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1.
As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.  相似文献   

2.
Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km2 river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.  相似文献   

3.
Deforestation is recognized as one of the most significant components in LULCC and global changes scenario. It is imperative to assess its trend and the rate at which it is occurring. The changes will have long-lasting impact on regional climate and in turn on biodiversity. Present study was taken up in Kanakapura and surrounding areas located on the fringes of Western Ghats biodiversity hot-spots. Temporal satellite data from Landsat was classified into forest cover maps. Drivers of forest cover changes such as roads and settlements were used in order to create predicted map of the region using GEOMOD tool in Idrisi Andes. The predicted map was then validated using actual land cover map of same year prepared from Landsat data. The validated map was found to be 84.26 % accurate. The validation was also tested using ROC approach which was found to be 0.614. The model was then further extended to predict forest cover losses for year 2015. The results highlight ongoing deforestation in the areas adjoining Western Ghats. It also presents an application of the tool and the validation methods which can be used in predictive modeling related studies.  相似文献   

4.
The Niger River is one of the most important sources of water supply for human consumption and agriculture in Western Africa. Two Landsat‐5 Multispectral Scanner (MSS) images, corresponding to the dry and wet seasons, over a selected area of the Niger River interior delta were classified to produce a land cover/land use map that reflects the geo‐hydrological units of this area. To classify the satellite data, training statistics were generated using a clustering algorithm with parameter values that maximize the separability among spectral classes. Both dry and wet season images are required to obtain an accurate classification for evaluation of hydrological parameters. The spatial resolution of the MSS proved to be adequate for this kind of work, since all the major cover types and geographic features were correctly recognized.  相似文献   

5.
流域尺度的不透水面遥感提取   总被引：7，自引：1，他引：6  
一个地区的不透水面覆盖度不仅是该地区城镇化程度重要指示因子,也是该地区生态环境状况的重要指示因子.现有的不透水面遥感提取方法,多集中在城区尺度上.而流域尺度上快速、准确的不透水面遥感提取方法在国内外还鲜有研究.本研究以覆盖海河流域同一季节的Landsat影像为数据源,利用已有土地利用数据集中的道路、城市、农村和工业用地...  相似文献   

6.
The role of corridors in mitigating the effects of landscape fragmentation on biodiversity is controversial. Recent studies have highlighted the need for new approaches in corridor design using long-term datasets. We present a method to identify transit corridors for elephant at a population scale over a large area and an extended period of time using long-term aerial surveys. We investigated environmental and anthropogenic factors directly and indirectly related to the wet versus dry season distribution of elephant and its transit corridors. Four environmental variables predicted the presence of elephant at the landscape scale in both seasons: distance from permanent water, protected areas and settlements and vegetation structure. Path analysis revealed that altitude and monthly average NDVI, and distance from temporary water had a significant indirect effect on elephant distribution at local scale in dry and wet seasons respectively. Five transit corridors connecting Tarangire National Park and the northern as well as south-eastern wet season dispersal areas were identified and matched the wildlife migration routes described in the 1960s. The corridors are stable over the decades, providing landscape connectivity for elephant. Our approach yielded insights how advanced spatial analysis can be integrated with biological data available from long-term datasets to identify actual transit corridors and predictors of species distribution.  相似文献   

7.
Landsat data are the longest available records that consistently document global change. However, the extent and degree of cloud coverage typically determine its usability, especially in the tropics. In this study, scene-based metadata from the U.S. Geological Survey Landsat inventories, ten-day, monthly, seasonal, and annual acquisition probabilities (AP) of targeted images at various cloud coverage thresholds (10% to 100%) were statistically analyzed using available Landsat TM, ETM+, and OLI observations over mainland Southeast Asia (MSEA) from 1986 to 2015. Four significant results were found. First, the cumulative average acquisition probability of available Landsat observations over MSEA at the 30% cloud cover (CC) threshold was approximately 41.05%. Second, monthly and ten-day level probability statistics for the 30% CC threshold coincide with the temporal distribution of the dry and rainy seasons. This demonstrates that Landsat images acquired during the dry season satisfy the requirements needed for land cover monitoring. Third, differences in acquisition probabilities at the 30% CC threshold are different between the western and eastern regions of MSEA. Finally, the ability of TM, ETM+, and OLI to acquire high-quality imagery has gradually enhanced over time, especially during the dry season, along with consequently larger probabilities at lower CC thresholds.  相似文献   

8.
Landsat MSS images and SPOT HRV data were employed to map the changes in turbidity levels in the Zhujiang estuarine region, South China, during the dry season in the period 1973–1987 at low and high tides. Analysis of turbidity trends and changes in the spatial pattern of high turbidity class was carried out with a GIS software—IDRISI. It was concluded that with the use of OVERLAY and RECLASS functions in the GIS approach a large number of turbidity maps could be easily compared and the turbidity trend determined. The GIS approach further permitted evaluation of the importance of such factors as water depths, mean tidal differences, and water salinity to sedimentation in the study region.  相似文献   

9.
In this study chlorophyll measurements were made during March 2012 in the estuarine waters of Off Kakinada and Yanam coast, Bay of Bengal onboard a coastal vessel. In-situ water samples and optical data was collected at 21 stations (surface to 150 m depth) using Underwater radiometer (Hyperpro-II). In-vivo chlorophyll profiles were collected using wet labs fluorometer integrated with underwater Hyperspectral radiometer. Chlorophyll-a concentrations were estimated using HPLC by collecting the water samples at each sampling location. And also chlorophyll-a concentrations were retrieved from the OCM-2 data of OCEANSAT-2 satellite, processed using SeaDAS v.6.2 with the available global ocean colour algorithms namely, OC2 and OC4V4. A total of 33 samples used covering all the stations for chlorophyll-a estimation, and surface water samples of all the stations only being used for direct comparison among chlorophyll concentrations of HPLC, in-situ (fluorometrically integrated to Hyperpro-II) and retrieved from OCM-2. A good correlation found between the Fluorometer derived and HPLC measured chlorophyll-a concentration with an R2 value of 0.78. The relation between Chlorophyll-a concentration measured from HPLC and retrieved from OCM-2 (OC2 and OC4V4 algorithms) using SeaDASv.6.2 for 10 samples has been compared for validation and obtained an R2 value of 0.6. Also comparisons done with the in-situ measured (fluorometer) Chlorophyll-a concentration with OCM-2 chlorophyll data (OC4-V4 and OC2 algorithms) and validation with 10 concurrent in-situ surface measurements showed a significant overestimation by OCM-2 at low chlorophyll-a concentrations and underestimation at high chlorophyll-a concentrations.  相似文献   

10.
Forel-Ule (FU) index of water color is an important parameter in traditional water quality investigations. We retrieved the FU index of the largest 10 lakes in China during 2000-2012 from MODerate-resolution Imaging Spectroradiometer surface reflectance product (MOD09) images. Since FU index is an optical parameter, it can be derived from optical remote sensing data by direct formulas, which is invariant with region and season. Based on validation by in situ measured reflectance data, the FU index products are reliable, with average relative error of 7.7%. FU index can be used to roughly assess water clarity: the clearer a water body is, and the bluer it is in color, the smaller its FU index is. FU index can also be used to roughly classify trophic state into three classes: oligotrophic, mesotrophic, and eutrophic. We analyzed the spatial, interannual, and seasonal variations of the FU index and its implications for water clarity and trophic state, and the findings are mostly consistent with the results from related literature. All in all, it might be a feasible way to roughly assess inland water quality by FU index in large region and over long time period.  相似文献   

11.
Rice crop occupies an important aspect of food security and also contributes to global warming via GHGs emission. Characterizing rice crop using spatial technologies holds the key for addressing issues of global warming and food security as different rice ecosystems respond differently to the changed climatic conditions. Remote sensing has become an important tool for assessing seasonal vegetation dynamics at regional and global scale. Bangladesh is one of the major rice growing countries in South Asia. In present study we have used remote sensing data along with GIS and ancillary map inputs in combination to derive seasonal rice maps, rice phenology and rice cultural types of Bangladesh. The SPOT VGT S10 NDVI data spanning Aus, Aman and Boro crop season (1st May 2008 to 30th April 2009) were used, first for generating the non-agriculture mask through ISODATA clustering and then to generate seasonal rice maps during second classification. The spectral rice profiles were modelled and phenological parameters were derived. NDVI growth profiles were modelled and crop calendar was derived. To segregate the rice cultural types of Bangladesh into IPCC rice categories, we used elevation, irrigated area, interpolated rainfall maps and flood map through logical modelling in GIS. The results indicated that the remote sensing derived rice area was 9.99 million ha as against the reported area of 11.28 million ha. The wet and dry seasons accounted for 64% and 36 % of the rice area, respectively. The flood prone, drought prone and deep water categories account for 7.5%, 5.56% and 2.03%, respectively. The novelty of current findings lies in the spatial outcome in form of seasonal and rice cultural type maps of Bangladesh which are helpful for variety of applications.  相似文献   

12.
绿洲—荒漠交错带地下水位分布的遥感模型研究   总被引：16，自引：0，他引：16  
以利用卫星遥感数据评价干旱区绿洲－荒漠交错带地下水位的分布作为主要研究目的，使用波段Landsat-7ETM 图像，用遥感－数学－模型学融合的研究方法，在实地考察地下水位，土壤水分和其他辅助资料的基础上，建立土壤水分和地下水位的实验方程，提出了评价地下水位分布的遥感模型－GLDRS模型。利用GLDRS模型对新疆策勒绿洲－荒漠交错带进行了实地验证，结果表明，研究结果符合实际，GLDRS多波段模型优越单波段模型，理论地下水位和实测地下水位之间的相关系数为0．901。  相似文献   

13.
For three agricultural crop types, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), we estimated biophysical parameters including fresh and dry biomass, leaf area index (LAI), and vegetation water content, for which we found the equivalent water thickness (EWT), fuel moisture content per fresh weight (FMCFW), and fuel moisture content per dry weight (FMCDW). We performed these estimations using data from the newly launched Landsat 8 Operational Land Imager (OLI) sensor, as well as its predecessor the Landsat 7 Enhanced Thematic Mapper Plus (ETM+). Progress in the design of the new sensor (i.e., Landsat 8), including narrower near-infrared (NIR) wavebands, higher signal-to-noise ratio (SNR), and greater radiometric resolution highlights the necessity to investigate the biophysical parameters of agricultural crops, especially compared to data from its predecessor. This study aims to evaluate vegetation indices (VIs) derived from the Landsat 8 OLI and the Landsat 7 ETM+. Both the Landsat 8 OLI and Landsat 7 ETM+ VIs agreed well with in-situ data measurements. However, the Landsat 8 OLI-derived VIs were generally more consistent with in situ data than the Landsat 7 ETM+ VIs. We also note that the Landsat 8 OLI is better able to capture the small variability of the VIs because of its higher SNR and wider radiometric range; in addition, the saturation phenomenon occurred earlier for the Landsat 7 ETM+ than for the Landsat 8 OLI. This indicates that the new sensor is better able to estimate the biophysical parameters of crops.  相似文献   

14.
Green-leaf phenology describes the development of vegetation throughout a growing season and greatly affects the interaction between climate and the biosphere. Remote sensing is a valuable tool to characterize phenology over large areas but doing at fine- to medium resolution (e.g., with Landsat data) is difficult because of low numbers of cloud-free images in a single year. One way to overcome data availability limitations is to merge multi-year imagery into one time series, but this requires accounting for phenological differences among years. Here we present a new approach that employed a time series of a MODIS vegetation index data to quantify interannual differences in phenology, and Dynamic Time Warping (DTW) to re-align multi-year Landsat images to a common phenology that eliminates year-to-year phenological differences. This allowed us to estimate annual phenology curves from Landsat between 2002 and 2012 from which we extracted key phenological dates in a Monte-Carlo simulation design, including green-up (GU), start-of-season (SoS), maturity (Mat), senescence (Sen), end-of-season (EoS) and dormancy (Dorm). We tested our approach in eight locations across the United States that represented forests of different types and without signs of recent forest disturbance. We compared Landsat-based phenological transition dates to those derived from MODIS and ground-based camera data from the PhenoCam-network. The Landsat and MODIS comparison showed strong agreement. Dates of green-up, start-of-season and maturity were highly correlated (r 0.86-0.95), as were senescence and end-of-season dates (r > 0.85) and dormancy (r > 0.75). Agreement between the Landsat and PhenoCam was generally lower, but correlation coefficients still exceeded 0.8 for all dates. In addition, because of the high data density in the new Landsat time series, the confidence intervals of the estimated keydates were substantially lower than in case of MODIS and PhenoCam. Our study thus suggests that by exploiting multi-year Landsat imagery and calibrating it with MODIS data it is possible to describe green-leaf phenology at much finer spatial resolution than previously possible, highlighting the potential for fine scale phenology maps using the rich Landsat data archive over large areas.  相似文献   

15.
Mapping crop types is of great importance for assessing agricultural production, land-use patterns, and the environmental effects of agriculture. Indeed, both radiometric and spatial resolution of Landsat’s sensors images are optimized for cropland monitoring. However, accurate mapping of crop types requires frequent cloud-free images during the growing season, which are often not available, and this raises the question of whether Landsat data can be combined with data from other satellites. Here, our goal is to evaluate to what degree fusing Landsat with MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) data can improve crop-type classification. Choosing either one or two images from all cloud-free Landsat observations available for the Arlington Agricultural Research Station area in Wisconsin from 2010 to 2014, we generated 87 combinations of images, and used each combination as input into the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to predict Landsat-like images at the nominal dates of each 8-day MODIS NBAR product. Both the original Landsat and STARFM-predicted images were then classified with a support vector machine (SVM), and we compared the classification errors of three scenarios: 1) classifying the one or two original Landsat images of each combination only, 2) classifying the one or two original Landsat images plus all STARFM-predicted images, and 3) classifying the one or two original Landsat images together with STARFM-predicted images for key dates. Our results indicated that using two Landsat images as the input of STARFM did not significantly improve the STARFM predictions compared to using only one, and predictions using Landsat images between July and August as input were most accurate. Including all STARFM-predicted images together with the Landsat images significantly increased average classification error by 4% points (from 21% to 25%) compared to using only Landsat images. However, incorporating only STARFM-predicted images for key dates decreased average classification error by 2% points (from 21% to 19%) compared to using only Landsat images. In particular, if only a single Landsat image was available, adding STARFM predictions for key dates significantly decreased the average classification error by 4 percentage points from 30% to 26% (p < 0.05). We conclude that adding STARFM-predicted images can be effective for improving crop-type classification when only limited Landsat observations are available, but carefully selecting images from a full set of STARFM predictions is crucial. We developed an approach to identify the optimal subsets of all STARFM predictions, which gives an alternative method of feature selection for future research.  相似文献   

16.
Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. After being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions.  相似文献   

17.
Land cover maps play an integral role in environmental management. However, countries and institutes encounter many challenges with producing timely, efficient, and temporally harmonized updates to their land cover maps. To address these issues we present a modular Regional Land Cover Monitoring System (RLCMS) architecture that is easily customized to create land cover products using primitive map layers. Primitive map layers are a suite of biophysical and end member maps, with land cover primitives representing the raw information needed to make decisions in a dichotomous key for land cover classification. We present best practices to create and assemble primitives from optical satellite using computing technologies, decision tree logic and Monte Carlo simulations to integrate their uncertainties. The concept is presented in the context of a regional land cover map based on a shared regional typology with 18 land cover classes agreed on by stakeholders from Cambodia, Laos PDR, Myanmar, Thailand, and Vietnam. We created annual map and uncertainty layers for the period 2000–2017. We found an overall accuracy of 94% when taking uncertainties into account. RLCMS produces consistent time series products using free long term historical Landsat and MODIS data. The customizable architecture can include a variety of sensors and machine learning algorithms to create primitives and the best suited smoothing can be applied on a primitive level. The system is transferable to all regions around the globe because of its use of publicly available global data (Landsat and MODIS) and easily adaptable architecture that allows for the incorporation of a customizable assembly logic to map different land cover typologies based on the user's landscape monitoring objectives  相似文献   

18.
Secondary tropical dry forests (TDFs) provide important ecosystem services such as carbon sequestration, biodiversity conservation, and nutrient cycle regulation. However, their biogeophysical processes at the canopy-atmosphere interface remain unknown, limiting our understanding of how this endangered ecosystem influences, and responds to the ongoing global warming. To facilitate future development of conservation policies, this study characterized the seasonal land surface temperature (LST) behavior of three successional stages (early, intermediate, and late) of a TDF, at the Santa Rosa National Park (SRNP), Costa Rica. A total of 38 Landsat-8 Thermal Infrared Sensor (TIRS) data and the Surface Reflectance (SR) product were utilized to model LST time series from July 2013 to July 2016 using a radiative transfer equation (RTE) algorithm. We further related the LST time series to seven vegetation indices which reflect different properties of TDFs, and soil moisture data obtained from a Wireless Sensor Network (WSN). Results showed that the LST in the dry season was 15–20 K higher than in the wet season at SRNP. We found that the early successional stages were about 6–8 K warmer than the intermediate successional stages and were 9–10 K warmer than the late successional stages in the middle of the dry season; meanwhile, a minimum LST difference (0–1 K) was observed at the end of the wet season. Leaf phenology and canopy architecture explained most LST variations in both dry and wet seasons. However, our analysis revealed that it is precipitation that ultimately determines the LST variations through both biogeochemical (leaf phenology) and biogeophysical processes (evapotranspiration) of the plants. Results of this study could help physiological modeling studies in secondary TDFs.  相似文献   

19.
Tropical deforestation through logging activities poses a direct threat to biodiversity. However, the detection of logging has remained a challenge. Based on study sites in Zimbabwe and Zambia, we tested whether the Normalized Difference Vegetation Index (NDVI) and the Coefficient of Variation in NDVI (CVNDVI) derived from high and medium spatial resolution satellite data could be used to detect logging in dry and wet miombo woodlands. Separately, we integrated NDVI and CVNDVI in logistic regression to test whether each can be used to successfully predict logging in the study sites. We tested whether the spatial resolution of satellite data has an effect in detection of logging using NDVI and CVNDVI derived from Landsat 8 and Worldview-2. Based on the ROC curves, we concluded that remotely sensed data could provide an effective predictive tool for detecting logging. However, in wet miombo woodlands the predictive power of remotely sensed data is weak.  相似文献   

20.
Vegetation phenology has a great impact on land-atmosphere interactions like carbon cycling, albedo, and water and energy exchanges. To understand and predict these critical land-atmosphere feedbacks, it is crucial to measure and quantify phenological responses to climate variability, and ultimately climate change. Coarse-resolution sensors such as MODIS and AVHRR have been useful to study vegetation phenology from regional to global scales. These sensors are, however, not capable of discerning phenological variation at moderate spatial scales. By offering increased observation density and higher spatial resolution, the combination of Landsat and Sentinel-2 time series might provide the opportunity to overcome this limitation.In this study, we analyzed the potential of combined Sentinel-2 and Landsat time series for estimating start of season (SOS) of broadleaf forests across Germany for the year 2018. We tested two common statistical modeling approaches (logistic and generalized additive models using thin plate splines) and the two most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).We found strong agreement between SOS estimates from logistic and spline models (rEVI = 0.86; rNDVI = 0.65), whereas agreement was higher for EVI than for NDVI (RMSDEVI = 3.07, RMSDNDVI = 5.26 days). The choice of vegetation index thus had a higher impact on the results than the fitting method. The EVI-based SOS also showed higher correlation with ground observations compared to NDVI (rEVI = 0.51, rNDVI = 0.42). Data density played an important role in estimating land surface phenology. Models combining Sentinel-2A/B, with an average cloud-free observation frequency of 12 days, were largely consistent with the combined Landsat and Sentinel-2 models, suggesting that Sentinel-2A/B may be sufficient to capture SOS for most areas in Germany in 2018. However, in non-overlapping swath areas and mountain areas, observation frequency was significantly lower, underlining the need to combine Landsat and Sentinel-2 for consistent SOS estimates over large areas. Our study demonstrates that estimating SOS of temperate broadleaf forests at medium spatial resolution has become feasible with combined Landsat and Sentinel-2 time series.  相似文献   

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