[HTML][HTML] Object-oriented lulc classification in google earth engine combining snic, glcm, and machine learning algorithms

A Tassi, M Vizzari - Remote Sensing, 2020 - mdpi.com
Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and
object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be …

Crop map** using supervised machine learning and deep learning: A systematic literature review

M Alami Machichi, E mansouri, Y Imani… - … Journal of Remote …, 2023 - Taylor & Francis
The ever-increasing global population presents a looming threat to food production. To meet
growing food demands while minimizing negative impacts on water and soil, agricultural …

Map** China's planted forests using high resolution imagery and massive amounts of crowdsourced samples

K Cheng, Y Su, H Guan, S Tao, Y Ren, T Hu… - ISPRS Journal of …, 2023 - Elsevier
Tree planting has been suggested as a potentially effective solution for mitigating climate
change. China has implemented the world's largest afforestation and reforestation project …

Strategies to ensure fuel security in Brazil considering a forecast of ethanol production

F de Oliveira Gonçalves, R Firmani Perna… - Biomass, 2023 - mdpi.com
Highlights Sugarcane would not be enough to meet the ethanol targets set for Brazil Corn
ethanol may be an attractive secondary feedstock to help supply the demand In Brazil …

Statistical features for land use and land cover classification in Google Earth Engine

WR Becker, TB Ló, JA Johann, E Mercante - Remote Sensing Applications …, 2021 - Elsevier
The possibility of identifying and quantifying agricultural areas objectively and quickly is a
relevant aspect in the Brazilian agricultural context, given the territorial extent of the country …

High-resolution rice map** based on SNIC segmentation and multi-source remote sensing images

L Yang, L Wang, GA Abubakar, J Huang - Remote Sensing, 2021 - mdpi.com
High-resolution crop map** is of great significance in agricultural monitoring, precision
agriculture, and providing critical information for crop yield or disaster monitoring …

A Scale Sequence Object-based Convolutional Neural Network (SS-OCNN) for crop classification from fine spatial resolution remotely sensed imagery

H Li, C Zhang, Y Zhang, S Zhang, X Ding… - … Journal of Digital …, 2021 - Taylor & Francis
The highly dynamic nature of agro-ecosystems in space and time usually leads to high intra-
class variance and low inter-class separability in the fine spatial resolution (FSR) remotely …

Greenhouse area detection in Guanzhong Plain, Shaanxi, China: spatio-temporal change and suitability classification

C Gao, Q Wu, M Dyck, J Lv, H He - International Journal of Digital …, 2022 - Taylor & Francis
The extensive use of greenhouses has brought soared economic benefits for farming
practitioners in China and an overview of the spatio-temporal distribution of greenhouses is …

Using remote sensing, process-based crop models, and machine learning to evaluate crop rotations across 20 million hectares in Western Australia

R Lawes, G Mata, J Richetti, A Fletcher… - Agronomy for …, 2022 - Springer
Remote sensing has been widely employed to identify crop types and monitor crop yields on
farms. Here, we combine successive seasons of these products to identify crop rotations in …

[HTML][HTML] Troubled waters at the frontier: Map** forest-dependent people's access to surface water in the Dry Chaco

PS Matthews, M Baumann, C Levers, T Kuemmerle… - Applied …, 2024 - Elsevier
Tropical dry woodlands are experiencing major rates of change across the planet, with over
71 million hectares of woodlands lost since 2000, much of it driven by the expansion of …