重庆市墒情分析评价预测系统的设计与实现

结合重庆市墒情、水雨情等自动监测系统,考虑主要作物种类、分布区域、播种面积、耕作制度、生育期间各生长发育指标,以及不同区域、深度的田间持水量,对已建立土壤墒情监测点的地区,采用土壤相对湿度评估农业墒情;对于尚未建立墒情监测站但已建立雨量监测站点的雨养农业区,采用降水量距平法或连续无雨日数法,进行墒情分析评价,用衰减系数法预测墒情的变化趋势。采用B/S开发模式,利用Flex通过天地图在线服务进行地图显示,采取IIS发布模式,基于Web Services的数据服务模式,设计一套基于Web GIS的墒情监测分析评价预测系统,通过相关评价指标反映农林作物土壤的干旱情况,并能结合天气情况预测未来墒情数据,为安排农业用水提供技术支撑,减少干旱灾害损失。     

Evaluation of a static water balance model in cropped and grazed systems of temperate Australia

Several water balance models have been developed for Australian conditions, however few of them were developed for the mixed cropping and grazing systems that are typical of temperate south-east Australia. HowLeaky? is a 1-dimensional water balance model that allows simulation of both cropped and grazed systems. This study tested the accuracy of HowLeaky? simulations of soil moisture content and surplus water (runoff plus deep drainage) for mixed farming systems in south-east Australia. Two datasets from the state of Victoria were used to validate model simulations: 1) Rutherglen, consisting of four years soil moisture observations and deep drainage estimates from seven treatments of crop rotations and annual pasture; 2) Vasey, consisting of three years soil moisture and runoff observations and deep drainage estimates from a perennial pasture. A static plant growth option was applied to simulate seasonal crop and pasture covers. Despite the static approach not accounting for inter-annual variation in crop development, HowLeaky? captured observed soil moisture trends, with the Nash Sutcliffe efficiency ranging from fair (0.34) in continuous crop, moderate (0.64–0.68) in annual and perennial pasture, to very good (>0.70) for continuous lucerne and lucerne-crop rotations. Annual water surplus was generally underestimated in this uncalibrated application of the model suggesting a need for some degree of model calibration for highly uncertain and influential parameters such as field capacity.     

Evaluation of soil moisture from satellite observations over South America

A study was performed to evaluate the surface soil moisture derived from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sensor observations over South America. Other soil moisture and rainfall datasets were also used for the analysis. The information for the soil data came from the Eta regional climate model, and for the rainfall data from the Tropical Rainfall Microwave Mission (TRMM) satellite. Statistical analysis was used to evaluate the quality of the soil moisture and rainfall products, with estimates of the correlation coefficient (R), χ² and Cramer's phi (?_c). The results show high correlations (R > 0.8) of the AMSR-E soil moisture products with the Eta model for different regions of South America. Comparison of soil moisture products with rainfall datasets showed that the AMSR-E C-band soil moisture product was highly correlated with the TRMM satellite rainfall datasets, with the highest values of χ² and ?. The results show that the AMSR-E C-band soil moisture products contain important information that can be used for various purposes, such as monitoring floods or droughts in arid areas or as input within the framework of an assimilation scheme of numerical weather prediction models.     

Dynamic response of NDVI to soil moisture variations during different hydrological regimes in the Sahel region

Over the last few decades, the African Sahel has become the focus of many studies regarding vegetation dynamics and their relationships with climate and people. This is because rainfall limits the production of biomass in the region, a resource on which people are directly dependent for their livelihoods. In this study, we utilized a remote-sensing approach to answering the following two questions: (1) how does the dynamic relationship between soil moisture and plant growth vary across hydrological regimes, and (2) are vegetation-type-dependent responses to soil moisture availability detectable from satellite imagery? In order to answer these questions, we studied the relationship between monthly modelled soil moisture as an indicator for water availability and the remotely sensed normalized difference vegetation index (NDVI) as a proxy for vegetation growth between a “recovery rainfall period” (1982 to 1997) and a “stable rainfall period” (1998 to 2013), at different time lags across the Sahel region. Using windowed cross-correlation, we find a strong significant positive relationship between NDVI and soil moisture at a concurrent time and at NDVI lagging behind soil moisture by 1 month for grassland, cropland, and deciduous shrubland vegetation – the dominant vegetation classes in the Sahel. South of the Sahel (the Sudanian and Guinean areas), we find longer optimal lags (soil moisture lagged by 1–3 months) in association with mixed forest and deciduous shrubland. We find no major significant change in optimal lag between the recovery and stable periods in the Sahelian region; however, in the Sudanian and Guinean areas, we observe a trend towards shorter time lags. This change in optimal lag suggests a vegetation change, which may be a response to a climatic shift or land-use change. This approach of identifying spatiotemporal trends in optimal lag correlations between modelled soil moisture and NDVI could prove to be a useful tool for mapping vegetation change and ecosystem behaviour, in turn helping inform climate change mitigation approaches and agricultural planning.     

An algorithm to estimate soil moisture over vegetated areas based on in situ and remote sensing information

An algorithm is proposed for estimating soil moisture over vegetated areas. The algorithm uses in situ and remote sensing information and statistical tools to estimate soil moisture at 1 km spatial resolution and at 20 cm depth over Puerto Rico. Soil moisture within the study region is characterized by spatial and temporal variability. The temporal variability for a given area exhibits long- and short-term variations that can be expressed by two empirical models. The average monthly soil moisture exhibits the long-term variability and is modelled by an artificial neural network (ANN), whereas the short-term variability is determined by hourly variation and is represented by a nonlinear stochastic transfer function model. Monthly vegetation index, land surface temperature, accumulated rainfall and soil texture are the major drivers of the ANN to estimate the monthly soil moisture. Radar, satellite and in situ observations are the major sources of information of the soil moisture empirical models. A self-organized ANN was also used to identify spatial variability to be able to determine a similar transfer function that best resembles the properties of a particular grid point and estimate the hourly soil moisture across the island. Validation techniques reveal an average absolute error of 3.34% of volumetric water content and this result shows that the proposed algorithm is a potential tool for estimating soil moisture over vegetated areas.     

Spatial and temporal patterns of surface soil moisture over India estimated using surface wetness index from SSM/I microwave radiometer

Results from an approach to infer surface soil moisture from time series analysis of surface wetness index derived using the Special Sensor Microwave/Imager (SSM/I) are presented. Soil moisture quantification was based on the study of temporal changes in surface wetness index and its scaling to maximum and air‐dry limits of soil in each grid cell (0.33°). The estimated soil moisture of Illinois, USA was compared with field measured soil moisture (0–10?cm) obtained from the Global Soil Moisture Data Bank. A root mean square error of 7.18% was found between estimated and measured volumetric soil moisture. A consistency in soil moisture and rainfall pattern was found in the un‐irrigated areas of northern India (Jodhpur, Varanasi) and southern India (Madurai), influenced by southwest and northeast monsoons, respectively. Soil moisture of more than 0.30 m³m^?3 was observed in the absence of rainfall due to the irrigation of rice crop in (Punjab) during the pre‐southwest monsoon period (May).     

Investigation of effect of heterogeneities in vegetation and rainfall on simulated SSM/I brightness temperatures

Land surface characteristics: soil and vegetation and rainfall inputs are distributed in nature. Representation of land surface characteristics and inputs in models is lumped at spatial scales corresponding to the grid size or observation density. Complete distributed representation of these characteristics or inputs is infeasible due to excessive computational costs or costs associated with maintaining dense observational networks. The measurements of microwave brightness temperatures by the SSM/I (Special Sensor Microwave Imager) are at resolutions of the order of 56km 56km for 19 GHz and 33 km 33 km for 37 GHz. At these resolutions, soil moisture and vegetation are not homogeneous over the measurement area. The experiments carried out in this study determine the effect of heterogeneities in vegetation (leaf area index) and input rainfall on simulated soil moisture and brightness temperatures and the inversion of brightness temperatures to obtain soil moisture estimates. This study would help us to understand the implications of using the SSM/I microwave brightness temperatures for soil moisture estimation. The consequences of treating rainfall inputs and vegetation over large land surface areas in a lumped fashion is examined. Simpler methods based on dividing the leaf area index or input rainfall into classes rather than explicit representation for representing heterogeneities in leaf area index and spatial distribution of rainfall is tested. It is seen that soil moisture is affected by the representation (lumped vs distributed) of rainfall and not leaf area index. The effect of spatially distributed soil moisture on the inversion of observed SSM/I brightness temperatures to obtain soil moisture estimates is investigated. The inversion process does not exhibit biases in the retrieval of soil moisture. The methodology presented in this paper can be used for any satellite sensor for purposes of analysis and evaluation.     

Analysis of in-situ soil moisture data and validation of SMOS soil moisture products at selected agricultural sites over a tropical region

Calibration and validation activities on Soil Moisture and Ocean Salinity (SMOS)-derived soil moisture products have been conducted worldwide since the data became available, but this has not been the case over tropical regions. This study focuses on the setting up of a soil moisture data collection network over an agricultural site in a tropical region in Peninsular Malaysia and on the validation of SMOS soil moisture products. The in-situ data over a one-and-a-half-year period was analysed and the validation of the SMOS soil moisture products with this in-situ data was conducted. Bias and root mean square error (RMSE) were computed between the SMOS soil moisture products and the in-situ surface soil moisture collected at the satellite passing times (6 am and 6 pm local time). Due to the known limitations of SMOS soil moisture retrieval over vegetated areas with a vegetation water content higher than 5 kg m^?2, an overestimation of SMOS soil moisture products to in-situ data was noticed in this study. The bias ranged from 0.064 to 0.119 m³ m^?3 and the RMSE was from 0.090 to 0.158 m³ m^?3, when both ascending and descending mode data were measured. This RMSE was found to be similar to those of a number of studies conducted previously at different regions. However, a wet bias was found during the validation, while previous validation activities at other locations showed dry biases. The result of this study is useful to support the continuous development and improvement of the SMOS soil moisture retrieval model, aiming to produce soil moisture products with higher accuracy, especially in tropical regions.     

Development and implementation of a generic pasture growth model (CLASS PGM)

This paper describes the development, testing and implementation of a generic pasture growth model (CLASS PGM) which can be used to simulate growth of composite pasture types of multiple species that may be summer or winter active, perennial or annual. The model includes carbon assimilation through photosynthesis and respiration followed by tissue growth, turnover and senescence. Environmental conditions as well as soil water, nutrient and salinity status influences pasture growth and tissue dynamics. The model allows the user to simulate a range of grazing management strategies. Concepts and theoretical basis of the pasture growth model is based upon the detailed technical report on pasture and crop growth modules (Johnson, 2003). For water balance computations, PGM is internally linked to the Richards’ equation - based hydrology tool, Unsaturated Moisture Movement Model U3M-1D (Vaze et al., 2004b) PGM is supported by a windows based user friendly graphical users interface (GUI). The model can be downloaded free from the Catchment Modelling Toolkit website supported by the eWater Cooperative Research Centre (http://www.toolkit.net.au/class). This paper gives an overview of the model structure, model inputs and outputs and the soils related inbuilt database. Results from model validation using long term observed data for soil moisture, pasture herbage mass and grazing for a grazing experiment at Wagga Wagga, New South Wales, Australia are discussed. When compared with herbage mass and soil moisture data from the experiment, PGM was found to adequately simulate the patterns and amplitudes of pasture growth and soil moisture recorded in the experiment.     

用Landsat TM资料分析浑善达克沙地东部的植被覆盖变化及其可能原因探讨

中纬度干旱半干旱草原是我国的主要地表类型之一,正在面临着严重的荒漠化问题。选择浑善达克沙地东部为研究地区,利用Landsat TM和ETM+资料对该地区的植被覆盖情况进行分析。在所选的4个年度中,以1996年的NDVI值为最高(0.67),1998年略次之(0.65),1987年居中(0.47),而2001年为最低,仅0.33。水分是决定干旱半干旱地区植被生长状况的关键因子。无论是年降水还是7、8月降水,与9月的NDVI都基本上呈现了一种线性关系。气温对NDVI的影响不明显。反映地表与植物冠层表面温度的热红外亮温经常与区域内的NDVI分布基本呈反相关。少数地区,特别是一些本身植被状况较差、生态相对更为脆弱的地区,植被遭到破坏后,即使在保证降水增加的情况下也不能在短时间内恢复。比较1996和1998年,荒漠化面积增长呈现出明显的上升趋势。2001年是严重干旱年,区域平均NDVI极低,但却有7.5%的地区出现NDVI增加。这一逆向变化可能与中央和地方政府于2000年启动的一系列沙源治理项目和禁牧规定有直接关系。由此说明,除了降水这一关键自然因子之外,人类活动也对干旱半干旱草原生态的恶化或恢复起着非常重要的作用。     

Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions

Irrigated agriculture is an important strategic sector in arid and semi-arid regions. Given the large spatial coverage of irrigated areas, operational tools based on satellite remote sensing can contribute to their optimal management. The aim of this study was to evaluate the potential of two spectral indices, calculated from SPOT-5 high-resolution visible (HRV) data, to retrieve the surface water content values (from bare soil to completely covered soil) over wheat fields and detect irrigation supplies in an irrigated area. These indices are the normalized difference water index (NDWI) and the moisture stress index (MSI), covering the main growth stages of wheat. These indices were compared to corresponding in situ measurements of soil moisture and vegetation water content in 30 wheat fields in an irrigated area of Morocco, during the 2012–2013 and 2013–2014 cropping seasons. NDWI and MSI were highly correlated with in situ measurements at both the beginning of the growing season (sowing) and at full vegetation cover (grain filling). From sowing to grain filling, the best correlation (R² = 0.86; p < 0.01) was found for the relationship between NDWI values and observed soil moisture values. These results were validated using a k-fold cross-validation methodology; they indicated that NDWI can be used to estimate and map surface water content changes at the main crop growth stages (from sowing to grain filling). NDWI is an operative index for monitoring irrigation, such as detecting irrigation supplies and mitigating wheat water stress at field and regional levels in semi-arid areas.     

Quantifying characteristic growth dynamics in a semi-arid grassland ecosystem by predicting short-term NDVI phenology from daily rainfall: a simple four parameter coupled-reservoir model

Predicting impacts on phenology of the magnitude and seasonal timing of rainfall pulses in water-limited grassland ecosystems concerns ecologists, climate scientists, hydrologists, and a variety of stakeholders. This report describes a simple, effective procedure to emulate the seasonal response of grassland biomass, represented by the satellite-based normalized difference vegetation index (NDVI), to daily rainfall. The application is a straightforward adaptation of a staged linear reservoir that simulates the pulse-like entry of rainwater into the soil and its redistribution as soil moisture, the uptake of water by plant roots, short-term biomass development, followed by the subsequent transpiration of water through foliage. The algorithm precludes the need for detailed, site specific information on soil moisture dynamics, plant species, and the local hydroclimate, while providing a direct link between discrete rainfall events and consequential biomass responses throughout the growing season. We applied the algorithm using rainfall data from the Central Plains Experimental Range to predict vegetation growth dynamics in the semi-arid shortgrass steppe of North America. The mean annual rainfall is 342 mm, which is strongly bifurcated into a dominantly ‘wet’ season, where during the three wettest months (May, June and July) the mean monthly rainfall is approximately 55 mm month^?1; and a ‘dry’ season, where during the three driest months (December, January and February), the mean monthly rainfall is approximately 7 mm month^?1. NDVI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 16 day, 250 m × 250 m product were used as a proxy for grassland phenology for the period-of-record 2000–2013. Allowing for temporal changes in basic parameters of the response function over the growing season, the predicted response of the model tracks the observed NDVI metric with correlation coefficients exceeding 0.92. A two-stage series reservoir is preferred, whereby the characteristic time for transfer of a rainfall event to the peak response of NDVI decreases from 24 days (early growing season) to 12 days (late growing season), while the efficiency of a given volume of rainfall to produce a correspondingly similar amount of aboveground biomass decreases by a factor of 40% from April to October. Behaviours of the characteristic time of greenup and loss of rainfall efficiency with progression of the growing season are consistent with physiological traits of cool-season C₃ grasses versus warm-season C₄ grasses, and with prior research suggesting that early season production by C₃ grasses is more responsive to a given amount of precipitation than mid-summer growth of C₄ shortgrasses. Our model explains >90% of seasonal biomass dynamics. We ascribe a systematic underprediction of observed early season greenup following drought years to a lagged or ‘legacy’ effect, as soil inorganic nitrogen, accumulated during drought, becomes available for future plant uptake.     

A simple model to predict soil moisture: Bridging Event and Continuous Hydrological (BEACH) modelling

This paper introduces a simple two-layer soil water balance model developed to Bridge Event And Continuous Hydrological (BEACH) modelling. BEACH is a spatially distributed daily basis hydrological model formulated to predict the initial condition of soil moisture for event-based soil erosion and rainfall–runoff models. The latter models usually require the spatially distributed values of antecedent soil moisture content and other input parameters at the onset of an event. BEACH uses daily meteorological records, soil physical properties, basic crop characteristics and topographical data. The basic processes incorporated in the model are precipitation, infiltration, transpiration, evaporation, lateral flow, vertical flow and plant growth. The principal advantage of this model lies in its ability to provide timely information on the spatially distributed soil moisture content over a given area without the need for repeated field visits. The application of this model to the CATSOP experimental catchment showed that it has the capability to estimate soil moisture content with acceptable accuracy. The root mean squared error of the predicted soil moisture content for 6 monitored locations within the catchment ranged from 0.011 to 0.065 cm³ cm^?3. The predicted daily discharge at the outlet of the study area agreed well with the observed data. The coefficient of determination and Nash–Sutcliffe efficiency of the predicted discharge were 0.824 and 0.786, respectively. BEACH has been developed within freely available GIS and programming language, PCRaster. It is a useful teaching tool for learning about distributed water balance modelling and land use scenario analysis.     

Modelling yearly flooding extent of the Waza-Logone floodplain in northern Cameroon based on MODIS and rainfall data

The Sahelian floodplains are of high ecological and economical importance, providing water and fresh pasture in the dry season. A spatial model is presented to predict the yearly flooding extent of the Waza-Logone floodplain based on cumulative runoff in the catchment area and estimations of the soil moisture prior to the flooding. Observations of flooding extent were based on thresholding 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) shortwave infrared (SWIR) images. The Soil Conservation Service Curve Number (SCS-CN) method was used to calculate cumulative runoff within the Logone catchment area based on rainfall estimates (RFEs) for Africa. MODIS SWIR images acquired prior to the flooding were used as indicators for soil moisture. The mean observed flooding extent of the Waza-Logone floodplain during the period 2000–2005 was 6747 km² with a standard deviation of 1838 km². Multiple regression analysis was performed to create a predictive model forecasting flooding extent 1.5 months in advance with a coefficient of determination (R ²) equal to 0.957. Multiple regression modelling was also performed for three subregions separately. The 1.5-month forecast model for the Waza subregion resulted in the highest accuracy (R ²?=?0.950). A floodwater distribution map was created for this subregion model, allowing determination where the flooding occurs for an estimated flood size. The average additional error caused by the mapping procedure was 138 km², which is relatively small compared to an average flooded area of 3211 km² for the Waza subregion. As the flooding extent in the Waza-Logone floodplain is highly correlated to the amount of natural resources available in the dry season, the model may be a valuable tool for sustainable management of these resources.     

Water resources

Abstract

Water, which was taken for granted and assumed to exist in abundance, has become one of our most precious natural resources. Hence hydrological and water management problems represent key areas of applied science in the current worldwide economic and ecological situations. Satellite remote sensing technologies have been used more and more to satisfy urgent requirements for information relating to water resources. Many efforts have been undertaken in Europe to develop appropriate methods and to advance these systems to an operational level. Main emphasis is given to processes in which land surfaces and inland waters are involved, dealing with the estimation of precipitation, the assessment of soil moisture, surface runoff and the forecasting of runoff, be it from rainfall or from snowmelt. Remote sensing methods are of fundamental importance for soil moisture monitoring on a global and on a regional scale. Experimental research has demonstrated that if, in principle, observations in the visible and thermal infrared regions of the electromagnetic spectrum are able to detect the surface moisture of bare soils, microwave sensors are the most promising tools for quantitative estimates of this parameter. A valuable amount of research on this subject has been performed in Europe and is reviewed within the framework of the most advanced international research. Within Europe regional or local aspects and problems dominate. Therefore the efforts and achievements concerned with these areas and applications, and the progress towards operational use, will be focused on primarily. The most advanced and most promising operational applications exist in the field of snow hydrology to improve power production.     

Simulation of nitrous oxide and nitric oxide emissions from tropical primary forests in the Costa Rican Atlantic Zone

Nitrous oxide (N₂O) and nitric oxide (NO) are important atmospheric trace gases participating in the regulation of global climate and environment. Predictive models on the emissions of N₂O and NO emissions from soil into the atmosphere are required. We modified the CENTURY model (Soil Sci. Soc. Am. J., 51 (1987) 1173) to simulate the emissions of N₂O and NO from tropical primary forests in the Atlantic Zone of Costa Rica at a monthly time step. Combined fluxes of N₂O and NO were simulated as a function of gross N mineralization and water-filled pore space (WFPS). The coefficients for partitioning N₂O from NO were derived from field measurements (Global Biogeochem. Cycles, 8 (1994) 399). The modified CENTURY was calibrated against observations of carbon stocks in various pools of forest ecosystems of the region, and measured WFPS and emission rates of N₂O and NO from soil to the atmosphere.WFPS is an important factor regulating nutrient cycling and emissions of N2O and NO from soils making the accuracy of the WFPS prediction central to the modeling process. To do this, we modified the hydrologic submodel and developed a new method for the prediction of WFPS at the monthly scale from daily rainfall information. The new method is based on: (1) the relationship between monthly rainfall and the number of rainfall events, and (2) the relative cumulative frequency distribution of ranked daily rainfall events. The method is generic and should be applicable to other areas.Simulated monthly average WFPS was 0.68±0.02 — identical with the field measurement average of 0.68±0.02 from the annual cycle observed by Keller and Reiners (Global Biogeochem. Cycles, 8 (1994) 399). Simulated fluxes of N₂O and NO were 52.0±9.4 mg-N m⁻² month⁻¹ and 6.5±0.7 mg-N m⁻² month⁻¹, respectively, compared with measured averages of 48.2±11.0 mg-N m⁻² month⁻¹ and 7.1±1.1 mg-N m⁻² month⁻¹. The simulated N₂O/NO ratio was 11.2±1.9 compared with the measured value of 10.9±4.7.WFPS is the dominant determinant of the fraction of gross N mineralization that is emitted from the soil as N₂O and NO. If WFPS were not limiting during part of the year, this fraction would be 4.2%. With some periods of lower WFPS, the realized fraction is 2.2%. Because of the strong relationships between N₂O and NO emission rates and rainfall and its derivative, WFPS, these moisture variables can be used to scale up nitrogen trace gas fluxes from sites to larger spatial scales.     

Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery

Soil moisture is important information in semiarid rangelands where vegetation growth is heavily dependent on the water availability. Although many studies have been conducted to estimate moisture in bare soil fields with Synthetic Aperture Radar (SAR) imagery, little success has been achieved in vegetated areas. The purpose of this study is to extract soil moisture in sparsely to moderately vegetated rangeland surfaces with ERS-2/TM synergy. We developed an approach to first reduce the surface roughness effect by using the temporal differential backscatter coefficient (Δσ_wet-dry⁰). Then an optical/microwave synergistic model was built to simulate the relationship among soil moisture, Normalized Difference Vegetation Index (NDVI) and Δσ_wet-dry⁰. With NDVI calculated from TM imagery in wet seasons and Δσ_wet-dry⁰ from ERS-2 imagery in wet and dry seasons, we derived the soil moisture maps over desert grass and shrub areas in wet seasons. The results showed that in the semiarid rangeland, radar backscatter was positively correlated to NDVI when soil was dry (m_v<10%), and negatively correlated to NDVI when soil moisture was higher (m_v>10%). The approach developed in this study is valid for sparse to moderate vegetated areas. When the vegetation density is higher (NDVI>0.45), the SAR backscatter is mainly from vegetation layer and therefore the soil moisture estimation is not possible in this study.     

Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data

There has been growing concern about land use/land cover change in tropical regions, as there is evidence of its influence on the observed increase in atmospheric carbon dioxide concentration and consequent climatic changes. Mapping of deforestation by the Brazil's National Space Research Institute (INPE) in areas of primary tropical forest using satellite data indicates a value of 587,727 km² up to the year 2000. Although most of the efforts have been concentrated in mapping primary tropical forest deforestation, there is also evidence of large-scale deforestation in the cerrado savanna, the second most important biome in the region.The main purpose of this work was to assess the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon (BLA) in 2000, using a set of multitemporal images from the 1-km SPOT-4 VEGETATION (VGT) sensor. Additionally, we discriminated primary tropical forest, cerrado savanna, and natural/artificial waterbodies. Four classification algorithms were tested: quadratic discriminant analysis (QDA), simple classification trees (SCT), probability-bagging classification trees (PBCT), and k-nearest neighbors (K-NN). The agriculture/pasture class is a surrogate for those areas cleared of its original vegetation cover in the past, acting as a source of carbon. On the contrary, the secondary succession forest class behaves as a sink of carbon.We used a time series of 12 monthly composite images of the year 2000, derived from the SPOT-4 VGT sensor. A set of 19 Landsat scenes was used to select training and testing data. A 10-fold cross validation procedure rated PBCT as the best classification algorithm, with an overall sample accuracy of 0.92. High omission and commission errors occurred in the secondary succession forest class, due to confusion with agriculture/pasture and primary tropical forest classes. However, the PBCT algorithm generated the lower misclassification error in this class. Besides, this algorithm yields information about class membership probability, with ∼80% of the pixels with class membership probability greater or equal than 0.8. The estimated total area of agriculture/pasture and secondary succession forest in 2000 in the BLA was 966 × 10³ and 140 × 10³ km², respectively. Comparison with an existing land cover map indicates that agriculture/pasture occurred primarily in areas previously occupied by primary tropical forest (46%) and cerrado savanna (33%), and also in transition forest (19%), and other vegetation types (2%). This further confirms the existing evidence of extensive cerrado savanna conversion.This study also concludes that SPOT-4 VGT data are adequate for discriminating several major land cover types in tropical regions. Agriculture/pasture was mapped with errors of about 5%. Very high classification errors were associated with secondary succession forest, suggesting that a different methodology/sensor has to be used to address this difficult land cover class (namely with the inclusion of ancillary data). For the other classes, we consider that accurate maps can be derived from SPOT-4 VGT data with errors lower than 20% for the cerrado savanna, and errors lower than 10% for the other land cover classes. These estimates may be useful to evaluate impacts of land use/land cover change on the carbon and water cycles, biotic diversity, and soil degradation.     

Modelling the ancient maize agriculture potential of landforms in Tikal National Park,Guatemala

The ancient polity of Tikal has been extensively studied by archaeologists and soil scientists, but more information is needed to determine the specific subsistence and ancient farming techniques that sustained its inhabitants for more than eight centuries. Recent settlement, soil resource, and vegetation surveys were completed during a re-evaluation of the earthworks of Tikal. The objective of this research was to combine non-parametric multiplicative regression, stable carbon isotopes, soil properties, and remote-sensing data for cost-effective, predictive modelling of ancient maize (Zea mays L.) agricultural areas. The study area is positioned within a 300 km² region surrounding the Tikal site centre (17° 13′ 19.0″ N, 89° 27′ 25.2″ W). In the study area, ancient maize agriculture was determined using carbon (C) isotopic signatures of C₄ vegetation that were incorporated into the soil humin fraction. Probability models predicting C isotopic enrichment were used to outline areas of potential long-term maize agriculture. A binomial model predicted areas mainly along the bajo margins, indicating that these areas may have been used for sustained ancient maize agriculture. Upland areas with shallow Rendolls soil were found to lack strong C₄ isotopic signatures. They showed a long-term C₃ vegetation signature that could have supported native forest, silvicultural activities, or C₃ cropping practices (i.e. forest gardens). The mosaic of C₃ and C₄ signatures within the study site soils suggests that the Tikal area supported a variety of food production systems.     

Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote-sensing methods have the spatial and temporal resolution required to map hazard increases. Here, a dynamic physically-based slope stability model that requires soil moisture is applied using remote-sensing products from multiple Earth observing platforms. The resulting landslide susceptibility maps using the advanced microwave scanning radiometer (AMSR-E) surface soil moisture are compared to those created using variable infiltration capacity (VIC-3L) modeled soil moisture at Cleveland Corral landslide area in California, US. Despite snow cover influences on AMSR-E surface soil moisture estimates, a good relationship between the downscaled AMSR-E's surface soil moisture and the VIC-3L modeled soil moisture is evident. The AMSR-E soil moisture mean (0.17 cm³/cm³) and standard deviation (0.02 cm³/cm³) are very close to the mean (0.21 cm³/cm³) and standard deviation (0.09 cm³/cm³) estimated by VIC-3L model. Qualitative results show that the location and extent of landslide prone regions are quite similar. Under the maximum saturation scenario, 0.42% and 0.49% of the study area were highly susceptible using AMSR-E and VIC-3L model soil moisture, respectively.