scholarly journals A color balancing method for wide range Remote Sensing imagery based on Regionalization

2015 ◽  
Vol XL-7/W4 ◽  
pp. 103-108 ◽  
Author(s):  
J. Liu ◽  
H. T. Li ◽  
H. Y. Gu
Keyword(s):  
Remote Sensing ◽  
Difficult Problem ◽  
Geographic Information ◽  
Dynamic Monitoring ◽  
Land Resource ◽  
Climatic Data ◽  
Large Area ◽  
Remote Sensing Imagery ◽  
Wide Range ◽  
Algorithmic Framework

Quick mosaicking of wide range remote sensing imagery is an important foundation for land resource survey and dynamic monitoring of environment and nature disasters. It is also technically important for basis imagery of geographic information acquiring and geographic information product updating. This paper mainly focuses on one key technique of mosaicking, color balancing for wide range Remote Sensing imagery. Due to huge amount of data, large covering rage, great variety of climate and geographical condition, color balancing for wide range remote sensing imagery is a difficult problem. In this paper we use Ecogeographic regionalization to divide the large area into several regions based on terrains and climatic data, construct the algorithmic framework of a color balancing method according to the regionalization result, which conduct from region edge to center to fit wide range imagery mosaicking. The experimental results with wide range HJ-1 dataset show that our method can significantly improve the wide range of remote sensing imagery color balancing effects: making images well-proportioned mosaicking and better in keeping images' original information. In summary, this color balancing method based on regionalization could be a good solution for nationwide remote sensing image color balancing and mosaicking.

Dynamic monitoring of wetland cover changes using time-series remote sensing imagery

2014 ◽  
Vol 24 ◽  
pp. 17-26 ◽  
Author(s):  
Lifan Chen ◽  
Zhenyu Jin ◽  
Ryo Michishita ◽  
Jun Cai ◽  
Tianxiang Yue ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Dynamic Monitoring ◽  
Remote Sensing Imagery ◽  
Wetland Cover

Contrasting changes in surface waters and barrens over the past 60 years for a subarctic forest–tundra site in northern Manitoba based on remote sensing imagery

2013 ◽  
Vol 50 (9) ◽  
pp. 967-977 ◽  
Author(s):  
Charles Umbanhowar ◽  
Philip Camill ◽  
Mark Edlund ◽  
Christoph Geiss ◽  
Wesley Durham ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Terrestrial Ecosystems ◽  
Water Bodies ◽  
Aerial Photographs ◽  
The Arctic ◽  
Remote Sensing Imagery ◽  
The Past ◽  
High Resolution Satellite Images ◽  
Wide Range

Intensified warming in the Arctic and Subarctic is resulting in a wide range of changes in the extent, productivity, and composition of aquatic and terrestrial ecosystems. Analysis of remote sensing imagery has documented regional changes in the number and area of ponds and lakes as well as expanding cover of shrubs and small trees in uplands. To better understand long-term changes across the edaphic gradient, we compared the number and area of water bodies and dry barrens (>100 m2) between 1956 (aerial photographs) and 2008–2011 (high-resolution satellite images) for eight ∼25 km2 sites near Nejanilini Lake, Manitoba (59.559°N, 97.715°W). In the modern landscape, the number of water bodies and barrens were similar (1162 versus 1297, respectively), but water bodies were larger (mean 3.1 × 104 versus 681 m2, respectively) and represented 17% of surface area compared with 0.4% for barrens. Over the past 60 years, total surface area of water did not change significantly (16.7%–17.1%) despite a ∼30% decrease in numbers of small (<1000 m2) water bodies. However, the number and area of barrens decreased (55% and 67%, respectively) across all size classes. These changes are consistent with Arctic greening in response to increasing temperature and precipitation. Loss of small water bodies suggests that wet tundra areas may be drying, which, if true, may have important implications for carbon balance. Our observations may be the result of changes in winter conditions in combination with low permafrost ice content in the region, in part explaining regional variations in responses to climate change.

scholarly journals The Development of Interpretataion Method For Remote Sensing Imagery In Determining The Candidate of Landslide In Leitimur Paninsula, Ambon Island

2017 ◽  
Vol 15 (1) ◽  
pp. 20 ◽  
Author(s):  
Ferad Puturuhu ◽  
Projo Danoedoro ◽  
Junun Sartohadi ◽  
Danang Srihadmoko
Keyword(s):  
Remote Sensing ◽  
Debris Flow ◽  
Spatial Data ◽  
Landsat 8 ◽  
Land Resource ◽  
Digital Imagery ◽  
Visual Interpretation ◽  
Remote Sensing Imagery ◽  
Soil Creep ◽  
Landslide Event

ABSTRAKPenginderaa jauh merupakan salah satu metode yang digunakan untuk menjawab permasalahan penelitian tentang teknologi perolehan data spasial dan sekaligus permasalahan kewilayahan serta manajemen sumber daya laha. Pemanfaatan metode penginderaan jauh untuk penelitian landslide dianataranya metode interpretasi citra secara visual dan digital.  Tujuan penelitian ini adalah membandingkan akurasi metode interpretasi dan menentukan lokasi kejadian landslide. Citra yang digunakan dalam penelitian ini adalah citra Landsat 8, Quickbird dan SRTM. Metode yang digunakan untuk menentukan kandidat landslide adalah interpretasi visual berlapis, Interpretasi citra digital dengan NDVI, OBIA, Toposhape, dan kombinasi NDVI-OBIA, dan NDVI-OBIA-Toposhape. Penggunaan metode interpretasi kejadian landslide yang terbaik adalah interpretasi visual berlapis dengan presentase 90 %. Interpretasi digital dengan NDVI mempunyai ketelitian 47 %, OBIA ketelitiannya  45 %, Toposhape 47 %, kombinasi NDVI-OBIA 47 %, dan Kombinasi NDVI-OBIA-Toposhape 53 %. Dari interpretasi visual berlapis dan pengamatan lapangan diperoleh tipe landslide yang ditemukan yaitu nendatan/slump (soil rotational slide) dalam jumlah yang banyak 7 titik (38.9%), rayapan tanah (soil creep),  aliran bahan rombakan (debris flow), longsor translasi dengan material tanah (earths Slide), dan  nendatan majemuk (multiple rotational slide).Kata kunci: Pengembanga, Metode, Interpretasi Citra, Penginderaan Jauh, Kandidat,    Landslide, Paninsula LeitimurABSTRACTRemote sensing is one of the methods used to address the problem of research on spatial data acquisition technologies and is also acquiring the problems of territorial and land resource management. The utilization of remote sensing method for the landslide research is visual and digital imagery interpretation. The purpose of this study was to compare the accuracy of the method of interpretation and determine the location of the landslide event. The imagery that used in this study was Landsat 8, Quickbird and SRTM. The method that used to determine the candidate of landslide was the layered visual interpretation, digital imagery interpretation with NDVI, OBIA, Toposhape, and combination-OBIA NDVI and NDVI-OBIA-Toposhape. The use of the interpretation method for the landslide event is the best of layered-visual interpretation with a percentage of 90%. Digital interpretation with NDVI has a 47% of its accuracy, thoroughness OBIA 45%, Toposhape 47%, the combination of NDVI-OBIA 47%, and the combination of NDVI-OBIA-Toposhape 53%. From  the layered-visual interpretation and field observations were obtained type of landslide found that soil rotational slide in large quantities 7 points (38.9%), creep soil (soil creep), the flow of material destruction (debris flow), landslides translation with soil materials (earths slide) and multiple rotational slide.Keywords: Development, Method, Imagery Interpretation, Remote Sensing, Candidate of Landslide, Landslide and Leitimur JaizirahCitation: Puturuhu, F., Danoedoro, P., Sartohadi, J. and Srihadmoko, D. (2017). The Development of Interpretataion Method for Remote Sensing Imagery In Determining The Candidate of Landslide In Leitimur Paninsula, Ambon Island. Jurnal Ilmu Lingkungan, 15(1), 20-34, doi:10.14710/jil.15.1.20-34

Spectral Characteristics of Cr and Cu in Agricultural Soil at Beijing Plain Based on SWIR Spectroscopy and PLSR

2013 ◽  
Vol 765-767 ◽  
pp. 3066-3072 ◽  
Author(s):  
Shu Min Li ◽  
Hong Li ◽  
Dan Feng Sun ◽  
Lian Di Zhou
Keyword(s):  
Heavy Metals ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Large Scale ◽  
Spectral Characteristics ◽  
Dynamic Monitoring ◽  
Optimum Number ◽  
Least Squares Regression ◽  
Large Area ◽  
Soil Heavy Metals

Heavy metals pollution in agricultural soils has been an important problem to human health, mapping large-scale spatial distribution of soil heavy metals is urgently needed. Instead of traditional methods, time-consuming and destructive, soil properties predicted by remote sensing technology shows a lot of advantages, which makes large area of real-time dynamic monitoring as possible. However, before achieving prediction using spectra data, the first thing to do is that finding the spectral characteristics of soil heavy metals. In this paper, taking Cr and Cu for example, the correlations between soil heavy metals content and laboratory-measured reflectance is studied using partial least squares regression (PLSR), which is an adaptive method to examine linear between spectrum and concentration. First of all, using the raw spectra, remove outliers of heavy metals concentration by PLSR modeling. Next, though comparing RMSEC and RMSEV against PLSR components, and cumulative explanatory of spectral components to metal content using different pre-precessing methods, find the right pre-pcocessing is CR and optimum number of components to Cr and Cu are 3 and 2 respectively. Simultaneously, with the meaning of PLSR models regression coefficients, we analysis the spectral characteristics of Cr and Cu, although can not to realize the prediction only take use of these spectra, which is still essential to achieve simulating spatial distribution of soil heavy metal by remote sensing.

The Intelligent Classifier’s Building of Typical Vegetation Based on Hyper-Spectral Data

2014 ◽  
Vol 1073-1076 ◽  
pp. 1972-1976
Author(s):  
Jie Zhang ◽  
Hao Yan Zhao ◽  
Min Xia Zhang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Dynamic Monitoring ◽  
Large Area ◽  
Desert Vegetation ◽  
Sensing Data ◽  
Monitoring Model ◽  
Hyper Spectral ◽  
Bp Neural Network Model ◽  
Intelligent Process

With 3S comprehensive analysis on vegetation and the further development of hyper-spectral technology, the dynamic monitor of large area vegetation in long-term has become the trend. Intelligent process, combined the remote sensing data and field data, constructing dynamic monitoring model, plays an important guilding role in ecological security and balance. By using hyper-spectral remote sensing data of desert vegetation, three groups of spectral characteristic parameters were selected as input data of typical desert vegetation in the research, and vegetation types were selected as output data. Typical vegetation classifier was constructed based on the BP neural network model to study the vegetation classification.

scholarly journals Recent Advances in Remote Sensing of Evapotranspiration

2021 ◽  
Vol 13 (21) ◽  
pp. 4260
Author(s):  
Nishan Bhattarai ◽  
Pradeep Wagle
Keyword(s):  
Remote Sensing ◽  
Environmental Sustainability ◽  
Global Environmental Change ◽  
Global Scale ◽  
Ecosystem Functions ◽  
Special Issue ◽  
Large Area ◽  
Modeling Tools ◽  
Recent Advances ◽  
Wide Range

Evapotranspiration (ET) plays an important role in coupling the global energy, water, and biogeochemical cycles and explains ecosystem responses to global environmental change. However, quantifying and mapping the spatiotemporal distribution of ET across a large area is still a challenge, which limits our understanding of how a given ecosystem functions under a changing climate. This also poses a challenge to water managers, farmers, and ranchers who often rely on accurate estimates of ET to make important irrigation and management decisions. Over the last three decades, remote sensing-based ET modeling tools have played a significant role in managing water resources and understanding land-atmosphere interactions. However, several challenges, including limited applicability under all conditions, scarcity of calibration and validation datasets, and spectral and spatiotemporal constraints of available satellite sensors, exist in the current state-of-the-art remote sensing-based ET models and products. The special issue on “Remote Sensing of Evapotranspiration II” was launched to attract studies focusing on recent advances in remote sensing-based ET models to help address some of these challenges and find novel ways of applying and/or integrating remotely sensed ET products with other datasets to answer key questions related to water and environmental sustainability. The 13 articles published in this special issue cover a wide range of topics ranging from field- to global-scale analysis, individual model to multi-model evaluation, single sensor to multi-sensor fusion, and highlight recent advances and applications of remote sensing-based ET modeling tools and products.

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