Search Results - Sense data.

Insight to Ecosystem Based Approach (EBA) at landscape level using a geospatial medium by Nagabhatla, N., Senaratna Sellamuttu, Sonali, Bobba, A.G., Finlayson, C. Max, Wickramasuriya, R., Brakel, Martin L. van, Prasad, S.N., Pattanaik, C.

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Analyzing multi-year rice-fallow dynamics in Odisha using multi-temporal Landsat-8 OLI and Sentinel-1 Data by Chandna, Parvesh Kumar, Mondal, Saptarshi

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PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series by Boschetti, Mirco, Busetto, Lorenzo, Manfron, Giacinto, Laborte, Alice, Asilo, Sonia, Pazhanivelan, Sellaperumal, Nelson, Andrew

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Crop development with data-driven approach towards sustainable agriculture: Lifting the achievements and opportunities of collaborative research between CIAT and Japan by Ogawa, Satoshi, Selvaraj, Michael Gomez, Ishitani, Manabu

“…Nowadays, it is presumed that this approach to data-driven agriculture realization will help establish a sustainable agroecosystem with increased agricultural productivity and sustainability by adapting or mitigating the effects of climate change and efficient use of natural resources and establishing a sustainable food value chain. …”
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Estimation of Glacier Outline and Volume Changes in the Vilcanota Range Snow-Capped Mountains, Peru, Using Temporal Series of Landsat and a Combination of Satellite Radar and Aeria... by Montoya-Jara, N., Loayza, H., Gutiérrez-Rosales, R.O., Bueno, M., Quiroz, R.

“…In general, it was possible to establish a reduction in both the area and volume of the Suyuparina and Quisoquipina glaciers based on freely accessible remote sensing data.…”
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Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2 by Bontemps, Sophie, Arias, Marcela, Cara, Cosmin, Dedieu, Gérard, Guzzonato, Eric, Hagolle, Olivier, Inglada, Jordi, Matton, Nicolas, Morin, David, Popescu, Ramona, Rabaute, Thierry, Savinaud, Mickael, Sepulcre, Guadalupe, Valero, Silvia, Ahmad, Ijaz, Bégué, Agnès, Wu, Bingfang, De Abelleyra, Diego, Diarra, Alhousseine, Dupuy, Stéphane, French, Andrew, Akhtar, Ibrar ul Hassan, Kussul, Nataliia, Lebourgeois, Valentine, Le Page, Michel, Newby, Terrence, Savin, Igor, Veron, Santiago Ramón, Koetz, Benjamin, Defourny, Pierre

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Rainfall variability and trends over the African continent using TAMSAT data (1983-2020): towards climate change resilience and adaptation by Alahacoon, Niranga, Edirisinghe, M., Simwanda, M., Perera, E. N. C., Nyirenda , V. R., Ranagalage, M.

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Change detection of Sri Ram Sagar Project-1 command area using geospatial tools by Pranay, Panjala

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Land and forest degradation inside protected areas in Latin America by Leisher, C., Touval, Jerome L., Hess, SM, Boucher, TM, Reymondin, Louis

“…Using six years of remote sensing data, we estimated land and forest degradation inside 1788 protected areas across 19 countries in Latin America. …”
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Using Sentinel-1, Sentinel-2, and planet imagery to map crop type of smallholder farms by Rao, Preeti, Zhou, Weiqi, Bhattarai, Nishan, Srivastava, Amit K., Singh, Balwinder, Poonia, Shishpal, Lobell, David B., Jain, Meha

“…Remote sensing offers a way to map crop types across large spatio-temporal scales at low costs. …”
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From YOLO to VLMs: advancing zero-shot and few-shot detection of wastewater treatment plants using satellite imagery in MENA Region by Premarathna, Akila, Hewageegana, Kanishka, Garcia Andarcia, Mariangel

“…The dataset comprises 1,207 validated WWTP locations (198 UAE, 354 KSA, 655 Egypt) and equal non-WWTP sites from field/AI data, as 600m×600m GeoTIFF images (Zoom 18, EPSG:4326). …”
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Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR by Brede, B., Lau, A., Bartholomeus, H.M., Kooistra, L.

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Water demand and use: Algeria by Belhaj Fraj, Makram, Bergaoui, Karim, Bellahreche, A., Schmitter, Petra, Dembélé, Moctar

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Mapping global cropping system: Challenges, opportunities and future perspectives by You, Liangzhi, Sun, Zhanli

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Advancements in satellite observations of inland and coastal waters: building towards a global validation network by Avouris, D. M., Maciel, F., Sharp, S. L., Craig, S. E., Dekker, A. G., Di Vittorio, C. A., Gardner, J. R., Goldsmith, E., Gossn, J. I., Greb, S. R., Grunert, B. K., Gurlin, D., Jampani, Mahesh, Khan, R. M., Lowin, B., McKinna, L., Mouw, C. B., Ogashawara, I., Calle, S. R., Salls, W., Sánchez-Cabeza, J.-A., Schaeffer, B., Seegers, B. N., Silander, J., Smail, E. A., Wang, M., Werdell, J.

“…Finally, we provide considerations for future field campaigns to improve coordination with remote sensing data collection and ensure that field data is well suited for use in model or algorithm development.…”
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Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley by Pizarro Carcausto, Samuel, Vera Vilchez, Jesús Emilio, Huamani, Joseph, Cruz, Juancarlos, Lastra Paucar, Sphyros Roomel, Solórzano Acosta, Richard Andi, Verástegui Martínez, Patricia

“…Hence the need know the spatial distribution of elements in soils, we mapped 25 elements, namely Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn and V, using various geospatial datasets, such as remote sensing, climate, topography, soil data, and distance, to establish the spatial estimation models of spatial distribution trained trough machine learning model with a supervised dataset of 109 topsoil samples, into Google earth engine platform. …”
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Multispectral drone image analysis for shade tree functional traits and climate response in cocoa agroforestry systems by Abdulai, I., Grunther, N.P.K., Asare, R., Rahman, M.H., Rotter, R., Hofmann, M.

“…Advanced research approach of using remote sensing techniques in agroforestry systems research has been limited. …”
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Assessing and projecting land use land cover changes using machine learning and artificial neural network models in Guder watershed, Ethiopia by Demessie, Sintayehu Fetene, Dile, Yihun T., Bedadi, Bobe, Tarkegn, Temesgen Gashaw, Bayabil, Haimanote Kebede, Dejene, Sintayehu Workeneh

“…The research utilizes an integrated approach combining remote sensing (RS) and GIS for spatial analysis, Google Earth Engine (GEE) for cloud-based data processing, Random Forest (RF) machine learning for historical LULC classification, and an artificial neural network (ANN) model via QGIS's MOLUSCE tool for future LULC predictions. …”
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Fragmentation increases wind disturbance impacts on forest structure and carbon stocks in a western Amazonian landscape by Schwartz, N., Uriarte, M.A, DeFries, Ruth S., Bedka, K.M., Fernandes, K., Gutiérrez Vélez, Victor Hugo, Pinedo Vasquez, M.

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Improving crop type mapping by integrating LSTM with temporal random masking and pixel-set spatial information by Zhang, Xinyu, Zhiwen, Cai, Hu, Qiong, Yang, Jingya, Wei, Haodong, You, Liangzhi, Xu, Baodong

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