Spectral Matching Techniques Research Articles

Effect of Boko Haram Insurgency on Irrigation Activities Using Field and Earth Observation Data in Part of Northeastern Nigeria

The Boko HaramInsurgency in Nigeria has affected the socio-economic activities in most parts of the northeastern part of the country, leading to a low supply of food from agricultural output and increased poverty. Therefore, the main objective of this paper is to analyse the effect of the Boko Haram insurgency on irrigation activities in Kukuwa village. Data was sourced for interviews of 240 respondents and Landsat8_OLI for 2014 and Sentinel 2 for 2015 to 2019. The images were converted to NDVI and used to determine the spectral signatures for each crop using Spectral Matching Techniques (SMTs). The result indicated the majority of the farmers irrigated their farms as > 2 ha (69%) before the crisis, 2 ha (87%) during the crises and > 2 ha (62%) after the crisis, and the majority hired farmers for labour. The intensity of the irrigation was reduced during the crises and it increased after the crises. The result of the SMTs in 2014 indicated a high spectral signature for irrigation activities, with 227.07ha (29.6%) for tomatoes. In 2015 and 2016, low spectral signatures for crops were observed for 541.69ha (69.80%) and 519.5ha (66.94%), respectively. While the spectral result in 2017, 2018, and 2019 indicates a good appreciation of irrigation activities, which shows an increase in most of the dominant crops cultivated in the area, such as tomato, onion, watermelon, and pepper. Among the breakthroughs of this paper is the analysing the reflected signatures from the classified image for different crops through irrigation activities during and after the insurgency in the area. The accuracy assessment indicated fairly good with Landsat but very good with the sentinel images. The study suggests further research into the effect of boko haram insurgency on irrigation activities using field and earth observation data in part of northeastern Nigeria.

Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

Identifying prospects and potential areas for introducing pearl millet stress-tolerant cultivars in Rajasthan, India: A geospatial analysis

Dryland crops are highly prone to various stresses such as water stress (drought) and heat stress. The identification of stress-prone regions is crucial for effective and efficient implementation of appropriate solutions, such as stress-tolerant crop varieties. This study was conducted in Rajasthan state located in North-western India. Rajasthan is predominantly a rainfed pearl millet ecosystem (>50 % during monsoon season) in India. The pearl millet productivity in Western Rajasthan is the lowest in India with a significant decline in its cultivated area. The present study tried to analyse the pearl millet cropping systems in various ecologies and identified the stress-prone areas by analysing the stress pattern from 2011 to 2020 which helps in targeting the stress tolerant cultivars. The spatial distribution of pearl millet areas was mapped using Sentinel-2 time-series data and spectral matching techniques. The mapped pearl millet areas were well correlated with district-level statistics obtained from secondary sources. Application of geospatial techniques for monitoring changes in pearl millet cropped area proves to be a cost-effective, and reliable approach. It also helps in assessing the cultivated area changes as well as the quantification of yield losses caused by abiotic stresses such as drought and heat. Agricultural research institutes, progressive farmers and line departments from the government can use these findings for better targeting and introduction of climate SMART pearl millet technologies in the state. Introduction of resilient technologies minimize the production risks faced by small and marginal farmer thereby reduces the crop income negative deviations. Scaling-up of such technologies not only protects farmer's livelihoods but also enhances the food and nutritional security in the state.

Assessment of Cropland Changes Due to New Canals in Vientiane Prefecture of Laos using Earth Observation Data

The lower catchment area of a Mak Hiao river system is vulnerable to flash floods and water stress. So it is important to construct irrigation structures in this area to minimize floods during the rainy season and store water for the winter season. The Asian Development Bank (ADB) has been supporting the Government of Laos in constructing such small reservoirs like Donkhuay schemes 1 & 2, Mak Hiao, Nalong 3 and Sang Houabor projects in lower catchment areas. Our study evaluated the impacts of small irrigation schemes in terms of land-use/land-cover (LULC), crop intensity, and productivity changes, using high resolution satellite imagery, socioeconomic, and ground data. We analyzed the temporal cropping pattern in the Vientiane prefecture of Laos using Planet and Sentinel-2 data. On the other hand, crop intensity and cropland changes were mapped using Sentinel-2 data and spectral matching techniques (SMTs). The crop classification accuracy based on field-plot data was 88.6%. Our results show that irrigation projects in the lower catchment areas brought about significant on-site changes in terms of cropland expansion and increased crop intensity. Remarkable changes in LULC were observed especially in the command areas owing to an increase of about 300% in crop area with access to irrigation and increase of water bodies by 31%. Our study found that interventions at the level of the command area do improved on-site soil, water and environmental services. They study emphasized underline the role of land-use regulations in reducing pressure on natural land-use systems and thereby serving the major goal of up-scaling sustainable natural resource management. The study documented the vital role of small/medium irrigation projects in restoring ecosystem services such as cropping patterns and LULC conversion.

Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security

ABSTRACT Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.

Assessment of the SASI Spectral Shape Index Time Series for Mapping Rice Ecosystems in the Mediterranean Region

There is a growing need to map rice ecosystems and to develop methods for monitoring rice distribution in order to account for rapid land use changes worldwide. In this study, we evaluated a methodology based on Vegetation Indices time series derived from an 8-day MODIS composite to identify rice fields and develop rice maps that can be timely updated in the long term. We have assessed the potential of the Spectral Shape Index time series and compared its performance with the Normalized Difference Vegetation Index in two coastal locations and in an inland location in the Mediterranean Region for 2012. A profile similarity comparison method, the Spectral Angle Mapper, was accomplished between the reference rice annual profile and the annual profiles of both indices in a pixel basis in order to determine rice pixels. The resultant maps were validated with rice masks, where available, or ortophotos and crop surface statistics where not. The results obtained demonstrated the potential of both indices to provide accurate rice maps when applied together with spectral matching techniques. The overall accuracy was 92.8%, 98.1% and 90.1% for the Spectral Shape Index and 92.4%, 77.24% and 82.8% for the Normalized Difference Vegetation Index in each location. The excellent performance of the Spectral Shape Index in the three locations highlighted the importance of exploring angular indices to improve the identification of land cover dynamics.

Paradigm of Geological Mapping of the Adıyaman Fault Zone of Eastern Turkey Using Landsat 8 Remotely Sensed Data Coupled with PCA, ICA, and MNFA Techniques

A principal and independent component analysis (PCA and ICA) and a minimum noise fraction analysis (MNFA) were applied in this study to Landsat 8 Operational Land Imager (OLI) images along the Adıyaman fault zone in Eastern Turkey. These analyses indicated that the lithologic units, fault patterns, and the morphological and structural features can be mapped highly accurately by using spectral-matching techniques in regions where rocks are well exposed. An inspection of all possible band combinations indicated that the PCA 134 and 231 and the ICA 132 band combinations give the best false color composite images for identifying the lithological units and contacts. The findings of the MNFA band combinations show that the MNFA 521 band combination also is robust for discriminating the lithological units, particularly Quaternary clastic units (colluvium/alluvium). MNFA band 1 alone provides the best image for tracing the tectonic and structural elements in the study area. The new up-to-date lithologic map of the Adıyaman fault zone we produced upon the interpretation of the processed OLI images reveals several river channels that are offset and beheaded by the Adıyaman fault, which verifies its Quaternary activity. This study demonstrated that, when used with the OLI data, the PCA, ICA, and MNFA are very powerful for lithological and structural mapping in actively deforming tectonic zones and hence can be applied to other regions elsewhere in the world where the climate is arid to semiarid, and the vegetation cover is scarce.

Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India

Crop yield estimation is important to inform logistics management such as the prescription of nutrient inputs, financing, storage and transport, marketing as well as to inform for crop insurance appraisals due to loss incurred by abiotic and biotic stresses. In this study, we used a suite of methods to assess yields at the village level (< 5 km2) using remote sensing technology and crop modeling in Indian states of Telangana, Andhra Pradesh and Odisha. Remote sensing products were generated using Sentinel-2 and Landsat 8 time series data and calibrated with data collected from farmers’ fields. We derived maps showing spatial variation in crop extent, crop growth stages and leaf area index (LAI), which are crucial in yield assessment. Crop classification was performed on Sentinel-2 time series data using spectral matching techniques (SMTs) and crop management information collected from field surveys along with ground data. The locations of crop cutting experiments (CCEs) was identified based on crop extent maps. LAI was derived based on the SAVI (soil-adjusted vegetation index) equation were using Landsat 8-time series data. We used the technique of re-parametrization of crop simulation models based on the several iterations using remote sensing leaf area index (LAI). The data assimilation approach helps in fine-tuning the initial parameters of the crop growth model and improving simulation with the help of remotely sensed observations. Results clearly show a good correlation between observed and simulated crop yields (R2 is greater than 0.7) for all the crops studied. Our study showed that by assimilation of remotely sensed data in to crop models, crop yields at harvest could be successfully predicted.

Mapping drought-induced changes in rice area in India

ABSTRACTRice is a staple food crop of India and is grown on 44 Mha (2011–12), 58.6% of which are irrigated. An inevitable phenomenon which looms over all aspects of human life and affects rice production in India is drought. Assessing drought damage using geospatial datasets available in the public domain, such as the Normalized Difference Vegetation Index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), can provide specific and local ecoregion information for developing drought-resistant rice varieties. Based on multi-temporal NDVI data and field observations in 2009, we developed a methodology to identify and map drought-affected areas in India. A long-term (10-year) average of NDVI during the rainy (kharif) season (June–October) was compared with NDVI from a known drought year (2002–03) to identify changes in rice area. Rainfall data from the Tropical Rainfall Monitoring Mission (TRMM) was used to support the drought analysis. Spectral matching techniques were used to categorise the drought-affected rice areas into three classes – severe, moderate, and mild based on the intensity of damage assessed through field sampling. Based on these ground survey samples, spectral signatures were generated. It was found that the rice area was about 16% less in the drought year (2002–03) than in a normal year (2000–01). A comparison of the MODIS-derived rice area affected by drought in 2002 for each state and district against the difference in the kharif season harvested rice area between 2000 and 2002 (from official statistics) revealed a substantial difference in harvested area in 2002 that was largely attributable to drought. An 84.7% correlation was found between the MODIS-derived drought-affected area in 2002 and the reduction in harvested area from 2000–01 to 2002–03. Good spatial correlation was found between the drought-affected rice areas and reduction of rice harvested areas in different rice ecologies, indicating the usefulness of such geospatial datasets in assessing abiotic stress such as drought and its consequences.

Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system

Cropland fallows are the next best-bet for intensification and extensification, leading to increased food production and adding to the nutritional basket. The agronomical suitability of these lands can decide the extent of usage of these lands. Myanmar’s agricultural land (over 13.8 Mha) has the potential to expand by another 50% into additional fallow areas. These areas may be used to grow short-duration pulses, which are economically important and nutritionally rich, and constitute the diets of millions of people as well as provide an important source of livestock feed throughout Asia. Intensifying rice fallows will not only improve the productivity of the land but also increase the income of the smallholder farmers. The enhanced cultivation of pulses will help improve nutritional security in Myanmar and also help conserve natural resources and reduce environmental degradation. The objectives of this study was to use remote sensing methods to identify croplands in Myanmar and cropland fallow areas in two important agro-ecological regions, delta and coastal region and the dry zone. The study used moderate-resolution imaging spectroradiometer (MODIS) 250-m, 16-day normalized difference vegetation index (NDVI) maximum value composite (MVC), and land surface water index (LSWI) for one 1 year (1 June 2012–31 May 2013) along with seasonal field-plot level information and spectral matching techniques to derive croplands versus cropland fallows for each of the three seasons: the monsoon period between June and October; winter period between November and February; and summer period between March and May. The study showed that Myanmar had total net cropland area (TNCA) of 13.8 Mha. Cropland fallows during the monsoon season account for a meagre 2.4% of TNCA. However, in the winter season, 56.5% of TNCA (or 7.8 Mha) were classified as cropland fallows and during the summer season, 82.7% of TNCA (11.4 Mha) were cropland fallows. The producer’s accuracy of the cropland fallow class varied between 92 and 98% (errors of omission of 2 to 8%) and user’s accuracy varied between 82 and 92% (errors of commission of 8 to 18%) for winter and summer, respectively. Overall, the study estimated 19.2 Mha cropland fallows from the two major seasons (winter and summer). Out of this, 10.08 Mha has sufficient moisture (either from rainfall or stored soil water content) to grow short-season pulse crops. This potential with an estimated income of US$ 300 per hectare, if exploited sustainably, is estimated to bring an additional net income of about US$ 1.5 billion to Myanmar per year if at least half (5.04 Mha) of the total cropland fallows (10.08 Mha) is covered with short season pulses.

Urban Sprawl and Adverse Impacts on Agricultural Land: A Case Study on Hyderabad, India

Many Indian capitals are rapidly becoming megacities due to industrialization and rural–urban emigration. Land use within city boundaries has changed dynamically, accommodating development while replacing traditional land-use patterns. Using Landsat-8 and IRS-P6 data, this study investigated land-use changes in urban and peri-urban Hyderabad and their influence on land-use and land-cover. Advanced methods, such as spectral matching techniques with ground information were deployed in the analysis. From 2005 to 2016, the wastewater-irrigated area adjacent to the Musi river increased from 15,553 to 20,573 hectares, with concurrent expansion of the city boundaries from 38,863 to 80,111 hectares. Opportunistic shifts in land-use, especially related to wastewater-irrigated agriculture, emerged in response to growing demand for fresh vegetables and urban livestock feed, and to easy access to markets due to the city’s expansion. Validation performed on the land-use maps developed revealed 80–85% accuracy.

Monitoring Changes in Croplands Due to Water Stress in the Krishna River Basin Using Temporal Satellite Imagery

Remote sensing-based assessments of large river basins such as the Krishna, which supplies water to many states in India, are useful for operationally monitoring agriculture, especially basins that are affected by abiotic stress. Moderate-Resolution Imaging Spectroradiometer (MODIS) time series products can be used to understand cropland changes at the basin level due to abiotic stresses, especially water scarcity. Spectral matching techniques were used to identify land use/land cover (LULC) areas for two crop years: 2013–2014, which was a normal year, and 2015–2016, which was a water stress year. Water stress-affected crop areas were categorized into three classes—severe, moderate and mild—based on the normalized difference vegetation index (NDVI) and intensity of damage assessed through field sampling. Furthermore, ground survey data were used to assess the accuracy of MODIS-derived classification individual products. Water inflows into and outflows from the Krishna river basin during the study period were used as direct indicators of water scarcity/availability in the Krishna Basin. Furthermore, ground survey data were used to assess the accuracy of MODIS-derived LULC classification of individual year products. Rainfall data from the tropical rainfall monitoring mission (TRMM) was used to support the water stress analysis. The nine LULC classes derived using the MODIS temporal imagery provided overall accuracies of 82% for the cropping year 2013–2014 and 85% for the year 2015–2016. Kappa values are 0.78 for 2013–2014 and 0.82 for 2015–2016. MODIS-derived cropland areas were compared with national statistics for the cropping year 2013–2014 with a R2 value of 0.87. Results show that both rainfed and irrigated areas in 2015–2016 saw significant changes that will have significant impacts on food security. It has been also observed that the farmers in the basin tend to use lower inputs and labour per ha during drought years. Among all, access to water is the major driver determining the crop choice and extent of input-use in the basin.

Capacity, damage and fragility models for steel buildings: a probabilistic approach

Recently proposed capacity-based damage indices and parametric models for capacity curves are applied to frame steel buildings located in soft soils of the Mexico City. To do that, the seismic performance of 2D models of low-, mid- and high-rise buildings is assessed. Deterministic and probabilistic nonlinear static and incremental dynamic analyses are implemented. Monte Carlo simulations and the Latin Hypercube sampling technique are used. Seismic actions are selected among accelerograms recorded in the study area. Spectral matching techniques are applied, so that the acceleration time histories have a predefined mean response spectrum and controlled error. The design spectrum of the Mexican seismic code for the zone is used as target spectrum. The well-known Park and Ang damage index allows calibrating the capacity-based damage index. Both damage indices take into account the contribution to damage of the stiffness degradation and of the energy dissipation. Damage states and fragility curves are also obtained and discussed in detail. The results reveal the versatility, robustness and reliability of the parametric model for capacity curves, which allow modelling the nonlinear part of the capacity curves by the cumulative integral of a cumulative lognormal function. However, these new capacity-based damage index and capacity models have been tested for and applied to 2D frame buildings only; they have not been applied to 3D building models yet. The Park and Ang and the capacity-based damage indices show that for the analysed buildings, the contribution to damage of the stiffness degradation is in the range 66–77% and that of energy loss is in the range 29–34%. The lowest contribution of energy dissipation (29%) is found for the low-rise, more rigid, building. The energy contribution would raise with the ductility of the building and with the duration of the strong ground motion. High-rise frame buildings in soft soils of Mexico City show the worst performance so that the use of adequate braced frames to control the displacements could be recommended.

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

ABSTRACTMapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.html

Identification of Multi-Style Hydrothermal Alteration Using Integrated Compositional and Topographic Remote Sensing Datasets

The western part of the island of Milos, Greece has undergone widespread, intense alteration associated with a range of mineralization, including seafloor Mn-Fe-Ba, sub seafloor Pb-Zn-Ag, and epithermal Au-Ag. The surrounding country rocks are a mixture of submarine and subaerial calc-alkaline volcanic rocks ranging from basaltic andesite to rhyolite in composition, but are predominantly andesites and dacites. The current surface spatial distribution of the alteration mineralogy is a function not only of the original hydrothermal, but also subsequent tectonic and erosional processes. The high relief and the excellent rock exposure provide ideal conditions to evaluate the potential of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite remote sensing data to identify and differentiate the different styles of alteration mineralisation. Laboratory spectral reflectance and calculated emittance measurements of field samples, supported by XRD analysis and field mapping, were used to support the analysis. Band ratio and spectral matching techniques were applied to the shortwave-infrared (SWIR) reflectance and thermal-infrared (TIR) emissivity imagery separately and were then integrated with topographic data. The band ratio and spectral matching approaches produced similar results in both the SWIR and TIR imagery. In the SWIR imagery, the advanced argillic, argillic and hydrous silica alteration zones were clearly identifiable, while in the TIR imagery, the silicic and advanced argillic alteration zones, along with the country rock, were differentiable. The integrated mineralogical–topographic datasets provided an enhanced understanding of the spatial and altitude distribution of the alteration zones when combined with conceptual models of their genesis, which provides a methodology for the differentiation of the multiple styles of alteration.

Crop Area Mapping Using 100-m Proba-V Time Series

A method was developed for crop area mapping inspired by spectral matching techniques (SMTs) and based on phenological characteristics of different crop types applied using 100-m Proba-V NDVI data for the season 2014–2015. Ten-daily maximum value NDVI composites were created and smoothed in SPIRITS (spirits.jrc.ec.europa.eu). The study sites were globally spread agricultural areas located in Flanders (Belgium), Sria (Russia), Kyiv (Ukraine) and Sao Paulo (Brazil). For each pure pixel within the field, the NDVI profile of the crop type for its growing season was matched with the reference NDVI profile based on the training set extracted from the study site where the crop type originated. Three temporal windows were tested within the growing season: green-up to senescence, green-up to dormancy and minimum NDVI at the beginning of the growing season to minimum NDVI at the end of the growing season. Post classification rules were applied to the results to aggregate the crop type at the plot level. The overall accuracy (%) ranged between 65 and 86, and the kappa coefficient changed from 0.43–0.84 according to the site and the temporal window. In order of importance, the crop phenological development period, parcel size, shorter time window, number of ground-truth parcels and crop calendar similarity were the main reasons behind the differences between the results. The methodology described in this study demonstrated that 100-m Proba-V has the potential to be used in crop area mapping across different regions in the world.

Cortical folding of the preterm brain: a longitudinal analysis of extremely preterm born neonates using spectral matching.

IntroductionInfants born extremely preterm (<28 weeks of gestation) are at risk of significant neurodevelopmental sequelae. In these infants birth coincides with a period of rapid brain growth and development, when the brain is also vulnerable to a range of insults. Mapping these changes is crucial for identifying potential biomarkers to predict early impairment.MethodsIn this study we use surface‐based spectral matching techniques to find an intrasubject longitudinal surface correspondence between the white–grey matter boundary at 30 and 40 weeks equivalent gestational age in nine extremely preterm born infants.ResultsUsing the resulting surface correspondence, we identified regions that undergo more cortical folding of the white–grey matter boundary during the preterm period by looking at changes in well‐known curvature measures. We performed Hotelling T2 statistics to evaluate the significance of our findings.DiscussionThe prefrontal and temporal lobes exhibit most development during the preterm period, especially in the left hemisphere. Such correspondences are a promising result as longitudinal measurements of change in cortical folding could provide insightful information about the mechanical properties of the underlying tissue and may be useful in inferring changes during growth and development in this vulnerable period.

Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data

ABSTRACTThe goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia, using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period. Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season (June–October), followed by a fallow during the rabi season (November–February). These cropland areas are not suitable for growing rabi-season rice due to their high water needs, but are suitable for a short -season (≤3 months), low water-consuming grain legumes such as chickpea (Cicer arietinum L.), black gram, green gram, and lentils. Intensification (double-cropping) in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands. Several grain legumes, primarily chickpea, are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region. The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers: (a) rice crop is grown during the primary (kharif) crop growing season or during the north-west monsoon season (June–October); (b) same croplands are left fallow during the second (rabi) season or during the south-east monsoon season (November–February); and (c) ability to support low water-consuming, short-growing season (≤3 months) grain legumes (chickpea, black gram, green gram, and lentils) during rabi season. Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season, because the moisture/water demand of these crops is too high. The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index (NDVI) time series for one year (June 2010–May 2011) of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using spectral matching techniques (SMTs), and extensive field knowledge. Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics. The producers’ and users’ accuracies of the cropland fallow classes were between 75% and 82%. The overall accuracy and the kappa coefficient estimated for rice classes were 82% and 0.79, respectively. The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia, with 88.3% in India, 0.5% in Pakistan, 1.1% in Sri Lanka, 8.7% in Bangladesh, 1.4% in Nepal, and 0.02% in Bhutan. Decision-makers can target these areas for sustainable intensification of short-duration grain legumes.

Land Use and Agricultural Change Dynamics in SAT Watersheds of Southern India

Impact of dynamic land use and land cover changes on the livelihood of local communities and ecosystem services is a major concern. This is particularly evident in most dryland agricultural systems in South Asia. We study land use/land cover (LULC) changes over the last two decades in a watershed (9589 ha) located in semi-arid eco-region in South India (Anantapuram district) using Landsat and IRS imagery. We captured additional data through field observations and focused group discussions. The high resolution 30 m data and the spectral matching techniques (SMTs) provided accuracy of 91–100% for various land use classes and 80–95% for the rice and groundnut areas. The watershed studied has undergone significant land use changes between 1988 and 2012. Diminishing size and number of surface water bodies, and contrastingly increased areas under irrigation clearly explain that the system has evolved significantly towards groundwater-irrigated groundnut production. Such changes could be beneficial in the short run, but if the groundwater withdrawal is without sufficient recharge, the long-term consequences on livelihoods could be negative. The water scarcity could be aggravated under the climate change. The construction of checkdams and dugout ponds to recharge groundwater is a potential solution to enhance recharge.

Interpretation of land cover using spectral modulation pattern- an example with Landsat 8 OLIimage

Spectral signature, in general, can be defined as characteristics of surface objects of transmission, absorption and reflection of electromagnetic radiation. Spectral signature is expected stable and unique for given surface material. Spectral signature can be graphically represented in two-dimensional space in the form of spectral curves. Spectral signature has been used long time for object detection and classification mostly by spectral matching methods. Spectral matching is time-consuming and requires reference spectra that is not always available in hand. Spectral modulation pattern is a simplified form of spectral curve shapes. We define this pattern by pairwise comparison of reflectance between two spectral bands. The aim of this research is to point out the use of spectral modulation patterns for land cover mapping. The experiment has been carried out with Landsat 8 OLI image data, which has six reflective bands of 30 m spatial resolution. The Landsat 8 OLI is an excellent data source for land cover mapping in both local and global scale. Due to very huge data volume, automated analysis is crucial when we need accomplish land cover maps in a short time. In this paper, the authors introduce the concept to use spectral modulation patterns of spectral signatures for automated interpretation of land cover. The first step in this concept is to understand correctly meaning of each modulation pattern of spectral signatures and their relation to land cover classes. In this study, we use three Landsat 8 OLI images of Vietnam in 2013 and 2014. Ground GPS field photos were collected to support interpretation of land cover.References Canty, M.J., Nielsen, A.A., Schmidt, M., 2004: Automatic radiometric normalization of multitemporal satellite imagery. Remote Sensing of Environment 91.3, 441-451. Duong, N. D.,1997: Graphical analysis of spectral reflectance curve.In Proceedings of the 18th Asian Conference on Remote Sensing, Oct 1997, pp. 20-24. Laborte, Alice G., Maunahan, A.A., Hijmans, R.J., 2010: Spectral signature generalization and expansion can improve the accuracy of satellite image classification. PloS one 5.5, e10516. Olthof, I., Butson, C., Fraser, R., 2005: Signature extension through space for northern landcover classification: A comparison of radiometric correction methods. Remote Sensing of Environment 95.3, 290-302. Ruby, J.G., Fischer, R.L., 2002: Spectral signatures database for remote sensing applications. International Symposium onOptical Science and TechnologyInternationalSociety for Optics and Photonics, 2002. Shaw, G.A., Burke, H-h.K, 2003: Spectral imaging for remote sensing". Lincoln Laboratory Journal 14.1, 3-28. Thenkabail, P., et al., 2007: Spectral matching techniques to determine historical land-use/land-cover (LULC) and irrigated areas using time-series 0.1-degree AVHRR Pathfinder datasets. Photogrammetric Engineering &amp; Remote Sensing73.10, 1029-1040.