Resultados de búsqueda - Sense data.

  1. Landscape-scale variability of soil health indicators: effects of cultivation on soil organic carbon in the Usambara Mountains of Tanzania por Winowiecki, Leigh Ann, Vågen, Tor-Gunnar, Massawe, Boniface H.J., Jelinski, Nicolas A., Lyamchai, Charles, Sayula, George, Msoka, Elizabeth

    Publicado 2016
    “…Prediction models were developed for the mapping of SOC based on RapidEye remote sensing data for January 2014, with good model performance (RMSEPcal = 8.0 g kg−1; RMSEPval = 10.5 g kg−1) and a SOC map was generated for the study. …”
    Enlace del recurso
    Journal Article
  2. Determination of eligible lands for A/R CDM project activities and of priority Districts for project development support in Indonesia por Murdiyarso, Daniel, Puntodewo, A., Widayati, A., Noordwijk, Meine van

    Publicado 2006
    “…Based on the best available remote sensing data from before 1990, the total area of formally eligible lands in Indonesia for the aff orestation and reforestation (A/R) Clean Development Mechanism (CDM) activities under the Kyoto protocol is about 46 M ha. …”
    Enlace del recurso
    Libro
  3. Menuju kesejahteraan: pemantauan kemiskinan di Kutai Barat, Indonesia por Gonner, C., Cahyat, A., Haug, M., Limberg, G.

    Publicado 2007
    “…Hasil survei terhadap sekitar 10000 rumah tangga dari 222 desa, 6 penelitian khusus mengenai masyarakat, data penginderaan jarak jauh dan beberapa lokakarya, menunjukkan aspek-aspek kesejahteraan mana yang berada dalam kondisi kritis dan di bagian mana intervensi sangat dibutuhkan segera. …”
    Enlace del recurso
    Libro
  4. Spatially explicit regionalization of airborne flux measurements using environmental response functions por Metzger, S., Junkermann, W., Mauder, M., Butterbach-Bahl, Klaus, Trancon y Widemann, B., Neidl, F., Schafer, K., Wieneke, S., Zheng, X.H., Schmid, H.P., Foken, T.

    Publicado 2013
    “…Wavelet decomposition of the turbulence data enables a spatial discretization of 90m of the flux measurements. …”
    Enlace del recurso
    Journal Article
  5. 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.

    Publicado 2016
    “…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). …”
    Enlace del recurso
    Journal Article
  6. An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE) por Medina Medina, Angel James, Salas López, Rolando, Zabaleta Santisteban, Jhon Antony, Tuesta Trauco, Katerin Meliza, Turpo Cayo, Efrain Yury, Huaman Haro, Nixon, Oliva Cruz, Manuel, Gómez Fernández, Darwin

    Publicado 2024
    “…Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.…”
    Enlace del recurso
    Enlace del recurso
    Artículo
  7. 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.

    Publicado 2024
    “…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. …”
    Enlace del recurso
    Journal Article
  8. Food system innovations and digital technologies to foster productivity growth and rural transformation por Benfica, Rui, Chambers, Judith A., Koo, Jawoo, Nin-Pratt, Alejandro, Falck-Zepeda, José B., Stads, Gert-Jan, Arndt, Channing

    Publicado 2021
    “…Given their transformative potential and the urgency of developing the enabling R&D and trajectories required for impact, we highlight genome editing bio-innovations, specifically CRISPR-Cas9, to address sustainable agricultural growth; and digital technologies, including remote sensing, connected sensors, artificial intelligence, digital advisory services, digital financial services, and e-commerce, to help guide the operations and decision-making of farmers, traders, and policymakers in agricultural value chains. …”
    Enlace del recurso
    Brief
  9. Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites por Schut, Antonius G.T., Sibiry Traoré, Pierre C., Blaes, Xavier, By, Rolf A. de

    Publicado 2018
    “…Fortnightly, in-situ in each field data were collected synchronous with UAV imaging using a Canon S110 NIR camera. …”
    Enlace del recurso
    Journal Article

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