Resultados de búsqueda - "Sense data."

Anthropogenic Decline of Ecosystem Services Threatens the Integrity of the Unique Hyrcanian (Caspian) Forests in Northern Iran por Zarandian, A., Baral, H., Yavari, A.R., Jafari, H.R., Stork, N.E., Ling, M.A., Amirnejad, H.

“…A rapid, qualitative, and participatory approach was used including interviews with local households and experts in combination with assessment of land use/cover remote sensing data to identify and map priority ecosystem services in the Geographic Information System (GIS). …”
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A scenario modelling approach to assess management impacts on soil erosion in coffee systems in Central America por Ospina, Alejandra

“…Soil erosion was modelled using the RUSLE (Revised Universal Soil Loss Equation), integrating soil and vegetation cover field data, with remote sensing data. Management scenarios were developed to assess the role of two principal coffee management strategies in mitigating soil erosion: increasing vegetation cover, and soil conservation practices. …”
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Identificación de tipos de bosques primarios en la zona norte de Costa Rica por Gallo, Melibea

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Classification of ground lichen using Sentinel-2 and airborne laser data por Larsson, Helene

“…Lichens are the primary winter grazing resource for reindeer, therefore, mapping of lichens is of interest.The objective of this study is to evaluate the use of remote sensing data from the new Sentinel-2 satellite for the classifcation of ground lichen and to assess whether adding information derived from airborne laser scanning (ALS) willimprove the result. …”

Land degradation assessment in the Argentinean Puna: Comparing expert knowledge with satellite-derived information por García, César Luis, Teich, Ingrid, Gonzalez Roglich, Mariano, Kindgard, Adolfo Federico, Ravelo, Andrés Carlos, Liniger, Hanspeter

“…In this article we compare the inferences on land degradation status and its temporal trends derived from LADA-WOCAT method with those obtained from remotely sensed data. Our aim is to understand similarities and differences in the assessments in order to provide recommendations and suggestions on improved LDN assessment methods, reporting and monitoring. …”
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Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments por Fita, David, San Bautista, Alberto, Castiñeira-Ibañez, Sergio, Franch, Belén, Domingo, Concha, Rubio, Constanza

“…Two separate relationships between NIR–red and NDVI–NIR were identified, suggesting that using remote sensing data can help enhance rice crop management by adjusting nitrogen input based on plant nitrogen concentration and yield estimates. …”
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Assessing potential locations for flood-based farming using satellite imagery: a case study of Afar region, Ethiopia por Gumma, Murali K., Amede, T., Getnet, M., Pinjarla, B., Panjala, P., Legesse, G., Tilahun, G., Akker, Elisabeth van den, Berdel, W., Keller, C., Siambi, M., Whitbread, Anthony M.

“…In an attempt to convert water from storm generated floods into productive use, this study proposes a methodology using remote sensing data and geographical information system tools to identify potential sites where flood spreading weirs may be installed and farming systems developed which produce food and fodder for poor rural communities. …”
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Coupling remote sensing and hydrological model for evaluating the impacts of climate change on streamflow in data-scarce environment por Akhtar, F., Awan, Usman Khalid, Borgemeister, C., Tischbein, B.

“…To mitigate the impact of climate change on reduced/increased surface water availability, the SWAT model, when combined with remote sensing data, can be an effective tool to support the sustainable management and strategic planning of water resources. …”
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Machine learning model accurately predict maize grain yields in conservation agriculture systems in southern Africa por Muthoni, Francis K., Thierfelder, Christian L., Mudereri, B.T., Manda, J., Bekunda, Mateete A., Hoeschle-Zeledon, Irmgard

“…Results demonstrates that multi-source remotely sensed data, coupled with advanced and efficient machine learning algorithms can provides accurate, cost-effective, and timely platform for predicting the optimal locations for the upscaling sustainable agricultural technologies.…”
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Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms por Tramontana, Gianluca, Jung, Martin, Schwalm, Christopher R., Ichii, Kazuhito, Camps-Valls, Gustau, Ráduly, Botond, Reichstein, Markus, Arain, M. Altaf, Cescatti, Alessandro, Kiely, Gerard, Merbold, Lutz, Serrano-Ortiz, Penelope, Sickert, Sven, Wolf, Sebastian, Papale, Dario

“…We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. …”
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Assessing climate resilience in rice production: Measuring the impact of the Millennium Challenge Corporation’s IWRM scheme in the Senegal River Valley using remote sensing and mac... por Fionnagáin, D.Ó., Geever, M., O’Farrell, J., Codyre, P., Trearty, R., Tessema, Y.M., Reymondin, Louis, Loboguerrero, Ana Maria, Spillane, Charlie, Golden, A

“…Economic analysis of increased rice yields in the region translates to approximately US\$ 61.2 million in market value since 2015, highlighting the economic returns from the project investment. Both the remote sensing data and ground audits identify issues regarding post-project deterioration of irrigation infrastructure, emphasising the need for long-term maintenance of irrigation infrastructure to support climate adaptation benefits arising from irrigation. …”
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From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach por Carbajal, M., Ramírez, D., Turin, C., Schaeffer, S.M., Konkel, J., Ninanya, J., Rinza, J., De Mendiburu, F., Zorogastua, P., Villaorduña, L., Quiroz, R.

“…Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variables using remote sensing data, land-use and land-cover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. …”
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Spatial rice yield estimation based on MODIS and Sentinel-1 SAR data and ORYZA crop growth model por Setiyono, Tri, Quicho, Emma, Gatti, Luca, Campos-Taberner, Manuel, Busetto, Lorenzo, Collivignarelli, Francesco, García-Haro, Francisco, Boschetti, Mirco, Khan, Nasreen, Holecz, Francesco

“…The developed system combines MODIS and SAR-based remote-sensing data to generate spatially explicit inputs for rice using a crop growth model. …”
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Technical workflow development for integrating drone surveys and entomological sampling to characterise aquatic larval habitats of Anopheles funestus in agricultural landscapes in... por Byrne, Isabel, Chan, Kallista, Manrique, Edgar, Lines, Jo, Wolie, Rosine Z., Trujillano, Fedra, Garay, Gabriel Jimenez

“…Using satellite remote sensing data, we developed an environmentally and spatially representative sampling frame and conducted paired mosquito larvae and drone mapping surveys from June to August 2021. …”
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A global benchmark map of water productivity for rainfed and irrigated wheat por Zwart, Sander J., Bastiaanssen, Wim G.M., Fraiture, Charlotte de, David, S.

“…The WATPRO water productivity model for wheat, using remote sensing data products as input, was applied at a global scale with global data sets of the NDVI and surface albedo to benchmark water productivity of wheat for the beginning of this millennium. …”
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Spatio-temporal distribution of actual evapotranspiration in the Indus basin irrigation system por Liaqat, U.W., Choi, M., Awan, Usman K.

“…In this study, we examined the application of SEBS using publically available remote sensing data to assess spatial variations in water consumption and to map water stress from daily to annual scales in the IBIS. …”
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Rapid emergency response mapping for the 2016 floods in Kelani river basin, Sri Lanka por Alahacoon, Niranga, Pani, Peejush, Matheswaran, Karthikeyan, Samansiri, Srimal, Amarnath, Giriraj

“…This is mainly due to the large and timely availability of different types of remotely sensed data as well as geospatial information acquired in the field which may be potentially exploited in the different phases of the disaster management cycle. …”
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Pan-tropical prediction of forest structure from the largest trees por Bastin, J.F., Rutishauser, E., Kellner, J.R., Saatchi, S., Pélissier, R., Hérault, B., Slik, F., Bogaert, J., Cannière, C. de, Marshall, A.R., Poulsen, J., Álvarez Loyayza, P., Andrade, A., Angbonga-Basia, A., Araújo Murakami, Alejandro, Arroyo, L., Ayyappan, N., Paulo de Azevedo, C., Banki, O., Barbier, N., Barroso, Jorcely G., Beeckman, H., Bitariho, R., Boeckx, P., Boehning-Gaese, K., Brandao, H., Brearley, F.Q., Breuer Ndoundou Hockemba, M., Brienen, R., Camargo, J.L.C., Campos Arceiz, A., Cassart, B., Chave, J., Chazdon, R., Chuyong, G.B., Clark, D.B., Clark, C.J., Condit, R., Honorio Coronado, E.N., Davidar, P., Haulleville, T. de, Descroix, L., Doucet, J-L., Dourdain, A., Droissart, V., Duncan, T., Silva Espejo, J.E., Espinosa, S., Farwig, N., Fayolle, A., Feldpausch, T.R., Ferraz, A., Fletcher, C., Gajapersad, K., Gillet, J.F., Leao do Amaral, I., Gonmadje, C., Grogan, J., Harris, D., Herzog, S.K., Homeier, J., Hubau, W., Hubbell, S.P., Hufkens, K., Hurtado, J., Kamdem, N.G., Kearsley, E., Kenfack, D., Kessler, M., Labrière, N., Laumonier, Y., Laurance, S.G.W., Laurance, W.F., Lewis, S.L., Libalah, M.B., Ligot, G., Lloyd, J., Lovejoy, Thomas E., Malhi, Y., Marimon, B.S., Marimon Junior, B.H., Martin, E.H., Matius, P., Meyer, V., Mendoza Bautista, C., Monteagudo Mendoza, Abel L., Mtui, A., Neill, D., Parada Gutierrez, G.A., Pardo, G., Parren, M., Parthasarathy, N., Phillips, Oliver L., Pitman, N.C.A., Ploton, P., Ponette, Q., Ramesh, B.R., Razafimahaimodison, J-C., Réjou-Méchain, M., Gonçalves Rolim, S., Romero Saltos, H., Brum Rossi, L.M., Spironello, W.R., Rovero, F., Saner, P., Sasaki, D., Schulze, M., Silveira, M., Singh, J., Sist, P., Sonke, B., Soto, J.D., Rodrigues de Souza, C., Stropp, J., Sullivan, M.J.P., Swanepoel, B., Ter Steege, H., Terborgh, J., Texier, N., Toma, T., Valencia, R., Valenzuela, L., Valle Ferreira, L., Cornejo Valverde, F., Andel, T.R. van, Vásque, R., Verbeeck, H., Vivek, P., Vleminckx, J., Vos, V.A., Wagner, F.H., Warsudi, P.P., Wortel, V., Zagt, R.J., Zebaze, D.

“…However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. …”
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From rangelands to cropland, land-use change and its impact on soil organic carbon variables in a Peruvian Andean highlands: a machine learning modeling approach por Carbajal, Mariella, Ramirez, David A., Turin Canchaya, Cecilia Claudia, Schaeffer, Sean M., Konkel, Julie, Ninanya, Johan, Rinza, Javier, De Mendiburu, Felipe, Zorogastua, Percy, Villaorduña, Liliana, Quiroz, Roberto

“…Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variablesusing remote sensing data, land-use and landcover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. …”
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Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes por Gaitan, Juan Jose, Bran, Donaldo Eduardo, Oliva, Gabriel Esteban, Ciari, Georgina, Nakamatsu, Viviana Beatriz, Salomone, Jorge Manuel, Ferrante, Daniela, Buono, Gustavo Gabriel, Massara Paletto, Virginia, Humano, Gervasio, Celdran, Diego Javier, Opazo, Walter Javier, Maestre, Fernando Tomás

“…Approaches based on remote sensing data can successfully assess large areas, but it is largely unknown how the different indices that can be derived from such data relate to ground-based indicators of ecosystem health. …”
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