and respond to drought. However, current drought indices could not fully reveal land use effects and they have limitations in applications. Besides, El Niño Southern Oscillation (ENSO), strongly influences the climate of Florida. Hence, understanding

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Drought index is a useful tool to assess and respond to drought. However, current drought indices could not fully reveal land use effects and they have limitations in applications. Besides, El Niño Southern Oscillation (ENSO), strongly influences the climate of Florida. Hence, understanding ENSO patterns on a regional scale and developing a new land use drought index suitable for Florida are critical in agriculture and water resources planning and management. This paper presents a 32 km high resolution land use adapted drought index, which relies on five types of land uses (lake, urban, forest, wetland, and agriculture) in Florida. The land uses were obtained from National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data from 1979 to 2002. The results showed that Bowen ratio responded to land use and could be used as an indicator to monitor drought events. Then, an innovative regional land use drought index was developed from the normalized Bowen ratio, which could reflect not only the level of severity during drought events resulting from land use effects, but also La Niña driven drought impacts. The proposed new index may help scientists answer the critical questions about drought effect on various land uses and potential feedbacks of changes in land use and land cover to climate. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Open AccessArticle A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

by Ying-Nong Chen, Cheng-Ta Hsieh, Ming-Gang Wen, Chin-Chuan Han and Kuo-Chin Fan

Remote Sens. 2015, 7(11), 14292-14326; doi:10.3390/rs71114292

Received: 23 May 2015 / Revised: 16 October 2015 / Accepted: 22 October 2015 / Published: 29 October 2015

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Abstract

In this paper, a general nearest feature line (NFL) embedding (NFLE) transformation called fuzzy-kernel NFLE (FKNFLE) is proposed for hyperspectral image (HSI) classification in which kernelization and fuzzification are simultaneously considered. Though NFLE has successfully demonstrated its discriminative capability, the non-linear manifold structure

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In this paper, a general nearest feature line (NFL) embedding (NFLE) transformation called fuzzy-kernel NFLE (FKNFLE) is proposed for hyperspectral image (HSI) classification in which kernelization and fuzzification are simultaneously considered. Though NFLE has successfully demonstrated its discriminative capability, the non-linear manifold structure cannot be structured more efficiently by linear scatters using the linear NFLE method. According to the proposed scheme, samples were projected into a kernel space and assigned larger weights based on that of their neighbors. The within-class and between-class scatters were calculated using the fuzzy weights, and the best transformation was obtained by maximizing the Fisher criterion in the kernel space. In that way, the kernelized manifold learning preserved the local manifold structure in a Hilbert space as well as the locality of the manifold structure in the reduced low-dimensional space. The proposed method was compared with various state-of-the-art methods to evaluate the performance using three benchmark data sets. Based on the experimental results: the proposed FKNFLE outperformed the other, more conventional methods. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Open AccessArticle Spatial and Temporal Patterns of Global NDVI Trends: Correlations with Climate and Human Factors

by Ya Liu, Yan Li, Shuangcheng Li and Safa Motesharrei

Remote Sens. 2015, 7(10), 13233-13250; doi:10.3390/rs71013233

Received: 27 March 2015 / Revised: 17 September 2015 / Accepted: 26 September 2015 / Published: 6 October 2015

Cited by 11 | PDF Full-text (7951 KB) | HTML Full-text | XML Full-text | Supplementary Files

Abstract

Changes in vegetation activity are driven by multiple natural and anthropogenic factors, which can be reflected by Normalized Difference Vegetation Index (NDVI) derived from satellites. In this paper, NDVI trends from 1982 to 2012 are first estimated by the Theil–Sen median slope method

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Changes in vegetation activity are driven by multiple natural and anthropogenic factors, which can be reflected by Normalized Difference Vegetation Index (NDVI) derived from satellites. In this paper, NDVI trends from 1982 to 2012 are first estimated by the Theil–Sen median slope method to explore their spatial and temporal patterns. Then, the impact of climate variables and human activity on the observed NDVI trends is analyzed. Our results show that on average, NDVI increased by 0.46 × 10⁻³ per year from 1982 to 2012 globally with decadal variations. For most regions of the world, a greening (increasing)–browning (decreasing)–greening (G-B-G) trend is observed over the periods 1982–2004, 1995–2004, and 2005–2012, respectively. A positive partial correlation of NDVI and temperature is observed in the first period but it decreases and occasionally becomes negative in the following periods, especially in the Humid Temperate and Dry Domain Regions. This suggests a weakened effect of temperature on vegetation growth. Precipitation, on the other hand, is found to have a positive impact on the NDVI trend. This effect becomes stronger in the third period of 1995–2004, especially in the Dry Domain Region. Anthropogenic effects and human activities, derived here from the Human Footprint Dataset and the associated Human Influence Index (HII), have varied impacts on the magnitude (absolute value) of the NDVI trends across continents. Significant positive effects are found in Asia, Africa, and Europe, suggesting that intensive human activity could accelerate the change in NDVI and vegetation. A more accurate attribution of vegetation change to specific climatic and anthropogenic factors is instrumental to understand vegetation dynamics and requires further research. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Open AccessArticle NDVI-Based Analysis on the Influence of Climate Change and Human Activities on Vegetation Restoration in the Shaanxi-Gansu-Ningxia Region, Central China

by Shuangshuang Li, Saini Yang, Xianfeng Liu, Yanxu Liu and Mimi Shi

Remote Sens. 2015, 7(9), 11163-11182; doi:10.3390/rs70911163

Received: 16 May 2015 / Revised: 9 August 2015 / Accepted: 25 August 2015 / Published: 31 August 2015

Cited by 8 | PDF Full-text (1906 KB) | HTML Full-text | XML Full-text | Supplementary Files

Abstract

In recent decades, climate change has affected vegetation growth in terrestrial ecosystems. We investigated spatial and temporal patterns of vegetation cover on the Loess Plateau’s Shaanxi-Gansu-Ningxia region in central China using MODIS-NDVI data for 2000–2014. We examined the roles of regional climate change

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In recent decades, climate change has affected vegetation growth in terrestrial ecosystems. We investigated spatial and temporal patterns of vegetation cover on the Loess Plateau’s Shaanxi-Gansu-Ningxia region in central China using MODIS-NDVI data for 2000–2014. We examined the roles of regional climate change and human activities in vegetation restoration, particularly from 1999 when conversion of sloping farmland to forestland or grassland began under the national Grain-for-Green program. Our results indicated a general upward trend in average NDVI values in the study area. The region’s annual growth rate greatly exceeded those of the Three-North Shelter Forest, the upper reaches of the Yellow River, the Qinling–Daba Mountains, and the Three-River Headwater region. The green vegetation zone has been annually extending from the southeast toward the northwest, with about 97.4% of the region evidencing an upward trend in vegetation cover. The NDVI trend and fluctuation characteristics indicate the occurrence of vegetation restoration in the study region, with gradual vegetation stabilization associated with 15 years of ecological engineering projects. Under favorable climatic conditions, increasing local vegetation cover is primarily attributable to ecosystem reconstruction projects. However, our findings indicate a growing risk of vegetation degradation in the northern part of Shaanxi Province as a result of energy production facilities and chemical industry infrastructure, and increasing exploitation of mineral resources. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Open AccessArticle Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data

by Yu Zhang, Longqian Chen, Yuchen Wang, Longgao Chen, Fei Yao, Peiyao Wu, Bingyi Wang, Yuanyuan Li, Tianjian Zhou and Ting Zhang

Remote Sens. 2015, 7(8), 10737-10762; doi:10.3390/rs70810737

Received: 13 May 2015 / Revised: 8 August 2015 / Accepted: 12 August 2015 / Published: 20 August 2015

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Abstract

This paper presents a new assessment method for alleviating urban heat island (UHI) effects by using an urban land surface moisture (ULSM) index. With the aid of Landsat 8 OLI/TIRS data, the land surface temperature (LST) was retrieved by a mono-window algorithm, and

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This paper presents a new assessment method for alleviating urban heat island (UHI) effects by using an urban land surface moisture (ULSM) index. With the aid of Landsat 8 OLI/TIRS data, the land surface temperature (LST) was retrieved by a mono-window algorithm, and ULSM was extracted by tasselled cap transformation. Polynomial regression and buffer analysis were used to analyze the effects of ULSM on the LST, and the alleviation effect of ULSM was compared with three vegetation indices, GVI, SAVI, and FVC, by using the methods of grey relational analysis and Taylor skill calculation. The results indicate that when the ULSM value is greater than the value of an extreme point, the LST declines with the increasing ULSM value. Areas with a high ULSM value have an obvious reducing effect on the temperature of their surrounding areas within 150 m. Grey relational degrees and Taylor skill scores between ULSM and the LST are 0.8765 and 0.9378, respectively, which are higher than the results for the three vegetation indices GVI, SAVI, and FVC. The reducing effect of the ULSM index on environmental temperatures is significant, and ULSM can be considered to be a new and more effective index to estimate UHI alleviation effects for urban areas. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Open AccessArticle Observing Land Subsidence and Revealing the Factors That Influence It Using a Multi-Sensor Approach in Yunlin County, Taiwan

by Wei-Chen Hsu, Hung-Cheng Chang, Kuan-Tsung Chang, En-Kai Lin, Jin-King Liu and Yuei-An Liou

Remote Sens. 2015, 7(6), 8202-8223; doi:10.3390/rs70608202

Received: 12 October 2014 / Revised: 18 May 2015 / Accepted: 8 June 2015 / Published: 19 June 2015

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Abstract

Land subsidence is a worldwide problem that is typically caused by human activities, primarily the removal of groundwater. In Western Taiwan, groundwater has been pumped for industrial, residential, agricultural, and aquacultural uses for over 40 years. In this study, a multisensor monitoring system

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Land subsidence is a worldwide problem that is typically caused by human activities, primarily the removal of groundwater. In Western Taiwan, groundwater has been pumped for industrial, residential, agricultural, and aquacultural uses for over 40 years. In this study, a multisensor monitoring system comprising GPS stations, leveling surveys, monitoring wells, and Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) was employed to monitor land subsidence in Western Taiwan. The results indicate that land subsidence in Yunlin County was mainly affected by the compaction of subsurface soils and over-pumping of groundwater from deep soils. The study area comprised western foothills, characterized by sediments containing predominantly gravel, and coastal areas, where clay was predominant. The subsidence in coastal areas was more severe than that in the western foothills, as a result of groundwater removal. An additional factor affecting subsidence was the compaction of deep layers caused by deep groundwater removal and the deep-layer compaction was difficult to recover. Based on multisensor monitoring results, severe subsidence is mainly affected by compaction of subsurface soils, over-pumping of groundwater from deep soils, and deep soil compaction. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Open AccessArticle An Improvement of the Radiative Transfer Model Component of a Land Data Assimilation System and Its Validation on Different Land Characteristics

by Hui Lu, Kun Yang, Toshio Koike, Long Zhao and Jun Qin

Remote Sens. 2015, 7(5), 6358-6379; doi:10.3390/rs70506358

Received: 20 March 2015 / Accepted: 18 May 2015 / Published: 21 May 2015

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Abstract

The paper reports the recent progress in the radiative transfer model (RTM) development, which serves as the observation operator of a Land Data Assimilation System (LDAS), and its validation at two Planetary Boundary Layer (PBL) stations with different weather and land cover conditions:

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The paper reports the recent progress in the radiative transfer model (RTM) development, which serves as the observation operator of a Land Data Assimilation System (LDAS), and its validation at two Planetary Boundary Layer (PBL) stations with different weather and land cover conditions: Wenjiang station of humid and cropped field and Gaize station of arid and bare soil field. In situ observed micrometeorological data were used as the driven data of LDAS, in which AMSR-E brightness temperatures (TB) were assimilated into a land surface model (LSM). Near surface soil moisture content output from LDAS, together with the one simulated by a LSM with default parameters, were compared to the in-situ soil moisture observation. The comparison results successfully validated the capability of LDAS with new RTM to simulate near surface soil moisture at various environments, supporting that LDAS can generally simulate soil moisture with a reasonable accuracy for both humid vegetated fields and arid bare soil fields while the LSM overestimates near surface soil moisture for humid vegetated fields and underestimates soil moisture for arid bare soil fields. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Open AccessArticle Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

by Phuong D. Dao and Yuei-An Liou

Remote Sens. 2015, 7(5), 5077-5097; doi:10.3390/rs70505077

Received: 4 February 2015 / Revised: 30 March 2015 / Accepted: 13 April 2015 / Published: 24 April 2015

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Abstract

Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by

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Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake’s and Mekong River’s banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia. In this approach, image segmentation processing was conducted with optimal scale parameter estimation based on the variation of objects’ local variances. The inundated area was identified by using Landsat 8 images with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. The rice classification result was well correlated with the statistical data at a commune level (R² = 0.675). The flood mapping and affected rice estimation results are useful to provide local governments with valuable information for flooding mitigation and post-flooding compensation and restoration. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Open AccessArticle Correlations between Urbanization and Vegetation Degradation across the World’s Metropolises Using DMSP/OLS Nighttime Light Data

by Yanxu Liu, Yanglin Wang, Jian Peng, Yueyue Du, Xianfeng Liu, Shuangshuang Li and Donghai Zhang

Remote Sens. 2015, 7(2), 2067-2088; doi:10.3390/rs70202067

Received: 18 November 2014 / Accepted: 2 February 2015 / Published: 12 February 2015

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Abstract

Changes in biodiversity owing to vegetation degradation resulting from widespread urbanization demands serious attention. However, the connection between vegetation degradation and urbanization appears to be complex and nonlinear, and deserves a series of long-term observations. On the basis of the Normalized Difference Vegetation

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Changes in biodiversity owing to vegetation degradation resulting from widespread urbanization demands serious attention. However, the connection between vegetation degradation and urbanization appears to be complex and nonlinear, and deserves a series of long-term observations. On the basis of the Normalized Difference Vegetation Index (NDVI) and the image’s digital number (DN) in nighttime stable light data (NTL), we delineated the spatiotemporal relations between urbanization and vegetation degradation of different metropolises by using a simplified NTL calibration method and Theil-Sen regression. The results showed clear and noticeable spatiotemporal differences. On spatial relations, rapidly urbanized cities were found to have a high probability of vegetation degradation, but in reality, not all of them experience sharp vegetation degradation. On temporal characteristics, the degradation degree was found to vary during different periods, which may depend on different stages of urbanization and climate history. These results verify that under the scenario of a vegetation restoration effort combined with increasing demand for a high-quality urban environment, the urbanization process will not necessarily result in vegetation degradation on a large scale. The positive effects of urban vegetation restoration should be emphasized since there has been an increase in demand for improved urban environmental quality. However, slight vegetation degradation is still observed when NDVI in an urbanized area is compared with NDVI in the outside buffer. It is worthwhile to pay attention to landscape sustainability and reduce the negative urbanization effects by urban landscape planning. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Open AccessArticle Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea

by Rui Chang, Rong Zhu, Merete Badger, Charlotte Bay Hasager, Xuhuang Xing and Yirong Jiang

Remote Sens. 2015, 7(1), 467-487; doi:10.3390/rs70100467

Received: 23 August 2014 / Accepted: 16 December 2014 / Published: 6 January 2015

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Abstract

Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the

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Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the accurate reconstructed offshore winds which can be used for offshore wind resource assessment. First, wind speeds retrieved from Synthetic Aperture Radar (SAR) and Scatterometer ASCAT images were validated against in situ measurements from seven coastal meteorological stations in South China Sea (SCS). The wind roses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and ASCAT agree well with these observations from the corresponding in situ measurements. The statistical results comparing in situ wind speed and SAR-based (ASCAT-based) wind speed for the whole co-located samples show a standard deviation (SD) of 2.09 m/s (1.83 m/s) and correlation coefficient of R 0.75 (0.80). When the offshore winds (i.e., winds directed from land to sea) are excluded, the comparison results for wind speeds show an improvement of SD and R, indicating that the satellite data are more credible over the open ocean. Meanwhile, the validation of satellite winds against the same co-located mast observations shows a satisfactory level of accuracy which was similar for SAR and ASCAT winds. These satellite winds are then assimilated into the Weather Research and Forecasting (WRF) Model by WRF Data Assimilation (WRFDA) system. Finally, the wind resource statistics at 100 m height based on the reconstructed winds have been achieved over the study area, which fully combines the offshore wind information from multiple satellite data and numerical model. The findings presented here may be useful in future wind resource assessment based on satellite data. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Open AccessReview Remote Sensing Analysis Techniques and Sensor Requirements to Support the Mapping of Illegal Domestic Waste Disposal Sites in Queensland, Australia

by Katharine Glanville and Hsing-Chung Chang

Remote Sens. 2015, 7(10), 13053-13069; doi:10.3390/rs71013053

Received: 13 February 2015 / Revised: 7 July 2015 / Accepted: 26 September 2015 / Published: 1 October 2015

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Abstract

Illegal disposal of waste is a significant management issue for contemporary governments with waste posing an economic, social, and environmental risk. An improved understanding of the distribution of illegal waste disposal sites is critical to enhance the cost-effectiveness and efficiency of waste management

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Illegal disposal of waste is a significant management issue for contemporary governments with waste posing an economic, social, and environmental risk. An improved understanding of the distribution of illegal waste disposal sites is critical to enhance the cost-effectiveness and efficiency of waste management efforts. Remotely sensed data has the potential to address this knowledge gap. However, the literature regarding the use of remote sensing to map illegal waste disposal sites is incomplete. This paper aims to analyze existing remote sensing methods and sensors used to monitor and map illegal waste disposal sites. The purpose of this paper is to support the evaluation of existing remote sensing methods for mapping illegal domestic waste sites in Queensland, Australia. Recent advances in technology and the acquisition of very high-resolution remote sensing imagery provide an important opportunity to (1) revisit established analysis techniques for identifying illegal waste disposal sites, (2) examine the applicability of different remote sensors for illegal waste disposal detection, and (3) identify opportunities for future research to increase the accuracy of any illegal waste disposal mapping products. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Other

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Open AccessTechnical Note Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

by Riccardo Tortini, Audrey L. Mayer and Pieralberto Maianti

Remote Sens. 2015, 7(6), 7809-7825; doi:10.3390/rs70607809

Received: 6 March 2015 / Revised: 21 May 2015 / Accepted: 8 June 2015 / Published: 15 June 2015

Cited by 2 | PDF Full-text (6214 KB) | HTML Full-text | XML Full-text | Supplementary Files

Abstract

Forest dynamics influence climate, biodiversity, and livelihoods at multiple scales, yet current resource policy addressing these dynamics is ineffective without reliable land use land cover change data. The collective impact of harvest decisions by many small forest owners can be substantial at the

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Forest dynamics influence climate, biodiversity, and livelihoods at multiple scales, yet current resource policy addressing these dynamics is ineffective without reliable land use land cover change data. The collective impact of harvest decisions by many small forest owners can be substantial at the landscape scale, yet monitoring harvests and regrowth in these forests is challenging. Remote sensing is an obvious route to detect and monitor small-scale land use dynamics over large areas. Using an annual series of Landsat-5 Thematic Mapper (TM) images and a GIS shapefile of property boundaries, we identified units where harvests occurred from 2005 to 2011 using an Object-Based Change Detection (OBCD) approach. Percent of basal area harvested was verified using stand-level harvest data. Our method detected all harvests above 20% basal area removal in all forest types (northern hardwoods, mixed deciduous/coniferous, coniferous), on properties as small as 10 acres (0.4 ha; approximately four Landsat pixels). Our results had a resolution of about 10% basal area (that is, a selective harvest removal of 30% could be distinguished from one of 40%). Our method can be automated and used to measure annual harvest rates and intensities for large areas of the United States, providing critical information on land use transition. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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Guest Editor Prof. Dr. Yuei-An Liou Founder & Honorary President, Taiwan Group on Earth Observations; Distinguished Professor & Academician, Center for Space and Remote Sensing Research, National Central University, Taoyuan 32001, Taiwan Website \| E-Mail Interests: microwave and optical remote sensing; atmospheric science; GPS meteorology and methodology; cryosphere
Guest Editor Dr. Chyi-Tyi Lee Supervisor, Taiwan Group on Earth Observations; Institute of Applied Geology, National Central University, Taoyuan, 32001, Taiwan Website \| E-Mail Phone: +886-3-4253334 Interests: engineering geology; earthquake geology; geostatistics; GIS
Guest Editor Dr. Yaoming Ma Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), Beijing 100101, China Website \| E-Mail Interests: atmospheric boundary layer observation and satellite remote sensing application
Guest Editor Dr. Takashi Oguchi Center for Spatial Information Science, University of Tokyo, Tokyo 113-0033, Japan Website \| E-Mail Interests: landform; geology; hydrology; natural environment; GIS; DEM
Guest Editor Dr. Indrajeet Chaubey Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USA Website \| E-Mail Interests: ecohydrology; management; and water quality of agricultural watersheds

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