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Early or in-season crop type mapping using remote sensing data is important for crop managements to maximize crop yield. Existing remote sensing-based approaches in the literature rely mainly on the timing and temporal frequency of satellite data acquisition during a growing season to obtain optimal identification feature (OIF) to complete crop mapping. At present, the optimal features for mapping crop types are commonly generated by using different feature selection methods, and thereby are not generic in nature. In this paper, a novel approach based on the OIF knowledge graph is proposed for identification of crop types within the crop growing season. The concept of OIF was proposed based on the one-class classification and optimization theory, which relates a specific crop type to its OIF. The OIF knowledge graph can be created to describe the seasonal trend of OIF in a season. Eventually, the OIF knowledge graphs can be incorporated into the moment-preserving segmentation method to automatically identify crop types within the growing season. To evaluate the performance of the new method, crop mapping was conducted using 2019 Sentinel-2 data in Heilongjiang province, China, and Illinois, USA, respectively. Results demonstrate that integrating the OIF knowledge graph and moment-preserving thresholding method is effective in identifying crop types without ground-truth data. The overall accuracy higher than 90 % (Kappa coefficient greater than 0.90), and the producer's accuracies for corn, soybean, and rice are generally greater than 93 % in Heilongjiang province. The proposed approach has the advantage of simplifying crop identification process while maintaining a satisfactory identification accuracy in shorter time than conventional methods. [ABSTRACT FROM AUTHOR]

• The community livability evaluation is quantitative, practical and fine-grained. • The evaluation criteria span dimensions of environment, traffic and population. • Remote sensing, surface observation and geospatial big data are used. • Most communities are distributed within the central and marginal urban areas. • Communities in urban area tend to have robustly high livability. Effective evaluation of community livability is in urgent need to avoid increased livability at the expense of sustainability. However, studies concerning community livability evaluation were still conceptual, qualitative or conducted at city or regional scales. The availability of abundant, fine-grained, and multi-source data in the big data era laid the foundation for comprehensive livability evaluation at much finer scales. This paper proposed a quantitative and practical method for up-to-date livability evaluation at individual community scale in China. Nine evaluation criteria were identified spanning dimensions of environment, traffic, convenience, and population. These criteria were calculated respectively from remote sensing, surface observation and geospatial big data. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied for community livability evaluation, and the uncertainty and sensitivity of evaluation results were assessed. The livability evaluation in the case study area of Haidian District, Beijing, China demonstrated the practicality and effectiveness of the framework. A total number of 1242 communities in Haidian District were evaluated. Communities in urban area were generally associated with higher evaluation scores and lower uncertainties than those in rural area. The careful selection of criteria weights with high sensitivity, i.e., green space coverage within community and driving time to schools, can potentially significantly reduce the uncertainty of the livability evaluation. The community scale livability evaluation is expected to bridge the gap between theoretical concepts and practical implementations of livability evaluation, and enables the development of more effective and locally specific regulations and policies to improve community livability. [ABSTRACT FROM AUTHOR]

In this study, an empirical assessment approach for the risk of crop loss due to water stress was developed and used to evaluate the risk of winter wheat loss in China, the United States, Germany, France and the United Kingdom. We combined statistical and remote sensing data on crop yields with climate data and cropland distribution to model the effect of water stress from 1982 to 2011. The average value of winter wheat loss due to water stress for the three European countries was about −931 kg/ha, which was higher than that in China (−570 kg/ha) and the United States (−367 kg/ha). Our study has important implications for the operational assessment of crop loss risk at a country or regional scale. Future studies should focus on using higher spatial resolution remote sensing data, combining actual evapotranspiration to estimate water stress, improving the method for downscaling of statistical crop yield data and establishing more sophisticated zoning methods. [ABSTRACT FROM AUTHOR]

In this study, an object-based image analysis (OBIA) approach was developed to classify field crops using multi-temporal SPOT-5 images with a random forest (RF) classifier. A wide range of features, including the spectral reflectance, vegetation indices (VIs), textural features based on the grey-level co-occurrence matrix (GLCM) and textural features based on geostatistical semivariogram (GST) were extracted for classification, and their performance was evaluated with the RF variable importance measures. Results showed that the best segmentation quality was achieved using the SPOT image acquired in September, with a scale parameter of 40. The spectral reflectance and the GST had a stronger contribution to crop classification than the VIs and GLCM textures. A subset of 60 features was selected using the RF-based feature selection (FS) method, and in this subset, the near-infrared reflectance and the image acquired in August (jointing and heading stages) were found to be the best for crop classification. [ABSTRACT FROM AUTHOR]

Human diets strongly rely on wheat, maize, rice and soybean; research on the potential crop productivity of these four main crops could provide the basis for increasing global crop yields. The evaluation model of realistic potential crop productivity based on remote sensing and agro-ecological zones was proposed in this study to provide reliable reference data for world food security. The statistical data on these four main crops yields were obtained from the FAO. The model was used to investigate the potential production of four staple crops in the world. The distributions of the realistic potential productivity of four staple crops (winter wheat, maize, rice and soybean) were produced. In the main producing countries of the four staple crops, statistical analysis was conducted on the realistic potential productivity (RPP) of the four staple crops, the highest productivity (HP) during the period 1983-2011 and the gap between RPP and HP. [ABSTRACT FROM AUTHOR]

Automatic land cover update was an effective means to obtain objective and timely land cover maps without human disturbance. This study investigated the efficacy of multi-temporal remote sensing data and advanced non-parametric classifier on improving the classification accuracy of the automatic land cover update approach integrating iterative training sample selection and Markov Random Fields model when the historical remote sensing data were unavailable. The results indicated that two-temporal remote sensing data acquired in one crop growth season could significantly improve the classification accuracy of the automatic land cover update approach by approximately 3–4%. However, the support vector machine (SVM) classifier was not suitable to be integrated in the automatic land cover update approach, because the huge initially selected training samples made the training of the SVM classifier unrealizable. [ABSTRACT FROM PUBLISHER]

Crop type identification is the basis of crop acreage estimation and plays a key role in crop production prediction and food security analysis. However, the accuracy of crop type identification using remote-sensing data needs to be improved to support operational agriculture-monitoring tasks. In this paper, a new method integrating high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data is proposed to improve crop type identification accuracy in Hungary. Four crop growth features, including peak value, date of peak occurrence, average rate of green-up, and average rate for the senescence period were extracted from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) profiles and spatially enhanced to 30 m resolution using resolution merge tools based on a multiplicative method to match the spatial resolution of Landsat Thematic Mapper (TM) data. A maximum likelihood classifier (MLC) was used to classify the TM and merged images. Independent validation results indicated that the average overall classification accuracy was improved from 92.38% using TM to 94.67% using the merged images. Based on the classification results using the proposed method, acreages of two major summer crops were estimated and compared to statistical data provided by the United States Department of Agriculture (USDA). The proposed method was able to achieve highly satisfactory crop type identification results. [ABSTRACT FROM PUBLISHER]

Abstract: This study presents a crop planting and type proportion (CPTP) method for crop acreage estimation of complex and diverse agricultural landscapes. CPTP has three major components: (1) Crop planting proportion (CPP), estimated with wide-swath satellite remote sensing data to completely cover the monitoring area by segmenting cropped and non-cropped areas through unsupervised classification. (2) Crop type proportion (CTP), estimated by transect sampling and a special GPS-Video-GIS instrument (GVG) and a visual interpretation of crop type proportion in collected pictures for different strata. (3) Multiplication of CPP and CTP with arable land area at the strata level, summed to the province and national level. Validation has been done with in situ data for different agricultural landscapes over China. Both CPP estimation with remote sensing data and CTP estimation through ground survey have a high accuracy with average relative error (RE) and root mean square error (RMSE) equal to 1.42% and 1.67% for CPP and to 2.63% and 2.25% for CTP. The RE for crop acreage estimation equals to 4.09%. The CPTP method thus has a high accuracy, yields timely information at low costs, and is robust and provides objective results. The study concludes that the CPTP method can be used for large area crop acreage estimation of complex agriculture landscapes. [Copyright &y& Elsevier]

Crop classification is a key issue for agricultural monitoring using remote-sensing techniques. Synthetic aperture radar (SAR) data are attractive for crop classification because of their all-weather, all-day imaging capability. The objective of this study is to investigate the capability of SAR data for crop classification in the North China Plain. Multi-temporal Envisat advanced synthetic aperture radar (ASAR) and TerraSAR data were acquired. A support vector machine (SVM) classifier was selected for the classification using different combinations of these SAR data and texture features. The results indicated that multi-configuration SAR data achieved satisfactory classification accuracy (best overall accuracy of 91.83%) in the North China Plain. ASAR performed slightly better than TerraSAR data acquired in the same time span for crop classification, while the combination of two frequencies of SAR data (C- and X-band) was better than the multi-temporal C-band data. Two temporal ASAR data acquired in late jointing and flowering periods achieved sufficient classification accuracy, and adding data to the early jointing period had little effect on improving classification accuracy. In addition, texture features of SAR data were also useful for improving classification accuracy. SAR data have considerable potential for agricultural monitoring and can become a suitable complementary data source to optical data. [ABSTRACT FROM AUTHOR]

Abstract: Crop acreage estimation is a key aspect to forecast crop production. Maize acreage estimation becomes more and more important because the fast production changes every year due to the dynamics of the prices. This paper focuses on maize acreage estimation in the North China Plain using ENVISAT MERIS and CBERS-02B CCD data of 2008. Firstly, adaptive maximum likelihood classification of CBERS-02B CCD images based on ground survey provided reliable maize area fraction image (AFI). CBERS derived AFIs (as reference AFI) were used to train a 3-layer back-propagation neural network, this was then used to the whole MERIS data to generate MERIS AFIs (AFIe). To estimate maize acreage, the maize AFI from MERIS was masked with cropland dataset and maize acreages were estimated by zonal statistic of maize AFI at district level. The statistical results were also modified using a non-arable coefficient to remove the effects of non-arable factors. The results showed a close relationship between estimated and statistical maize acreage (R 2 ≈0.88). At province level, the estimation error is approximately 8%. This method is valuable for wide-scale, regional crop acreage estimation at the early stage of growing season. The study gives suggestions about high resolution image acquisition, spatial distribution and cropland datasets. [Copyright &y& Elsevier]

• NNVAI is proposed to indicate winter wheat damage caused by a single frost event. • Growth-stage-specific relations between NNVAI and frost features were explored. • Winter wheat is sensitive to different features of frost at different growth stages. • Developed monitoring framework to do two-phase assessment of winter wheat damage. Frost events pose a threat to normal development of winter wheat and increase risk of production loss, even given the context of global warming. On the Huang-Huai-Hai Plain (HHHP), which is the largest region of winter wheat cultivation in China, frost damage has become one of the major meteorological catastrophes. In recent years, both frequency of occurrence and severity of frost events in this region have exhibited an upward trend. For timely and accurate monitoring of single-event frost damage of winter wheat, this study developed a frost-monitoring framework that considers the processes of the evolution of a frost event and the response of winter wheat, as well as the lag between them. The framework adopts a NDVI valley area index (NVAI) to represent the gradual response process of winter wheat to frost. In this framework, the normalized NVAI (NNVAI) is used to indicate the degree of damage, and a two-phased damage assessment (i.e., T1 and T2) is adopted to resolve the contradiction between the urgency of obtaining information on frost severity, the spatial distribution of frost damage, and the delayed response process of winter wheat to frost. Using prediction models based on historical statistics, assessment at the T1 stage estimates the possible damage after the end of the frost, and assessment at the T2 stage evaluates the actual damage caused by frost when the winter wheat has recovered. Application of the proposed framework to a frost event that occurred on the HHHP during March 4–9, 2018 revealed that the average absolute percentage error between actual and predicted damage across the entire region was 33 %. Moreover, the prediction of damage was made 13 days ahead of the occurrence of actual damage. Thus, the framework could provide guidance to enable action to be taken to reduce the impact of frost. The proposed monitoring framework fully considers the processes of the evolution of a frost event and the response of winter wheat to frost, and it learns potential patterns from historical frost events to provide early prediction of potential frost damage. Consequently, the framework is considered suitable for wide practical application. [ABSTRACT FROM AUTHOR]

Grape is an economic crop of great importance and is widely cultivated in China. With the development of remote sensing, abundant data sources strongly guarantee that researchers can identify crop types and map their spatial distributions. However, to date, only a few studies have been conducted to identify vineyards using satellite image data. In this study, a vineyard is identified using satellite images, and a new approach is proposed that integrates the continuous wavelet transform (CWT) and a convolutional neural network (CNN). Specifically, the original time series of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and green chlorophyll vegetation index (GCVI) are reconstructed by applying an iterated Savitzky-Golay (S-G) method to form a daily time series for a full year; then, the CWT is applied to three reconstructed time series to generate corresponding scalograms; and finally, CNN technology is used to identify vineyards based on the stacked scalograms. In addition to our approach, a traditional and common approach that uses a random forest (RF) to identify crop types based on multi-temporal images is selected as the control group. The experimental results demonstrated the following: (i) the proposed approach was comprehensively superior to the RF approach; it improved the overall accuracy by 9.87% (up to 89.66%); (ii) the CWT had a stable and effective influence on the reconstructed time series, and the scalograms fully represented the unique time-related frequency pattern of each of the planting conditions; and (iii) the convolution and max pooling processing of the CNN captured the unique and subtle distribution patterns of the scalograms to distinguish vineyards from other crops. Additionally, the proposed approach is considered as able to be applied to other practical scenarios, such as using time series data to identify crop types, map landcover/land use, and is recommended to be tested in future practical applications. [ABSTRACT FROM AUTHOR]

Many methods have been used to generate gridded population maps by downscaling demographic data. As one of these methods, the accuracy of the dasymetric model depends heavily on the covariates. Point-of-interest (POI) data, as important covariates, have been widely used for population estimation. However, POIs are often used indiscriminately in existing studies. A few studies further used selected categories of POIs identified based only on the nonspatial quantitative relationship between the POIs and population. In this paper, the spatial association between the POIs and population distribution was considered to identify the POIs with a strong spatial correlation with the population distribution, i.e., population-sensitive POIs. The ability of population-sensitive POIs to improve the fine-grained population mapping accuracy was explored by comparing the results of random forest dasymetric models driven by population-sensitive POIs, all POIs, and no POIs, along with the same sets of multisource remote sensing and social sensing data. The results showed that the model driven by population-sensitive POI had the highest accuracy. Population-sensitive POIs were also more effective in improving the population mapping accuracy than were POIs selected based only on their quantitative relationship with the population. The model built using population-sensitive POIs also performed better than the two popular gridded population datasets WorldPop and LandScan. The model we proposed in this study can be used to generate accurate spatial population distribution information and contributes to achieving more reliable analyses of population-related social problems. [ABSTRACT FROM AUTHOR]

Currently, remote sensing crop identification is mostly based on all available images acquired throughout crop growth. However, the available image and data resources in the early growth stage are limited, which makes early crop identification challenging. Different crop types have different phenological characteristics and seasonal rhythm characteristics, and their growth rates are different at different times. Therefore, making full use of crop growth characteristics to augment crop growth difference information at different times is key to early crop identification. In this study, we first calculated the differential features between different periods as new features based on images acquired during the early growth stage. Secondly, multi-temporal difference features of each period were constructed by combination, then a feature optimization method was used to obtain the optimal feature set of all possible combinations in different periods and the early key identification characteristics of different crops, as well as their stage change characteristics, were explored. Finally, the performance of classification and regression tree (Cart), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM) classifiers in recognizing crops in different periods were analyzed. The results show that: (1) There were key differences between different crops, with rice changing significantly in period F, corn changing significantly in periods E, M, L, and H, and soybean changing significantly in periods E, M, N, and H. (2) For the early identification of rice, the land surface water index (LSWI), simple ratio index (SR), B11, and normalized difference tillage index (NDTI) contributed most, while B11, normalized difference red-edge3 (NDRE3), LSWI, the green vegetation index (VIgreen), red-edge spectral index (RESI), and normalized difference red-edge2 (NDRE2) contributed greatly to corn and soybean identification. (3) Rice could be identified as early as 13 May, with PA and UA as high as 95%. Corn and soybeans were identified as early as 7 July, with PA and UA as high as 97% and 94%, respectively. (4) With the addition of more temporal features, recognition accuracy increased. The GBDT and RF performed best in identifying the three crops in the early stage. This study demonstrates the feasibility of using crop growth difference information for early crop recognition, which can provide a new idea for early crop recognition. [ABSTRACT FROM AUTHOR]

Because canola is a major oilseed crop, accurately determining its planting areas is crucial for ensuring food security and achieving UN 2030 sustainable development goals. However, when canola is extracted using remote-sensing data, winter wheat causes serious interference because it has a similar growth cycle and spectral reflectance characteristics. This interference seriously limits the classification accuracy of canola, especially in mixed planting areas. Here, a novel canola flower index (CFI) is proposed based on the red, green, blue, and near-infrared bands of Sentinel-2 images to improve the accuracy of canola mapping, based on the finding that spectral reflectance of canola on the red and green bands is higher than that of winter wheat during the canola flowering period. To investigate the potential of the CFI for extracting canola, the IsoData, support vector machine (SVM), and random forest (RF) classification methods were used to extract canola based on Sentinel-2 raw images and CFI images. The results show that the average overall accuracy and kappa coefficient based on CFI images were 94.77% and 0.89, respectively, which were 1.05% and 0.02, respectively, higher than those of the Sentinel-2 raw images. Then we found that a threshold of 0.14 on the CFI image could accurately distinguish canola from non-canola vegetation, which provides a solution for automatic mapping of canola. The overall classification accuracy and kappa coefficient of this threshold method were 96.02% and 0.92, which were very similar to those of the SVM and RF methods. Moreover, the advantage of the threshold classification method is that it reduces the dependence on training samples and has good robustness and high classification efficiency. Overall, this study shows that CFI and Sentinel-2 images provide a solution for automatic and accurate canola extraction. [ABSTRACT FROM AUTHOR]

Early crop identification can provide timely and valuable information for agricultural planting management departments to make reasonable and correct decisions. At present, there is still a lack of systematic summary and analysis on how to obtain real-time samples in the early stage, what the optimal feature sets are, and what level of crop identification accuracy can be achieved at different stages. First, this study generated training samples with the help of historical crop maps in 2019 and remote sensing images in 2020. Then, a feature optimization method was used to obtain the optimal features in different stages. Finally, the differences of the four classifiers in identifying crops and the variation characteristics of crop identification accuracy at different stages were analyzed. These experiments were conducted at three sites in Heilongjiang Province to evaluate the reliability of the results. The results showed that the earliest identification time of corn can be obtained in early July (the seven leaves period) with an identification accuracy up to 86%. In the early stages, its accuracy was 40~79%, which was low, and could not reach the satisfied accuracy requirements. In the middle stages, a satisfactory recognition accuracy could be achieved, and its recognition accuracy was 79~100%. The late stage had a higher recognition accuracy, which was 90~100%. The accuracy of soybeans at each stage was similar to that of corn, and the earliest identification time of soybeans could also be obtained in early July (the blooming period) with an identification accuracy up to 87%. Its accuracy in the early growth stage was 35~71%; in the middle stage, it was 69~100%; and in the late stage, it was 92~100%. Unlike corn and soybeans, the earliest identification time of rice could be obtained at the end of April (the flooding period) with an identification accuracy up to 86%. In the early stage, its accuracy was 58~100%; in the middle stage, its accuracy was 93~100%; and in the late stage, its accuracy was 96~100%. In terms of crop identification accuracy in the whole growth stage, GBDT and RF performed better than other classifiers in our three study areas. This study systematically investigated the potential of early crop recognition in Northeast China, and the results are helpful for relevant applications and decision making of crop recognition in different crop growth stages. [ABSTRACT FROM AUTHOR]

Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices. China's global crop-monitoring system (CropWatch) uses remote sensing data combined with selected field data to determine key crop production indicators: crop acreage, yield and production, crop condition, cropping intensity, crop-planting proportion, total food availability, and the status and severity of droughts. Results are combined to analyze the balance between supply and demand for various food crops and if needed provide early warning about possible food shortages. CropWatch data processing is highly automated and the resulting products provide new kinds of inputs for food security assessments. This paper presents a comprehensive overview of CropWatch as a remote sensing-based system, describing its structure, components, and monitoring approaches. The paper also presents examples of monitoring results and discusses the strengths and limitations of the CropWatch approach, as well as a comparison with other global crop-monitoring systems. [ABSTRACT FROM PUBLISHER]

Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data ( R^\mathrm \mathbf 2 = 0.6942 , RMSE =0.0884), compared with the widely used dimidiate pixel model ( R^\mathrm \mathbf 2 = 0.7034 , RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale. [ABSTRACT FROM PUBLISHER]

In this article, a vegetation classification hypothesis based on plant biochemical composition is presented. The basic idea of this hypothesis is that the vegetation species/crops have their own biochemical composition characteristics, which are separable from each other for those co-existing species at a specific region. Therefore, vegetation species can be classified based on the biochemical composition characteristics, which can be retrieved from hyperspectral remote-sensing data. In order to test this hypothesis, an experiment was conducted in north-western China. Field data on the biochemical compositions and spectral responses of different plants and an Earth-observing 1 (EO-1) Hyperion image were simultaneously collected. After analysing the relationship between biochemical composition and spectral data collected from Hyperion, the vegetation biochemical compositions were estimated using sample biochemical data and bands of Hyperion data. The vegetation classification was completed using the biochemical content classifier (BCC) and maximum-likelihood classifier (MLC) with all Hyperion bands (MLC_A) and selected bands (MLC_S), which were used for estimating considered biochemical contents (cellulose and carotenoid). The overall classification accuracy of the BCC (95.2%) was as good as MLC_S (95.2%) and better than MLC_A (91.1%), as was the kappa value (BCC 92.849%, MLC_S 92.845%, MLC_A 86.637%), suggesting that the BCC was a feasible classification method. The biochemical-based classification method has higher vegetation classification accuracy and execution speed, reduces data dimension and redundancy and needs only a few spectral bands to retrieve biochemical contents instead of using all of the spectral bands. It is an effective method to classify vegetation based on plant biochemical composition characteristics. [ABSTRACT FROM AUTHOR]

Opium is a narcotic obtained from opium poppy and is the raw material of heroin for the illegal drug trade. Monitoring the illegal concentrated cultivation of opium poppy in major regions is critical for the understanding by governments and international communities of the scale of illegal drug trade. This paper investigates whether opium poppy can be discriminated from its coexisting plants using analytical-spectral-device field spectrometer data in the visible to short-wave infrared spectral range. Canopy spectral measurements were conducted during three different growth periods of opium poppy. A synthetic method with three analysis levels was applied to discriminate opium poppy from other species and to select optimal bands for opium poppy discrimination. First, the Mann–Whitney U-test method was used to test the spectral reflectance difference between opium poppy and coexisting crops at each wavelength. Then, the Jeffries–Matusita distance and band correlation analysis were conducted to select the optimal wavebands for discriminating opium poppy using the significant wavebands from the test results. Finally, classification and regression tree analysis was employed to validate the classification accuracy based on the selected optimal wavebands. The results indicated that the spectral reflectance of opium poppy was significantly different from that of coexisting crops in many surveyed wavebands, and opium poppy could be discriminated using a field survey spectrum at canopy level. The best time for discriminating opium poppy from coexisting crops was around flowering time. This paper provided the prerequisite for monitoring opium poppy using satellite remote sensing data in some regions of concern. [ABSTRACT FROM AUTHOR]

Abstract: Background: Myanmar has long been a focus of the international community as a major opium poppy cultivation region. Method: This study used remote sensing technology and ground verification to monitor opium poppy cultivation for three opium poppy growth seasons in North Myanmar. Results: The study found that opium poppy cultivation has remained high. In 2005–6, 2006–7 and 2007–8 growing seasons the total areas monitored were 52,482km2, 178,274km2 and 236,342km2 and the total cultivated area of opium poppy was 8959ha, 18,606ha and 22,300, respectively. This was significantly less than cultivation levels reported during the 1990s. The major cultivation regions were located in Shan State, producing 88% of total poppy cultivation in North Myanmar in 2007–8. The opium poppy was mainly cultivated in the interlocking regions controlled by the local armed forces in Shan State. The field survey noted that most households in this area were poor and poppy cultivation was a main source of income. There were also differences between our figures on poppy cultivation and those reported by United Nations Office on Drugs and Crime. Conclusion: Our study shows that although the opium poppy cultivation in North Myanmar has reduced over recent years, it remains a major producer of opium and to which the international community needs to pay attention, especially in those areas controlled by local armed forces. [Copyright &y& Elsevier]

Abstract: Opium is a narcotic obtained from opium poppy and raw materials of heroin for illegal drug trade. Information on opium production is important for governments and international communities to understand the scale of illegal drug trade and to implement drug fighting policies. Opium production can be estimated from opium yield and poppy cultivation acreage. Poppy cultivation acreage in turn can be estimated using remote sensing technique. This study explores the feasibility of assessing opium yield using remote sensing technique. The objective of this study is to find an effective biophysical indicator that can be related to both opium yield estimation and remote sensing measurements. Data are obtained from three years ground experiments conducted in an official farm in North-Western China. Opium yield and main plant and environmental parameters of opium poppy are measured. A linear regression analysis measures the relationship between opium yield and main plant parameters. A strong correlation between opium yield and leaf area index (LAI) in flowering and harvesting periods of opium poppy is observed (R 2 =0.826 and 0.862 respectively). LAI is a good indicator during flowering and harvesting periods and can be used to build a model for opium yield estimation. Furthermore, LAI can be retrieved using remote sensing at a region scale. The study shows that remote sensing could be used to estimate opium yield in opium poppy concentrated regions, and that a more comprehensive and precise opium production information could be obtained for further analysis of illegal drug circulating trend and policy. [Copyright &y& Elsevier]