Results 21 to 30 of about 17,569,314 (273)

Monitoring rangeland vegetation in the Sahel by Landsat MSS and NOAA AVHRR

, 1991
Quantities of herbaceous vegetation of Sahelian rangelands in Niger and Mali were compared to vegetation indices (VI) derived from Landsat MSS and NOAA AVHRR LAC images.
Hiederer, Roland
core   +7 more sources

Using Landsat and Sentinel-2 spectral time series to detect East African small woodlots

Science of Remote Sensing, 2023
Accurate maps of gains in tree cover are necessary to quantify carbon storage, wildlife habitat, and land use changes. Satellite-based mapping of emerging smallholder woodlots in heterogeneous landscapes of sub-Saharan Africa is challenging. Our goal was
Niwaeli E. Kimambo, Volker C. Radeloff
doaj   +1 more source

Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Extracting building construction time is crucial for effective land resource management and sustainable urban development, particularly in fast-growing urban areas.
Tengyun Hu, Meng Zhang, Xuecao Li, Tinghai Wu, Qiwei Ma, Jianneng Xiao, Xieqin Huang, Jinchen Guo, Yangchun Li, Donglie Liu   +9 more
doaj   +2 more sources

Gap Filling for Historical Landsat NDVI Time Series by Integrating Climate Data

Remote Sensing, 2021
High-quality Normalized Difference Vegetation Index (NDVI) time series are essential in studying vegetation phenology, dynamic monitoring, and global change.
Wentao Yu, Jing Li, Qinhuo Liu, Jing Zhao, Yadong Dong, Xinran Zhu, Shangrong Lin, Hu Zhang, Zhaoxing Zhang   +8 more
doaj   +1 more source

Continuity of Top-of-Atmosphere, Surface, and Nadir BRDF-Adjusted Reflectance and NDVI between Landsat-8 and Landsat-9 OLI over China Landscape

Remote Sensing, 2023
The successful launch of Landsat-9 marks a significant achievement in preserving the data legacy and ensuring the continuity of Landsat’s calibrated Earth observations.
Yuanheng Sun, Binyu Wang, Senlin Teng, Bingxin Liu, Zhaoxu Zhang, Ying Li   +5 more
doaj   +1 more source

Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

Remote Sensing, 2021
The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth’s surface.
Zhipeng Tang, Giuseppe Amatulli, Petri K. E. Pellikka, Janne Heiskanen   +3 more
doaj   +1 more source

Monitoring shifting cultivation in Laos with Landsat time series

Remote Sensing of Environment, 2023
Shifting cultivation is an agricultural practice in which plots of land are cultivated temporarily, then abandoned, and vegetation is allowed to regenerate during fallow periods. Shifting cultivation is usually associated with cutting and burning forests and thus it is an important driver of forest disturbance in the tropics.
Chen, Shijuan, Olofsson, Pontus, Saphangthong, Thatheva, Woodcock, Curtis E.   +3 more
openaire   +2 more sources

Detecting semi-arid forest decline using time series of Landsat data

European Journal of Remote Sensing, 2023
Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation.
Elham Shafeian, Fabian Ewald Fassnacht, Hooman Latifi   +2 more
doaj   +1 more source

Time-Series Model-Adjusted Percentile Features: Improved Percentile Features for Land-Cover Classification Based on Landsat Data

Remote Sensing, 2020
Percentile features derived from Landsat time-series data are widely adopted in land-cover classification. However, the temporal distribution of Landsat valid observations is highly uneven across different pixels due to the gaps resulting from clouds ...
Shuai Xie, Liangyun Liu, Jiangning Yang
doaj   +1 more source

Historical forest biomass dynamics modelled with Landsat spectral trajectories [PDF]

, 2014
Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España).
White, Joanne C., Alejandro, Pablo, Gomez, Cristina, Wulder, Michael A.   +3 more
core   +1 more source