Orchards, Plantations, Trees as Crops

Chapter Contents (Back)

Trias-Sanz, R.[Roger],
Texture Orientation and Period Estimator for Discriminating Between Forests, Orchards, Vineyards, and Tilled Fields,
GeoRS(44), No. 10, October 2006, pp. 2755-2760.

Trias-Sanz, R.[Roger], Boldo, D.[Didier],
A High-Reliability, High-Resolution Method for Land Cover Classification Into Forest and Non-forest,
Springer DOI 0506

Johansen, K.[Kasper], Phinn, S.R.[Stuart R.], Witte, C.[Christian], Philip, S.[Seonaid], Newton, L.[Lisa],
Mapping Banana Plantations from Object-oriented Classification of SPOT-5 Imagery,
PhEngRS(75), No. 9, September 2009, pp. 1069-1082.
WWW Link. 0910
The extent of banana plantations was mapped using panchromatic and multispectral SPOT-5 imagery and object-oriented segmentation and classification in Definiens Professional 5. BibRef

Reis, S.[Selçuk], Tasdemir, K.[Kadim],
Identification of hazelnut fields using spectral and Gabor textural features,
PandRS(66), No. 5, September 2011, pp. 652-661.
Elsevier DOI 1110
Orchard detection; Hazel orchards; Texture analysis; Multi-scale Gabor features; Self-organizing maps; Maximum likelihood classifier BibRef

Recio, J.A., Hermosilla, T., Ruiz, L.A.,
Automated extraction of agronomic parameters in orchard Plots from high-resolution imagery,
Other2012, pp. 161-174 In: The use of remote sensing and geographic information systems for irrigation amangement in Southwest Europe. CIHEAM.
PDF File. 1204

le Maire, G., Marsden, C., Nouvellon, Y., Stape, J., Ponzoni, F.,
Calibration of a Species-Specific Spectral Vegetation Index for Leaf Area Index (LAI) Monitoring: Example with MODIS Reflectance Time-Series on Eucalyptus Plantations,
RS(4), No. 12, December 2012, pp. 3766-3780.
DOI Link 1211

Aksoy, S., Yalniz, I.Z., Tasdemir, K.,
Automatic Detection and Segmentation of Orchards Using Very High Resolution Imagery,
GeoRS(50), No. 8, August 2012, pp. 3117-3131.

Stagakis, S., González-Dugo, V., Cid, P., Guillén-Climent, M.L., Zarco-Tejada, P.J.,
Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices,
PandRS(71), No. 1, July 2012, pp. 47-61.
Elsevier DOI 1208
Water stress; Remote sensing; Narrow-band indices; Fruit quality; Regulated deficit; PRI BibRef

Bernardes, T., Moreira, M., Adami, M., Giarolla, A., Rudorff, B.,
Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery,
RS(4), No. 9, September 2012, pp. 2492-2509.
DOI Link 1210

Recio, J.A., Hermosilla, T., Ruiz, L.A., Palomar, J.,
Automated extraction of tree and plot-based parameters in citrus orchards from aerial images,
CompElAg(90), 2013, pp. 24-34.
Elsevier DOI 1212

Balaguer-Beser, A., Ruiz, L.A., Hermosilla, T., Recio, J.A.,
Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images,
CompGosSci(50), 2013, pp. 115-127.
Elsevier DOI 1212

van Beek, J.[Jonathan], Tits, L.[Laurent], Somers, B.[Ben], Coppin, P.[Pol],
Stem Water Potential Monitoring in Pear Orchards through WorldView-2 Multispectral Imagery,
RS(5), No. 12, 2013, pp. 6647-6666.
DOI Link 1402
Corrections: See also Correction: Stem Water Potential Monitoring in Pear Orchards through WorldView-2 Multispectral Imagery. BibRef

van Beek, J.[Jonathan], Tits, L.[Laurent], Somers, B.[Ben], Janssens, P.[Pieter], Odeurs, W.[Wendy], Vandendriessche, H.[Hilde], Deckers, T.[Tom], Coppin, P.[Pol],
Correction: Stem Water Potential Monitoring in Pear Orchards through WorldView-2 Multispectral Imagery,
RS(6), No. 2, 2014, pp. 1760-1761.
DOI Link 1403
See also Stem Water Potential Monitoring in Pear Orchards through WorldView-2 Multispectral Imagery. BibRef

van Beek, J.[Jonathan], Tits, L.[Laurent], Somers, B.[Ben], Deckers, T.[Tom], Verjans, W.[Wim], Bylemans, D.[Dany], Janssens, P.[Pieter], Coppin, P.[Pol],
Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards,
RS(7), No. 8, 2015, pp. 9886.
DOI Link 1509

Chen, B.Q.[Bang-Qian], Wu, Z.X.[Zhi-Xiang], Wang, J.[Jikun], Dong, J.W.[Jin-Wei], Guan, L.M.[Li-Ming], Chen, J.M.[Jun-Ming], Yang, K.[Kai], Xie, G.S.[Gui-Shui],
Spatio-temporal prediction of leaf area index of rubber plantation using HJ-1A/1B CCD images and recurrent neural network,
PandRS(102), No. 1, 2015, pp. 148-160.
Elsevier DOI 1503
Leaf area index BibRef

Fagan, M.E.[Matthew E.], de Fries, R.S.[Ruth S.], Sesnie, S.E.[Steven E.], Arroyo-Mora, J.P.[J. Pablo], Soto, C.[Carlomagno], Singh, A.[Aditya], Townsend, P.A.[Philip A.], Chazdon, R.L.[Robin L.],
Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery,
RS(7), No. 5, 2015, pp. 5660-5696.
DOI Link 1506

Fan, H.[Hui], Fu, X.H.[Xiao-Hua], Zhang, Z.[Zheng], Wu, Q.[Qiong],
Phenology-Based Vegetation Index Differencing for Mapping of Rubber Plantations Using Landsat OLI Data,
RS(7), No. 5, 2015, pp. 6041-6058.
DOI Link 1506

Zhang, Q.[Qian], Ju, W.M.[Wei-Min], Chen, J.M.[Jing M.], Wang, H.M.[Hui-Min], Yang, F.T.[Feng-Ting], Fan, W.L.[Wei-Liang], Huang, Q.[Qing], Zheng, T.[Ting], Feng, Y.K.[Yong-Kang], Zhou, Y.L.[Yan-Lian], He, M.Z.[Ming-Zhu], Qiu, F.[Feng], Wang, X.J.[Xiao-Jie], Wang, J.[Jun], Zhang, F.M.[Fang-Min], Chou, S.[Shuren],
Ability of the Photochemical Reflectance Index to Track Light Use Efficiency for a Sub-Tropical Planted Coniferous Forest,
RS(7), No. 12, 2015, pp. 15860.
DOI Link 1601

Qiao, H.L.[Hai-Lang], Wu, M.Q.[Ming-Quan], Shakir, M.[Muhammad], Wang, L.[Li], Kang, J.[Jun], Niu, Z.[Zheng],
Classification of Small-Scale Eucalyptus Plantations Based on NDVI Time Series Obtained from Multiple High-Resolution Datasets,
RS(8), No. 2, 2016, pp. 117.
DOI Link 1603

Torbick, N.[Nathan], Ledoux, L.[Lindsay], Salas, W.[William], Zhao, M.[Meng],
Regional Mapping of Plantation Extent Using Multisensor Imagery,
RS(8), No. 3, 2016, pp. 236.
DOI Link 1604

López-López, M.[Manuel], Calderón, R.[Rocío], González-Dugo, V.[Victoria], Zarco-Tejada, P.J.[Pablo J.], Fereres, E.[Elías],
Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery,
RS(8), No. 4, 2016, pp. 276.
DOI Link 1604

Bulanon, D.M.[Duke M.], Lonai, J.[John], Skovgard, H.[Heather], Fallahi, E.[Esmaeil],
Evaluation of Different Irrigation Methods for an Apple Orchard Using an Aerial Imaging System,
IJGI(5), No. 6, 2016, pp. 79.
DOI Link 1608

Bellvert, J.[Joaquim], Marsal, J.[Jordi], Girona, J.[Joan], Gonzalez-Dugo, V.[Victoria], Fereres, E.[Elías], Ustin, S.L.[Susan L.], Zarco-Tejada, P.J.[Pablo J.],
Airborne Thermal Imagery to Detect the Seasonal Evolution of Crop Water Status in Peach, Nectarine and Saturn Peach Orchards,
RS(8), No. 1, 2016, pp. 39.
DOI Link 1602

Middleton, E.M.[Elizabeth M.], Rascher, U.[Uwe], Corp, L.A.[Lawrence A.], Huemmrich, K.F.[K. Fred], Cook, B.D.[Bruce D.], Noormets, A.[Asko], Schickling, A.[Anke], Pinto, F.[Francisco], Alonso, L.[Luis], Damm, A.[Alexander], Guanter, L.[Luis], Colombo, R.[Roberto], Campbell, P.K.E.[Petya K. E.], Landis, D.R.[David R.], Zhang, Q.[Qingyuan], Rossini, M.[Micol], Schuettemeyer, D.[Dirk], Bianchi, R.[Remo],
The 2013 FLEX: US Airborne Campaign at the Parker Tract Loblolly Pine Plantation in North Carolina, USA,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706

Park, S.Y.[Su-Young], Ryu, D.[Dongryeol], Fuentes, S.[Sigfredo], Chung, H.[Hoam], Hernández-Montes, E.[Esther], O'Connell, M.[Mark],
Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV),
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708

Dube, T.[Timothy], Mutanga, O.[Onisimo],
The impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa,
PandRS(119), No. 1, 2016, pp. 415-425.
Elsevier DOI 1610
Aboveground carbon mapping BibRef

Dube, T.[Timothy], Mutanga, O.[Onisimo],
Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas,
PandRS(108), No. 1, 2015, pp. 12-32.
Elsevier DOI 1511
Estimation accuracy BibRef

Ciriza, R.[Raquel], Sola, I.[Ion], Albizua, L.[Lourdes], Álvarez-Mozos, J.[Jesús], González-Audícana, M.[María],
Automatic Detection of Uprooted Orchards Based on Orthophoto Texture Analysis,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706

Ye, S.[Su], Rogan, J.[John], Sangermano, F.[Florencia],
Monitoring rubber plantation expansion using Landsat data time series and a Shapelet-based approach,
PandRS(136), 2018, pp. 134-143.
Elsevier DOI 1802
Time series, Shapelet, Rubber plantations, Landsat, Forest mapping BibRef

Zhai, D.L.[De-Li], Dong, J.[Jinwei], Cadisch, G.[Georg], Wang, M.C.[Ming-Cheng], Kou, W.L.[Wei-Li], Xu, J.C.[Jian-Chu], Xiao, X.M.[Xiang-Ming], Abbas, S.[Sawaid],
Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802

Geng, J., Chen, J.M., Fan, W., Tu, L., Tian, Q., Yang, R., Yang, Y., Wang, L., Lv, C., Wu, S.,
GOFP: A Geometric-Optical Model for Forest Plantations,
GeoRS(55), No. 9, September 2017, pp. 5230-5241.
vegetation, vegetation mapping, 3-D canopy visualization technique, Moderate Resolution Imaging Spectroradiometer surface reflectance, Neyman model, Poisson model, bidirectional reflectance distribution function, bidirectional reflectance factor, forest plantations, hypergeometric model, natural forest canopies, BibRef

Robson, A.[Andrew], Rahman, M.M.[Muhammad Moshiur], Muir, J.[Jasmine],
Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802

de la Fuente-Sáiz, D.[Daniel], Ortega-Farías, S.[Samuel], Fonseca, D.[David], Ortega-Salazar, S.[Samuel], Kilic, A.[Ayse], Allen, R.[Richard],
Calibration of METRIC Model to Estimate Energy Balance over a Drip-Irrigated Apple Orchard,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708

Salgadoe, A.S.A.[Arachchige Surantha Ashan], Robson, A.J.[Andrew James], Lamb, D.W.[David William], Dann, E.K.[Elizabeth Kathryn], Searle, C.[Christopher],
Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804

Johansen, K.[Kasper], Raharjo, T.[Tri], McCabe, M.F.[Matthew F.],
Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806

Kelley, L.C.[Lisa C.], Pitcher, L.[Lincoln], Bacon, C.[Chris],
Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806

Chemura, A.[Abel], Mutanga, O.[Onisimo], Odindi, J.[John], Kutywayo, D.[Dumisani],
Mapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data,
PandRS(138), 2018, pp. 1-11.
Elsevier DOI 1804
Nutrient management, Random forest, Canopy nitrogen, Precision agriculture BibRef

Chen, B.Q.[Bang-Qian], Xiao, X.M.[Xiang-Ming], Wu, Z.X.[Zhi-Xiang], Yun, T.[Tin], Kou, W.[Weili], Ye, H.C.[Hui-Chun], Lin, Q.H.[Qing-Huo], Doughty, R.[Russell], Dong, J.[Jinwei], Ma, J.[Jun], Luo, W.[Wei], Xie, G.S.[Gui-Shui], Cao, J.H.[Jian-Hua],
Identifying Establishment Year and Pre-Conversion Land Cover of Rubber Plantations on Hainan Island, China Using Landsat Data during 1987-2015,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809

Chen, G.[Gang], Thill, J.C.[Jean-Claude], Anantsuksomsri, S.[Sutee], Tontisirin, N.[Nij], Tao, R.[Ran],
Stand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series,
PandRS(144), 2018, pp. 94-104.
Elsevier DOI 1809
Stand age estimation, Rubber plantation, Geographic object-based image analysis, Landsat time series, Tree growth model BibRef

Yang, Z.[Zhiqi], Dong, J.W.[Jin-Wei], Qin, Y.W.[Yuan-Wei], Ni, W.J.[Wen-Jian], Zhao, G.S.[Guo-Song], Chen, W.[Wei], Chen, B.Q.[Bang-Qian], Kou, W.L.[Wei-Li], Wang, J.[Jie], Xiao, X.M.[Xiang-Ming],
Integrated Analyses of PALSAR and Landsat Imagery Reveal More Agroforests in a Typical Agricultural Production Region, North China Plain,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Iizuka, K.[Kotaro], Watanabe, K.[Kazuo], Kato, T.[Tsuyoshi], Putri, N.A.[Niken Andika], Silsigia, S.[Sisva], Kameoka, T.[Taishin], Kozan, O.[Osamu],
Visualizing the Spatiotemporal Trends of Thermal Characteristics in a Peatland Plantation Forest in Indonesia: Pilot Test Using Unmanned Aerial Systems (UASs),
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Al-Ruzouq, R.[Rami], Shanableh, A.[Abdallah], Gibril, M.B.A.[Mohamed Barakat A.], AL-Mansoori, S.[Saeed],
Image Segmentation Parameter Selection and Ant Colony Optimization for Date Palm Tree Detection and Mapping from Very-High-Spatial-Resolution Aerial Imagery,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Sarron, J.[Julien], Malézieux, É.[Éric], Sané, C.A.B.[Cheikh Amet Bassirou], Faye, É.[Émile],
Mango Yield Mapping at the Orchard Scale Based on Tree Structure and Land Cover Assessed by UAV,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Rahman, M.M.[Muhammad Moshiur], Robson, A.[Andrew], Bristow, M.[Mila],
Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

She, Y.[Ying], Ehsani, R.[Reza], Robbins, J.[James], Leiva, J.N.[Josué Nahún], Owen, J.[Jim],
Applications of High-Resolution Imaging for Open Field Container Nursery Counting,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Shaharum, N.S.N., Shafri, H.Z.M., Ghani, W.A.W.A.K., Samsatli, S., Yusuf, B., Al-Habshi, M.M.A., Prince, H.M.,
Image Classification for Mapping Oil Palm Distribution Via Support Vector Machine Using Scikit-learn Module,
DOI Link 1901

Alves, H.M.R., Vieira, T.G.C., Volpato, M.M.L., Lacerda, M.P.C., Borém, F.M.,
Geotechnologies For The Characterization Of Specialty Coffee Environments Of Mantiqueira De Minas In Brazil,
ISPRS16(B8: 797-799).
DOI Link 1610

Alves, H.M.R., Volpato, M.M.L., Vieira, T.G.C., Maciel, D.A., Gonçalves, T.G., Dantas, M.F.,
Characterization And Spectral Monitoring Of Coffee Lands In Brazil,
ISPRS16(B8: 801-803).
DOI Link 1610

Nogueira, K.[Keiller], Schwartz, W.R.[William Robson], dos Santos, J.A.[Jefersson A.],
Coffee Crop Recognition Using Multi-scale Convolutional Neural Networks,
Springer DOI 1511

Kattenborn, T., Sperlich, M., Bataua, K., Koch, B.,
Automatic Single Tree Detection in Plantations using UAV-based Photogrammetric Point clouds,
DOI Link 1404

Alves, H.M.R., Vieira, T.G.C., Souza, V.C.O., Bertoldo, M.A., Lacerda, M.P.C., Andrade, H., Bernardes, N.,
Monitoring the Relationships between Environment and Coffee Production in Agroecosytems of the State of Minas Gerais in Brazil,
PDF File. 0607

Vieira, T.G.C., Alves, H.M.R., Souza, V.C.O., Bernardes, T., Lacerda, M.P.C.,
Assessing and Mapping Changes, in Space and Time, of Coffee Lands of the State of Minas Gerais in Brazil,
PDF File. 0607

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