Remote classification of Cerrado (Savanna) and agricultural land covers in northeastern Brazil

Remote classification of land-use/land-cover (LULC) types in Brazil's Cerrado ecoregion is necessary because knowledge of Cerrado LULC is incomplete, sources of inaccuracy are unknown, and high-resolution data are required for the validation of moderate-resolution LULC maps. The aim of this research is to discriminate between Cerrado and agriculture using high-resolution Landsat 7 ETM⁺ imagery for the western region of Bahia state in northeastern Brazil. The Maximum Likelihood Classification (MLC) and Spectral Angle Mapper (SAM) algorithms were applied to a ～3000 km² subset, yielding comparable classification accuracies. The panchromatic band was reserved for validation. User's and producer's accuracies were highest for non-irrigated agriculture (～94%) but lower for Cerrado Lato Sensu (89%). Classification errors likely resulted from spatial and spectral characteristics of particular classes (e.g. riparian forest and burned) and overestimation of other classes (e.g. Eucalyptus and water). Manual misinterpretation of validation data may have also led to lower reported classification accuracies.     

Discharge and suspended sediment flux estimated along the mainstream of the Amazon and the Madeira Rivers (from in situ and MODIS Satellite Data)

Water and suspended sediment fluxes are considered during the period 2000–2008 in a region including the full Amazon River from the confluence of the Negro River to Santarém, the end part of the Solimões River, and the lower part of the Madeira River. Three types of data are used: water discharge estimated from field measurements, and suspended sediment obtained from field measurements and derived from MODIS satellite data. A generalized least square method including a propagating term is developed in order to propagate the signal upward and downward the river. The approach is introduced and tested. Several experiments are considered in order, first, to estimate the ability to propagate the signal from stations located before the confluences of Negro and Madeira Rivers to stations located on the Amazon River; second to investigate the possibility to propagate the signal along the Amazon River which dynamics is coupled with floodplains dynamics; and third to produce optimal solutions of water and sediment fluxes. For each experiment, the influence of field and satellite data is compared. The approach is efficient in the upper part of the region of study where the Solimões, the Negro and the Madeira Rivers meet and fails in the lower part of the region where interactions between Amazon River and floodplains play an important role on the fluxes’ dynamics. The optimal experiment includes in situ and satellite data from all the stations available and is used to analyse the recent evolution of suspended sediment flux along the Amazon River and its interaction with the large coupled floodplains. A high accumulation rate is observed during the 2000–2002 period, followed by decreasing rates until 2005 and by increasing values in 2006 and 2007. Our results suggest that floodplains extending along a river reach of 390 km-long between Itacoatiara and Óbidos trap about 15% of the suspended sediment flux passing at Óbidos. The simulated deposition rate is of about 0.3 Mt km⁻¹ yr⁻¹ corresponding to an accretion rate of about 27 mm yr⁻¹.     

Retrieving spatial variations of land surface temperatures from satellite data–Cairo region,Egypt

Cairo region is characterized by a range of physiographic features, including: flat agricultural lands, bare sandy deserts, highlands, calcareous terrains and urban land use. A time series data-set (300 images) acquired from the Moderate Resolution Imaging Spectroradiometer for the period July 2002–June 2015 were utilized to retrieve the spatial variations in the mean land surface temperature (LST) for the above-mentioned surface features. Results showed that vegetation, topography and surface albedo have negative correlations with LST. Vegetation/LST correlation has the maximum regression coefficient (R² = 0.68) and albedo/LST has the minimum (R² = 0.03). Cultivated lands reveal the lowest mean LST (<32 °C), whereas industrial lands exhibit the highest LST (>40 °C) of Cairo region. There is a considerable urban heat island formed at Helwan south of Cairo, where heavy industries are settled. Industrial activities raised the mean LST of the region by at least 4 °C than the surrounding urban lands.     

Investigating flash floods potential areas using ASCAT and TRMM satellites in the Western Cape Province,South Africa

The aim of the study was to evaluate flash flood potential areas in the Western Cape Province of South Africa, by integrating remote sensing products of high rainfall intensity, antecedent soil moisture and topographic wetness index (TWI). Rainfall has high spatial and temporal variability, thus needs to be quantified at an area in real time from remote sensing techniques unlike from sparsely distributed, point gauge network measurements. Western Cape Province has high spatial variation in topography which results in major differences in received rainfall within areas not far from each other. Although high rainfall was considered as the major cause of flash flood, also other contributing factors such as topography and antecedent soil moisture were considered. Areas of high flash flood potential were found to be associated with high rainfall, antecedent precipitation and TWI. Although TRMM 3B42 was found to have better rainfall intensity accuracy, the product is not available in near real time but rather at a rolling archive of three months; therefore, Multi- sensor precipitation estimate rainfall estimates available in near real time are opted for flash flood events. Advanced Scatterometer (ASCAT) soil moisture observations were found to have a reasonable r value of 0.58 and relatively low MAE of 3.8 when validated with in situ soil moisture measurements. The results of this study underscore the importance of ASCAT and TRMM satellite datasets in mapping areas at risk of flooding.     

Detecting understory plant invasion in urban forests using LiDAR

Light detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Ligustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L. sinense occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized into the following groups: overstory, understory, topography, and overall vegetation characteristics, and IKONOS spectral features – optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L. sinense. We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L. sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%. The top performing model from the RF classifier produced the highest kappa of 64.8%, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2% over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L. sinense.     

Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots,UAV-LiDAR data and Sentinel-2 imagery

The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha⁻¹. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R² = 0.62 > 0.52, RMSE = 50.36 Mg ha⁻¹<56.63 Mg ha⁻¹). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R² = 0.62, RMSE = 51.03 Mg ha⁻¹). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.     

Spatial analysis of an urban flash flood survey results

The geographical distribution of a flash flood's occurrence could be an important consideration in assessing its severity and social impacts. In this study, the spatial distribution of survey responses is analysed with respect to features such as the stream network (natural water path), the drainage network, buildings, and catchments. This provides information on both flash flood distribution causes in an urban setting and residents' perceptions of the hazard that would otherwise not be revealed. The study demonstrated the possibility of taking into account people's experience in detecting the location of potential flash flood hazards. It also illustrates the usefulness of GIS-supported spatial analysis of survey results in identifying factors that increase the occurrence of urban flash floods.     

Landscape Dynamics in North East Region of India (Meghalaya State) Using Spatial Decision Tree Model

Abstract

India has figured with two hotspots ‐ the Western Ghats and the Eastern Himalayas ‐ in an identification of 8 ‘hottest’ biodiversity hotspots (Myers et. al. 2000). The Meghalaya state (study area) in North Eastern India lies within the “Indo‐Burma” area, which is one of the 8 ‘hottest’ biodiversity hotspots. Timber extraction, the age‐old practice of shifting cultivation and mining has been a major cause of extensive changes in this landscape. Human induced disturbance differs from natural disturbance especially in extension, severity and frequency. Spatial presentation of landscape dynamics can be used to infer disturbance regimes horizontally. Disturbance regimes are mostly dominated by landuse practices in Meghalaya and these landuse practices are important contributors for overall interpretation of ecological processes operating within the landscape. In this scenario, the development of models to study landscape dynamics using remote sensing and GIS would be of great importance to ecologists. In this paper, we make an attempt to characterize landscape dynamics using a decision tree based approach. The varying impacts of human interventions reflected in three zones at landscape level have been brought out in the present study. The physiographic zones of Meghalaya (viz. Garo hills, Khasi hills and Jaintia hills) manifest different landscape characteristics and present varying degree of degradation status. Garo hills, experiences maximum shifting cultivation and has shown highest dynamism in the study area followed by Jaintia and Khasi hills. Characterization of landscape dynamics is important in the perspective of decision makers and policy makers in order to prioritize conservation strategies, so that urgent and necessary action can be taken.     

Evaluation of water discharge and sediment budget in the lesser Himalaya

This paper deals with the quantitative measurement of water discharge and sediment budget, comparison of the quantitative data of the middle part of the Bino Valley at the boundary of Pauri Garhwal and Almora district of U.P. The Ghatgad Catchment is cultivated (70%), the Gungad Catchment is moderately forested (55%) and the Kolani Catchment is forested (95%). Based on the morphometric and drainage analysis, the water discharge, suspended load yield and rainfall (1995-96) has been computed. The maximum annual average discharge has been observed in moderately forested (Gungad, 70.3 LS^-1) and cultivated (Ghatgad, 67.6 LS^-1) catchments and minimum discharge (24.5 LS^-1) in the forested (Kolani) catchment. The highest monthly suspended load, amounting to 49.28 percent of the total annual load and a denudation rate 8.51 T/km²/month being observed in September for Ghatgad catchment., where as 47.64 percement of the total sediment and 4.06 T/km²/month being observed in August for Gungad catchment. In Kolani the highest monthly concentration of suspended load being (63.98%), of the total annual load and denudation rate 2.75 T/km²/month being observed in the month of June.     

Diurnal rainfall variability over the Upper Blue Nile Basin: A remote sensing based approach

In this study we aim to assess the diurnal cycle of rainfall across the Upper Blue Nile (UBN) basin using satellite observations from Tropical Rainfall Measuring Mission (TRMM). Seven years (2002–2008) of Precipitation Radar (PR) and TRMM Microwave Imager (TMI) data are used and analyses are based on GIS operations and simple statistical techniques. Observations from PR and TMI reveal that over most parts of the basin area, the rainfall occurrence and conditional mean rain rate are highest between mid- and late-afternoon (15:00–18:00 LST). Exceptions to this are the south-west and south-eastern parts of the basin area and the Lake Tana basin where midnight and early morning maxima are observed. Along the Blue Nile River gorge the rainfall occurrence and the conditional mean rain rate are highest during the night (20:00–23:00 LST). Orographic effects by large scale variation of topography, elevation and the presence of the UBN river gorge were assessed taking two transects across the basin. Along transects from north to south and from east to west results indicate increased rainfall with increase of elevation whereas areas on the windward side of the high mountain ranges receive higher amount of rainfall than areas on the leeward side. As such, mountain ranges and elevation affect the rainfall distribution resulting in rain shadow effect in the north-eastern parts of Choke-mountain and the ridges in the north-east of the basin. Moreover, a direct relation between rainfall occurrence and elevation is observed specifically for 17:00–18:00 LST. Further, results indicate that the rainfall distribution in the deeply incised and wide river gorge is affected with relatively low rainfall occurrence and low mean rainfall rates in the gorge areas. Seasonal mean rainfall depth is highest in the south-west area and central highlands of the basin while areas in the north, north-east and along the Blue Nile gorge receive the least amount of rainfall. Statistical results of this work show that the diurnal cycle of rainfall occurrence from TRMM estimates show significant correlation with the ground observations at 95% confidence level. In the UBN basin, the PR conditional mean rain rate estimates are closer to the ground observations than the TMI. Analysis on mean wet season rainfall amount indicates that PR generally underestimates and TMI overestimates the ground observed rainfall.     

Assessment of satellite ocean color products of MERIS,MODIS and SeaWiFS along the East China Coast (in the Yellow Sea and East China Sea)

The validation of satellite ocean-color products is an important task of ocean-color missions. The uncertainties of these products are poorly quantified in the Yellow Sea (YS) and East China Sea (ECS), which are well known for their optical complexity and turbidity in terms of both oceanic and atmospheric optical properties. The objective of this paper is to evaluate the primary ocean-color products from three major ocean-color satellites, namely the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Through match-up analysis with in situ data, it is found that satellite retrievals of the spectral remote sensing reflectance R_rs(λ) at the blue-green and green bands from MERIS, MODIS and SeaWiFS have the lowest uncertainties with a median of the absolute percentage of difference (APD_m) of 15–27% and root-mean-square-error (RMS) of 0.0021–0.0039 sr⁻¹, whereas the R_rs(λ) uncertainty at 412 nm is the highest (APD_m 47–62%, RMS 0.0027–0.0041 sr⁻¹). The uncertainties of the aerosol optical thickness (AOT) τ_a, diffuse attenuation coefficient for downward irradiance at 490 nm K_d(490), concentrations of suspended particulate sediment concentration (SPM) and Chlorophyll a (Chl-a) were also quantified. It is demonstrated that with appropriate in-water algorithms specifically developed for turbid waters rather than the standard ones adopted in the operational satellite data processing chain, the uncertainties of satellite-derived properties of K_d(490), SPM, and Chl-a may decrease significantly to the level of 20–30%, which is true for the majority of the study area. This validation activity advocates for (1) the improvement of the atmosphere correction algorithms with the regional aerosol optical model, (2) switching to regional in-water algorithms over turbid coastal waters, and (3) continuous support of the dedicated in situ data collection effort for the validation task.     

A new gravity map, a new marine geoid around Japan and the detection of the Kuroshio current

About half a million marine gravity measurements over a 30^∘×30^∘ area centered on Japan have been processed and adjusted to produce a new free-air gravity map from a 5′×5′ grid. This map seems to have a better resolution than those previously published as measured by its correlation with bathymetry. The grid was used together with a high-degree and -order spherical harmonics geopotential model to compute a detailed geoid with two methods: Stokes integral and collocation. Comparisons with other available geoidal surfaces derived either from gravity or from satellite altimetry were made especially to test the ability of this new geoid at showing the sea surface topography as mapped by the Topex/Poseidon satellite. Over 2 months (6 cycles) the dynamic topography at ascending passes in the region (23^∘47^∘N and 123^∘147^∘E) was mapped to study the variability of the Kuroshio current. Received: 15 July 1994 / Accepted: 17 February 1997     

利用GPS形变资料确定地壳形变的应变特征

基于弹性力学理论并结合 GPS测量的特点提出了一种利用 GPS形变观测资料确定地壳水平应变场的方法 ,并介绍了在获取应变参数基础上求定剪应变和面膨胀的基本公式。结合华北地区实际的 GPS形变观测资料确定了华北地区的应变场特征 ,获得了较为满意的结果     

利用GPS形变资料确定地壳形变的应变特征

基于弹性力学理论并结合 GPS测量的特点提出了一种利用 GPS形变观测资料确定地壳水平应变场的方法 ,并介绍了在获取应变参数基础上求定剪应变和面膨胀的基本公式。结合华北地区实际的 GPS形变观测资料确定了华北地区的应变场特征 ,获得了较为满意的结果     

利用POLDER数据验证MODIS BRDF模型参数产品及Ross-Li模型

将MODIS BRDF模型参数产品(MCD43A1)模拟的近红外波段反射率与从POLDER-3/PARASOL BRDF全球数据集中筛选的9961个像元的BRDF观测数据进行对比,验证了MCD43A1所采用的RossThick-LiSparseR BRDF 模型(Ross-Li模型)拟合二向反射的能力。结果表明,Ross-Li模型总体上可以有效地模拟地物的二向反射,所有像元的近红外波段反射率模拟值与POLDER-3观测数据之间的R²达到0.943,RMSE为0.016,模拟反射率比POLDER-3数据总体偏低5.2%。但Ross-Li模型明显低估了热点反射率,热点模拟结果比POLDER-3数据平均偏低14%,模拟值的R2为0.824,RMSE为0.07。热点反射率模拟误差与地表覆盖类型有关,针叶林热点反射率模拟值偏低最严重,其次是阔叶林、草地与农田,灌木与裸地热点反射率模拟值偏低相对较小。通过修正Ross-Li模型中的体散射核,可以明显改善热点反射率的模拟效果(R²＝0.839,RMSE＝0.043)。Ross-Li模型对天底、暗点等特征方向反射率的模拟较为准确。Ross-Li模型的模拟精度随太阳天顶角和观测天顶角的增大而降低。对于农田与草地而言,Ross-Li模型的模拟精度随NDVI的增加而降低;但在森林与灌木覆盖条件下,当NDVI约为0.5时,Ross-Li模型的模拟效果最差。     

Performance of GNSS-R GLORI data for biomass estimation over the Landes forest

The Above-Ground Biomass (AGB) is a key parameter used for the modeling of the carbon cycle. The aim of this study is to make an experimental assessment of the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to forest AGB. This is based on the analysis of the data recorded during several GLORI airborne campaigns in June and July 2015, over the Landes Forest (France). Ground truth measurements of tree height, density and diameter at breast height (DBH), as well as AGB, were carried out for 100 maritime pine forest plots of various ages. The GNSS-R data were used to obtain the right-left (Γ_RL) and right-right (Γ_RR) reflectivity observables, which are geo-referenced in accordance with the known positions of relevant GPS satellites and the airborne receiver. The correlations between forest AGB and the GNSS-R observables yield the highest sensitivity at high elevation angles (70°-90°). In this case, for (Γ_RL) and the reflectivity polarization ratio (PR = Γ_RL/Γ_RR) estimated with a coherent integration time Tc = 20 ms, the coefficients of determination R² are equal to 0.67 and 0.51, with a sensitivity of −0.051 dB/[10⁶g (Mg) ha⁻¹], and −0.053 dB/[Mg ha⁻¹], respectively. The relationships between AGB and the observables are confirmed through the use of a 5-fold cross validation approach, with several different coherent integration times.     

Prioritisation of sub-watersheds based on earth observation data of agricultural dominated northern river basin of India

The Upper Tons River Basin of North India has been selected for prioritisation of sub-watersheds (SW) based on morphometric parameters with respect to groundwater derived from topographic sheets and CARTOSAT data. There are 10 SW have been delineated in the region, high stream frequency (F_s) values of SW (1–5) and SW-9 indicated the occurrence of steep slopes, less permeable rocks, greater runoff, less infiltration possibility. Further, these regions have been predicted as poor groundwater potentialities. SW-2 has been identified as poorest groundwater potential zone, whereas SW-4 and SW (6–8) regions possess good permeable bed rocks. The Drainage density (D_d) map demonstrated that the middle south-west region possesses higher D_d whereas northeastern regions contain lower D_d. Further, the areal parameters indicate elongated shape of the basin, hilly region has moderate to steeper ground slope. The outcomes of work have potential to manage groundwater and to ameliorate the flash flood and droughts.     

利用SRTM绘制东Sahara大型古排水盆地及其对南埃及新发展计划的影响(英文)

In the current study, the shuttle radar topography mission (SRTM) data, with～90 m horizontal resolution, were used to delineate the paleodrainage system and their mega basin extent in the East Sahara area. One mega-drainage basin has been detected, covering an area of 256 000 km2. It is classified into two sub mega basins. The Uweinate sub mega basin, which is composed of four main tributaries, collected water from a vast catchment region and drained eastward from the north, west, and southwest, starting at...     

Impact of the spatial resolution of climatic data and soil physical properties on regional corn yield predictions using the STICS crop model

The assimilation of Earth observation (EO) data into crop models has proven to be an efficient way to improve yield prediction at a regional scale by estimating key unknown crop management practices. However, the efficiency of prediction depends on the uncertainty associated with the data provided to crop models, particularly climatic data and soil physical properties. In this study, the performance of the STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard) crop model for predicting corn yield after assimilation of leaf area index derived from EO data was evaluated under different scenarios. The scenarios were designed to examine the impact of using fine-resolution soil physical properties, as well as the impact of using climatic data from either one or four weather stations across the region of interest. The results indicate that when only one weather station was used, the average annual yield by producer was predicted well (absolute error <5%), but the spatial variability lacked accuracy (root mean square error = 1.3 t ha⁻¹). The model root mean square error for yield prediction was highly correlated with the distance between the weather stations and the fields, for distances smaller than 10 km, and reached 0.5 t ha⁻¹ for a 5-km distance when fine-resolution soil properties were used. When four weather stations were used, no significant improvement in model performance was observed. This was because of a marginal decrease (30%) in the average distance between fields and weather stations (from 10 to 7 km). However, the yield predictions were improved by approximately 15% with fine-resolution soil properties regardless of the number of weather stations used. The impact of the uncertainty associated with the EO-derived soil textures and the impact of alterations in rainfall distribution were also evaluated. A variation of about 10% in any of the soil physical textures resulted in a change in dry yield of 0.4 t ha⁻¹. Changes in rainfall distribution between two abundant rainfalls during the growing season led to a significant change in yield (0.5 t ha⁻¹ on average). Our results highlight the importance of using fine-resolution gridded daily precipitation data to capture spatial variations of rainfall as well as using fine-resolution soil properties instead of coarse-resolution soil properties from the Canadian soil dataset, especially for regions with high pedodiversity.     

Brazilian spatial dynamics in the long term (1872–2000): “path dependency” or “reversal of fortune”?

This paper analyzes the spatial dynamics of Brazilian regional inequalities between 1872 and 2000 using contemporary tools. The first part of the paper provides new estimates of income per capita in 1872 by municipality using census and electoral information on income by occupation. The level of analysis is the Minimum Comparable Areas 1872–2000 developed by Reis et al. (áreas mínimas comparáveis para os períodos intercensitários de 1872 a 2000, 2007). These areas are the least aggregation of adjacent municipalities required to allow consistent geographic area comparisons between census years. In the second section of the paper, Exploratory Spatial Data Analysis, Markov chains and stochastic kernel techniques (spatially conditioned) are applied to the dataset. The results suggest that, in broad terms, the spatial pattern of income distribution in Brazil during that period of time has remained stable.