Assessment of citrus crop condition in Umling block of Ri-Bhoi district using RS and GIS technique

Orange orchards of Meghalaya are small in size and are found together with other plants on the slopes of hills. It is also reported that the productivity of orchards is declining in some parts of the state. Therefore in this study, we have attempted to map areas prone to citrus productivity decline based on the integrated effect of soil erosion, vegetation condition and moisture stress. It is difficult to identify orchards on hilly terrain using standard FCC of IRS-P6 LISS III data. Hence, an enhanced color composite image was prepared from three images generated from indices namely SBI, NDWI and NDVI. This enhanced color image was classified using the maximum likelihood classification method and enabled identifying villages prone to citrus decline. The study shows that orchards of 29 villages which are suffering from moisture stress and mostly located on steep slopes that cause heavy soil loss leading to nutrient imbalances are prone to citrus decline. These data will be useful in mapping potential citrus decline areas over zones having similar climatic conditions so that the concerned state horticulture/agriculture departments and citrus growers can take necessary remedial actions.     

Identification of hazelnut fields using spectral and Gabor textural features

Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection. Permanent crops (including nut orchards) are extracted with very high resolution remote sensing imagery using visual interpretation or automated systems based on mainly textural features which reflect the regular plantation pattern of their orchards, since the spectral values of the nut orchards are usually close to the spectral values of other woody vegetation due to various reasons such as spectral mixing, slope, and shade. However, when the nut orchards are planted irregularly and densely at fields with high slope, textural delineation of these orchards from other woody vegetation becomes less relevant, posing a challenge for accurate automatic detection of these orchards. This study aims to overcome this challenge using a classification system based on multi-scale textural features together with spectral values. For this purpose, Black Sea region of Turkey, the region with the biggest hazelnut production in the world and the region which suffers most from this issue, is selected and two Quickbird archive images (June 2005 and September 2008) of the region are acquired. To differentiate hazel orchards from other woodlands, in addition to the pansharpened multispectral (4-band) bands of 2005 and 2008 imagery, multi-scale Gabor features are calculated from the panchromatic band of 2008 imagery at four scales and six orientations. One supervised classification method (maximum likelihood classifier, MLC) and one unsupervised method (self-organizing map, SOM) are used for classification based on spectral values, Gabor features and their combination. Both MLC and SOM achieve the highest performance (overall classification accuracies of 95% and 92%, and Kappa values of 0.93 and 0.88, respectively) when multi temporal spectral values and Gabor features are merged. High F_β values (a combined measure of producer and user accuracy) for detection of hazel orchards (0.97 for MLC and 0.94 for SOM) indicate the high quality of the classification results. When the classification is based on multi spectral values of 2008 imagery and Gabor features, similar F_β values (0.95 for MLC and 0.93 for SOM) are obtained, favoring the use of one imagery for cost/benefit efficiency. One main outcome is that despite its unsupervised nature, SOM achieves a classification performance very close to the performance of MLC, for detection of hazel orchards.     

Integrating cellular automata,artificial neural network,and fuzzy set theory to simulate threatened orchards: application to Maragheh,Iran

Urbanization processes challenge the growth of orchards in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) and artificial neural network with fuzzy set theory (CA–ANN–Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes. Second, the CA–ANN–Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA–ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming years up to 2025. Results show that CA–ANN–Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA–ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades, while the CA–ANN–Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers.     

Remote sensing of spider mite damage in California peach orchards

Remote sensing techniques can decrease pest monitoring costs in orchards. To evaluate the feasibility of detecting spider mite damage in orchards, we measured visible and near infrared reflectance of 1153 leaves and 392 canopies in 11 peach orchards in California. Pairs of significant wavelengths, identified by Partial Least Squares regression, were combined into normalized difference indices. These and 9 previously published indices were evaluated for correlation with mite damage.     

Surface Reflectance Climate Data Records (CDRs) is a Reliable Landsat ETM+ Source to Study Chlorophyll Content in Pecan Orchards

We evaluated the relationships among three Landsat Enhanced Thematic Mapper (ETM+) datasets, top-of-atmosphere (TOA) reflectance, surface reflectance climate data records (surface reflectance-_CDR) and atmospherically corrected images using Fast Line-of-Sight atmospheric analysis of Spectral Hypercubes model (surface reflectance-_FLAASH) and their linkto pecan foliar chlorophyll content(chl-cont). Foliar chlorophyll content as determined with a SPAD meter, and remotely-sensed data were collected from two mature pecan orchards (one grown in a sandy loam and the other in clay loam soil) during the experimental period. Enhanced vegetation index derived from remotely sensed data was correlated to chl-cont. At both orchards, TOA reflectance was significantly lower than surface reflectance within the 550–2400 nm wavelength range. Reflectance from atmospherically corrected images (surface reflectance-_CDR and surface reflectance-_FLAASH) was similar in the shortwave infrared (SWIR: 1550–1750 and 2080–2350 nm) and statistically different in the visible (350–700 nm). Enhanced vegetation index derived from surface reflectance-_CDR and surface reflectance-_FLAASH had higher correlation with chl-cont than TOA. Accordingly, surface reflectance is an essential prerequisite for using Landsat ETM+  data and TOA reflectance could lead to miss-/or underestimate chl-cont in pecan orchards. Interestingly, the correlation comparisons (Williams t test) between surface reflectance-_CDR and chl-cont was statistically similar to the correlation between chl-cont and commercial atmospheric correction model. Overall, surface reflectance-_CDR, which is freely available from the earth explorer portal, is a reliable atmospherically corrected Landsat ETM+ image source to study foliar chlorophyll content in pecan orchards.     

A tree counting algorithm for precision agriculture tasks

Abstract

This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data. Depending on site-specific pruning practices, the morphologic characteristics of tree crowns may generate one or more brightness peaks (tree top) on the imagery. To optimize tree counting and to minimize typical background noises from orchards (i.e. bare soil, weeds, and man-made objects), a four-step algorithm was implemented with spatial transforms and functions suitable for spaced stands (asymmetrical smoothing filter, local minimum filter, mask layer, and spatial aggregation operator). System performance was evaluated through objective criteria, showing consistent results in fast capturing tree position for precision agriculture tasks.     

胶东半岛果园TM影像信息的提取决策树方法

本文选取胶东半岛最具代表性的5个果品县(市)为研究区,以Landsat TM影像数据为分类影像,尝试提取果园信息。选用可以"无缝"融入多种辅助信息的决策树分类方法,综合NDVI、地形地貌和缨帽变换等多种辅助信息,利用年内物候变化最大的果园与背景地物的光谱差异,进行果园信息提取;利用SPOT影像以及野外考察资料作为检验样本进行精度验证。表明综合多种辅助信息,利用决策树分类法提取TM影像果园信息可行且准确性较高。     

Significance of the remotely sensed thermal infrared measurements obtained over a citrus orchard

In this work we have developed a theoretical model that helps the interpretation of the remotely sensed thermal infrared measurements carried out over citrus orchards. A detailed analysis of the different factors which take part in the definition of the effective emissivity and temperature (observation height, viewing angle, type of soil, dimensions and separation between orange trees) is made. The model was validated under vertical observation in a citrus orchard during seven nights. In this situation we have determined that the model performs to an accuracy of about 1%.     

Assessment of floodplain landuse and channel morphology within meandering reach of the Talar River in Iran using GIS and aerial photographs

Abstract

The main objective of this study is to investigate and monitor the landuse and morphological changes in the floodplain part of the Talar River, northern parts of Iran. In the present study, the aerial photographs have been used to produce landuse maps of the floodplain for three periods including 1968, 1994, and 2013. The quantitative analysis of the produced landuse maps showed that the floodplain has undergone substantial landuse changes. Moreover, the sediment bar and the beach area have been decreased about 97 and 90%, respectively, during the 45-year period. Substantial increases of 192 and 622% have been observed for orchards and residential areas, respectively. On the other hand, not only the forest and riparian vegetation were decreased but also the average width of river was decreased about 25.5 m. In addition, flow length of the study reach increased about 8 m. The RNCI was about ?0.7 m per year indicating sedimentation process. During the period of 1968–2013, Caspian Sea has retreated about 150 m and the delta of Talar River was changed. This study showed that morphological actions during first 26 years (1968–1994) were the stable and last 19 years had the change period, especially sedimentation (bar).     

Remote sensing of horticultural plantations in Kumarsain tehsil in Shimla district, Himachal Pradesh

The favourable agroclimatic conditions for orchards especially apples have increased the acreages in Himachal Pradesh (HP) which has significantly contributed in the growth of state economy. Realizing the importance of horticulture in HP and its changing scenario of the land use/land cover, a study was conducted to identify and map apple and almond plantations in the Kumarsain tehsil of Shimla district using Remote Sensing (RS) techniques. IRS-IB LISS-II False Colour Composite (FCC) diapositives of October 27, March 30 and April 20, 1992 were visually analysed for mapping apple and almond plantations. The results indicate that IRS LISS-II data of April 20 on 1∶50,000 scale was found very useful for identification and mapping of apple and almond plantations in this region. Accuracy of interpretation was also tested on sample basis assuming a binomial distribution for the probability of success/failure of sample points. The overall interpretation accuracy assessed based on 40 sample points was found to be 87 per cent at 90 per cent confidence limits.     

三峡水库建设前后10年库区景观空间格局动态变化研究——以重庆开县为例

在遥感和地理信息系统技术的支持下,以1992年和2002年TM图像为基础,采用斑块密度、斑块平均面积、景观形状指数、景观分离度指数、景观多样性指数、景观优势度指数、景观相对聚集度指数深入而全面地研究了三峡工程建设前后十年重庆开县景观空间格局的动态变化,本研究的开展旨在为库区生态环境保护提供科学依据。研究结果表明,十年间研究区景观空间格局发生了明显变化,草地、未利用地、旱地、果园和水田面积净减,森林、建设用地、灌木林和水域面积增加,退耕还林已初见成效。     

The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data

Abstract

This paper investigates the contribution of multi-temporal enhanced vegetation index (EVI) data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer (MODIS) imagery. In object-oriented classification, similar pixels are firstly grouped together and then classified; the produced result does not suffer the speckled appearance and closer to human vision. EVI data are from the MODIS sensor aboard Terra spacecraft. 69 EVI data (scenes) were collected during the period of three years (2001–2003) in a mountainous vegetated area. These data sets were used to study the phenology of the land cover types. Different land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover classification. Two experiments were carried out: one was only with single date MODIS multispectral data, and the other one including also the 69 EVI images. Eight classes were distinguished: temperate forest, tropical dry forest, grassland, irrigated agriculture, rain-fed agriculture, orchards, lava flows and human settlement. The two classifications were evaluated with independent verification data, and the results showed that with multi-temporal EVI data, the classification accuracy was improved 5.2%. Evaluated by McNemar's test, this improved was significant, with significance level p=0.01.     

Impact of the spatial resolution on the energy balance components on an open-canopy olive orchard

The recent technical improvements in the sensors used to acquire images from land surfaces has made possible to assess the performance of the energy balance models using unprecedented spatial resolutions. Thus, the objective of this work is to evaluate the response of the different energy balance components obtained from METRIC model as a function of the input pixel size. Very high spatial resolution airborne images (≈50 cm) on three dates over olive orchards were used to aggregate different spatial resolutions, ranging from 5 m to 1 km. This study represents the first time that METRIC model has been run with such high spatial resolution imagery in heterogeneous agricultural systems, evaluating the effects caused by its aggregation into coarser pixel sizes. Net radiation and soil heat flux showed a near insensitive behavior to spatial resolution changes, reflecting that the emissivity and albedo respond linearly to pixel aggregation. However, greater discrepancies were obtained for sensible (up to 17%) and latent (up to 23%) heat fluxes at spatial resolutions coarser than 30 × 30 m due to the aggregation of non-linear components, and to the inclusion of non-agricultural areas in such aggregation. Results obtained confirm the good performance of METRIC model when used with high spatial resolution imagery, whereas they warn of some major errors in crop evapotranspiration estimation when medium or large scales are used.     

Improving crop type determination using satellite imagery: A study for the Regione del Veneto,Italy

The Regione del Veneto (Italy) is cooperating with the University of California, Santa Barbara and other researchers in Italy and the U.S.A. to develop a system of econometric crop production modeling. Five crops are to be included in this project: small grains (wheat and barley), corn, sugar beets, soybeans, orchards and vineyards. A critical part of the crop yield modeling process is the identification of crops using multispectral satellite data. This paper explores two strategies to improve crop classification accuracies: (1) use of ancillary data stored in digital format and (2) use of multitemporal data. Ancillary information stored on digital files were used in this research to remove (mask) non‐agricultural areas from satellite image data. Comparison between the classification of masked and unmasked images showed that improvement ranged from 3% to 26% depending on crop type. The multidate classification was performed by compiling an image of transformed spectral bands and three TM‐5 bands. The transformed bands were TM band 4 over TM band 3. Based on the work conducted in this study it is clear that crop type determination from satellite imagery is possible for small field agricultural areas such as those found in Italy.     

Land-Parcel Land-Use History as a Key to Site Selection for Documenting Soil Contamination Risk： a Case Study from Australian Suburbia

In that orcharding in early-to-mid twentieth century southeastern Australia involved use of certain heavy metal and As compounds in regular pest-control spray procedures, some interest attaches to the possibility that these landparcels are underlain by soils with above-background Cu, Pb and As levels. Interpreta- tion of Land-cover changes allowed land parcels previously occupied by orchards to be identified in the 1950s through time-series air-photos. A comparison of soil analysis results referring to soil samples from control sites, and from land parcels formerly occupied by orchardists, shows that contamination （above-background） levels of cations in the pesticides can be found in the top 6 cm of former orchard soils. It is clear that digital spatial data handling and culturally-informed air photo interpretation has a place in soil contamination studies, land-use planning （with particular reference to re-development） and in administration of public health.     

Land-parcel land-use history as a key to site selection for documenting soil contamination risk: A case study from Australian Suburbia

In that orcharding in early-to-mid twentieth century southeastern Australia involved use of certain heavy metal and As compounds in regular pest-control spray procedures, some interest attaches to the possibility that these landparcels are underlain by’ soils with above-background Cu, Pb and As levels. Interpretation of Land-cover changes allowed land parcels previously occupied by orchards to be identified in the 1950s through time-series air-photos. A comparison of soil analysis results referring to soil samples from control sites, and from land parcels formerly occupied by orchardists, shows that contamination (above-background) levels of cations in the pesticides can be found in the top 6 cm of former orchard soils. It is clear that digital spatial data handling and culturally-informed air photo interpretation has a place in soil contamination studies, land-use planning (with particular reference to re-development) and in administration of public health.     

Estimating residual biomass of olive tree crops using terrestrial laser scanning

Agricultural residues have gained increasing interest as a source of renewable energy. The development of methods and techniques that allow to inventory residual biomass needs to be explored further. In this study, the residual biomass of olive trees was estimated based on parameters derived from using a Terrestrial Laser Scanning System (TLS). To this end, 32 olive trees in 2 orchards in the municipality of Viver, Central Eastern Spain, were selected and measured using a TLS system. The residual biomass of these trees was pruned and weighed. Several algorithms were applied to the TLS data to compute the main parameters of the trees: total height, crown height, crown diameter and crown volume. Regarding the last parameter, 4 methods were tested: the global convex hull volume, the convex hull by slice volume, the section volume, and the volume measured by voxels. In addition, several statistics were computed from the crown points for each tree. Regression models were calculated to predict residual biomass using 3 sets of potential explicative variables: firstly, the height statistics retrieved from 3D cloud data for each crown tree, secondly, the parameters of the trees derived from TLS data and finally, the combination of both sets of variables. Strong relationships between residual biomass and TLS parameters (crown volume parameters) were found (R² = 0.86, RMSE = 2.78 kg). The pruning biomass prediction fraction was improved by 6%, in terms of R², when the variance of the crown-point elevations was selected (R² = 0.92, RMSE = 2.01 kg). The study offers some important insights into the quantification of residual biomass, which is essential information for the production of biofuel.     

2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models

One of the challenges of remote sensing and computer vision lies in the three-dimensional (3-D) reconstruction of individual trees by using automated methods through very high-resolution (VHR) data sets. However, a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions. In this paper, we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models (DSMs). The symmetry of the citrus trees in a DSM is handled by an orientation-based radial symmetry transform which is computed in a unique way. Next, we propose an efficient strategy to accurately build influence regions of each tree, and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy. We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds. Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes. Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.     

Reducing background effects in orchards through spectral vegetation index correction

Satellite remote sensing provides an alternative to time-consuming and labor intensive in situ measurements of biophysical variables in agricultural crops required for precision agriculture applications. In orchards, however, the spatial resolution causes mixtures of canopies and background (i.e. soil, grass and shadow), hampering the estimation of these biophysical variables. Furthermore, variable background mixtures obstruct meaningful comparisons between different orchard blocks, rows or within each row. Current correction methodologies use spectral differences between canopies and background, but struggle with a vegetated orchard floor. This background influence and the lack of a generic solution are addressed in this study.Firstly, the problem was demonstrated in a controlled environment for vegetation indices sensitive to chlorophyll content, water content and leaf area index. Afterwards, traditional background correction methods (i.e. soil-adjusted vegetation indices and signal unmixing) were compared to the proposed vegetation index correction. This correction was based on the mixing degree of each pixel (i.e. tree cover fraction) to rescale the vegetation indices accordingly and was applied to synthetic and WorldView-2 satellite imagery. Through the correction, the effect of background admixture for vegetation indices was reduced, and the estimation of biophysical variables was improved (ΔR² = 0.2–0.31).     

Land resource inventories for integrated land use planning with special reference to the catchment of Pohru river in Jammu and Kashmir state

A Soil and land Use Map of Pohru catchment was prepared through the adoption of a systematic Air Photo-interpretation procedure including selective field studies in such a manner that the ten delineated mapping units were co-extensive with major Land Resource Areas. Defined in terms of physiography, soil profile, soil erosion and present land use each of the ten units has unique characteristics requiring differential management, which was arrived at through joint field studies by Pedologists who prepared the Map, Foresters, Soil Conservationists, Agronomists. a Horticulturist and an Agricultural Chemist. Stemming from this integrated effort a Land Use Plan for the catchment was drawn up with its major objective as the conservation of Land and Soil Resources leading to a reduction in sediment yields that are now so high as to clog the Jhelum river at the polnt where Pohru river joins it. In respect of four Land Resource areas, the plan prescribes radical readjustments of Land Use such as conversion of Forest Department owned Himalayan Southern slopes to grass lands, change of cropping pattern in Karewa side slopes and conversion of the northern and southern Himalayan footslopes now under cultivation into orchards. The areal extents of the different Land Resource Areas as also the cost of implementing the conservation recommendations have been furnished for every one of the twentyeight watersheds falling under the eight subcatchments of the Pohru catchment. The total cost of implementing the conservation recommendations approximates to about fifty five million rupees.