This dataset is a collection of drone RGB and multispectral imagery from plots across Australia (AusPlots, SuperSites, Cal/Val sites to be established in the future). Standardised data collection and data processing protocols are used to collect drone imagery and to generate orthomosaics. The protocols developed in 2022 are based on the DJI Matrice 300 (M300) RTK drone platform. DJI Zenmuse P1 and MicaSense RedEdge-MX/Dual sensors are used with M300 to capture RGB and multispectral imagery simultaneously. The data is georeferenced using the DJI D-RTK2 base station and onboard GNSS RTK. In the processing workflow, the multispectral image positions (captured with navigation-grade accuracy) are interpolated using image timestamp and RGB image coordinates. Dense point clouds and the fine-resolution RGB smoothed surface were used to generate co-registered RGB (1 cm/pixel) and multispectral (5 cm/pixel) orthomosaics. Mission-specific metadata for each plot is provided in the imagery/metadata folder. The Drone Data Collection and RGB and Multispectral Imagery Processing protocols can be found at https://www.tern.org.au/field-survey-apps-and-protocols/ .

서해안 식생 복원사업 대상지의 염습지를 드론을 이용해 촬영한 Lidar영상

한국환경연구원(KEI)에서 제공하는 2021년 드론데이터 정사영상_원본에 대한 정보입니다. KEI에서 구축한 드론 데이터는 공간정보의 일종으로 국토교통부 국가공간정보 보안관리규정, 국가정보원 보안업무규정 등의 적용을 받습니다. 이에 따라 국가기관과 지방자지단체 및 공공기관에게만 요청에 의해 제공됩니다. 관련 사항은 아래 연락처나 이메일로 연락주시면 답변드리겠습니다. 환경영향평가, 정책 관련 연구 시 연계자료로 활용가능

서해의 갯벌 식생 복원사업 대상지의 염습지를 드론을 이용해 촬영한 분광 영상

이 데이터는 2022년 드론을 활용해 촬영한 고해상도 영상을 판독하여 구축한 농업 디지털전자지도(팜맵) 기반의 남해군 작물 재배 현황 속성 정보입니다. 드론 영상에서 경작지 경계와 작물 유형을 정밀 분석해 실제 재배 작물의 종류, 재배 면적, 위치 등을 구조화한 데이터로, 기존 통계 기반 정보보다 정확도가 높다. 농업 생산계획 수립, 스마트팜 운영, 농지 관리, 재해 대응 등 다양한 행정·정책 활용이 가능하며 지역 농업 구조를 파악하는 데 핵심 자료로 쓰일 수 있다. 또한 지리정보 기반 속성값으로 구성되어 다른 공간정보 시스템과 연계하기 쉽다는 장점이 있다.

갯벌 식생 복원사업 대상지인 신안군의 염습지를드론을이용해 촬영한 Lidar영상

한국환경연구원(KEI)에서 제공하는 2020-2021년 드론데이터 3D POINT CLOUD에 대한 정보입니다. KEI에서 구축한 드론 데이터는 공간정보의 일종으로 국토교통부 국가공간정보 보안관리규정, 국가정보원 보안업무규정 등의 적용을 받습니다. 이에 따라 국가기관과 지방자지단체 및 공공기관에게만 요청에 의해 제공됩니다. 관련 사항은 아래 연락처나 이메일로 연락주시면 답변드리겠습니다. 환경영향평가, 정책 관련 연구 시 연계자료로 활용가능

TERN worked together with Airborne Research Australia (ARA) to deliver airborne hyperspectral and lidar data for a number of selected homogenous 5 km x 5 km field sites across several locations in Australia (formally known as the AusCover Supersites). A Riegl Q560 Lidar, a SPECIM AisaEAGLE II hyperspectral scanner (VNIR) and a SPECIM AisaHAWK hyper-spectral scanner were mounted in underwing pods of ARA's ECO-Dimona research aircraft VH-EOS, each one together with its own navigation and altitude system. The spatial resolution of the Airborne hyperspectral data is 0.5m and Airborne LiDAR is 0.3m. Details on the data acquisition for each site is summarized here.

In support of U.S. Geological Survey (USGS) Southwest Biological Science Center researchers, and in coordination with the Bureau of Land Management (BLM) and National Ecological Observatory Network (NEON), the USGS National Uncrewed Systems Office (NUSO) conducted uncrewed aircraft systems (UAS) remote sensing flights over two BLM Assessment, Inventory, and Monitoring (AIM) plots at the NEON Moab site in Utah for multi-scale carbon sequestration research on public lands. The UAS data collected include natural color, multispectral, and hyperspectral imagery, and lidar to capture diverse information about vegetation and soils on drylands. The first site (“site 1”) features intact sagebrush and was mapped on May 3, 2023. The second site (“site 7”) is located on a grazed rangeland environment and was mapped on May 5, 2023. These UAS surveys were conducted in early May 2023 to coincide spatially and temporally with ground-based BLM AIM sampling and airplane-based remote sensing surveys by NEON. This portion of the data release presents discrete lidar point clouds from low-altitude UAS flights at two dryland sites approximately 40 km south of Moab, Utah. A YellowScan Vx20-100 scanner (laser wavelength 905 nm) was flown at an altitude of 31 meters above ground level on a DJI Matrice 600 Pro UAS with approved government edition firmware. The lidar point clouds were post-processed kinematic (PPK) corrected to a concurrently operating Trimble R8s GNSS base station and each point was assigned Red, Gren, Blue (RGB) image values using corresponding natural color orthomosaics at each site. The point clouds were also point classified using a bare-ground classification scheme (0-Created, never classified; 2-Ground) and exported in .las format.

In support of U.S. Geological Survey (USGS) Southwest Biological Science Center researchers, and in coordination with the Bureau of Land Management (BLM) and National Ecological Observatory Network (NEON), the USGS National Uncrewed Systems Office (NUSO) conducted uncrewed aircraft systems (UAS) remote sensing flights over two BLM Assessment, Inventory, and Monitoring (AIM) plots at the NEON Moab site in Utah for multi-scale carbon sequestration research on public lands. The UAS data collected include natural color, multispectral, and hyperspectral imagery, and lidar to capture diverse information about vegetation and soils on drylands. The first site (“site 1”) features intact sagebrush and was mapped on May 3, 2023. The second site (“site 7”) is located on a grazed rangeland environment and was mapped on May 5, 2023. These UAS surveys were conducted in early May 2023 to coincide spatially and temporally with ground-based BLM AIM sampling and airplane-based remote sensing surveys by NEON. This portion of the data release presents discrete lidar point clouds from low-altitude UAS flights at two dryland sites approximately 40 km south of Moab, Utah. A YellowScan Vx20-100 scanner (laser wavelength 905 nm) was flown at an altitude of 31 meters above ground level on a DJI Matrice 600 Pro UAS with approved government edition firmware. The lidar point clouds were post-processed kinematic (PPK) corrected to a concurrently operating Trimble R8s GNSS base station and each point was assigned Red, Gren, Blue (RGB) image values using corresponding natural color orthomosaics at each site. The point clouds were also point classified using a bare-ground classification scheme (0-Created, never classified; 2-Ground) and exported in .las format.