Embed Size (px)
Citation preview
Very High Resolution satellite images for land management in the Macaronesia
Manuel Arbelo
Grupo de Observación de la Tierra y la Atmósfera (GOTA)
Universidad de La Laguna, Spain
What is GOTA? Who are we?
SATELMAC Project
◦ Partners
◦ Motivation
◦ Objectives
◦ Crop mapping from very high spatial resolution satellite images
◦ Classifying of forest fuel types by using VHR satellite imagery
◦ Book: “Satélites para deteção remota aplicada à Gestão Territorial”
Current and future projects
◦ PALMERA Project
◦ SAT-RURAL Project
2
Grupo de Observación de la Tierra y la Atmósfera
3
Registered as official research group at ULL, 2000.
Composed of members from two departments of Faculty of Physics with common interests:
◦ Dpto. Física Fundamental y Experimental, Electrónica y Sistemas
◦ Dpto. de Física Básica
ULL excellence group
Multidisciplinary; physicists, biologists, computer scientists, agricultural engineers and forestry engineers.
Faculty of Physics Univ. La Laguna
4
Main research lines:
◦ Remote sensing: applications and algorithms
◦ Atmospheric modeling
Equipment:
◦ VIS-NIR spectroradiometer
◦ Thermal radiometers
◦ Calibration sources
◦ Atmospheric LiDAR
◦ Cluster computers
◦ Meteorological stations
◦ HRPT reception station (NOAA data from 1993)
Software and images
◦ ENVI
◦ eCognition
◦ ASTER, MODIS, NOAA, Landsat, Geoeye, WorldView-2
◦ …
Grupo de Observación de la Tierra y la Atmósfera
5
The SATELMAC Project is framed within the axis 1 of the Transnational Cooperation Programme MAC (Madeira-Canarias-Azores) 2007-2013. The partners are:
6
Land managers should be supported by accurate and updated maps of land use and land cover.
Remote sensing satellite images have been traditionally used for this purpose. However, just a decade ago, satellites captured only images with spatial resolutions so large that they were not useful, in most cases, in the Macaronesian archipelagos.
The last commercial satellite generation
(WorldlView, GeoEye, Quickbird, etc.),
with a level of detail similar to that obtained
by aerial photography, make this technology
more suitable for our islands.
Motivation
7
Objectives
Obtaining maps of land use and productive potential through satellite images of high resolution. (Crop mapping)
The characterization of forest resources using remote sensing data for the fire prevention and modeling. (Forest fuels mapping)
The development of a methodology for monitoring agricultural land use by other applications. (detection of Illegal buildings)
8
Crop maps are an essential tool for land
management in Canary Islands.
Due to the small size of the islands and their high population density, rural and urban areas usually appear mixed. Thus, to develop land management plans, an accurate and updated crop map is very important.
Until now, crop maps were obtained by means of intense field work, where crops were identified plot by plot. This methodology produces very accurate maps, but the costs are very high and cannot be updated as frequently as it should be.
This study analyzes the potential of using VHR satellite images for updating crops maps in Canary Islands.
9
Crop mapping from very high spatial resolution satellite images
TENERIFE Island
An example: Study area of 100 km2
The main crops are potatoes, vineyard, arable and fallow lands. There is also a small proportion of fruit trees and other minor crops, but these have not been considered in this study because of their low importance.
Potatoes Vineyard
Fallow land Arable land
10
Crop mapping from very high spatial resolution satellite images
A multispectral GeoEye-1 image was acquired on the 3rd of October 2010, when the most important crop (potatoes) reached its greatest development.
GeoEye-1 has 4 bands (450-520nm, 520-600 nm, 625-695 nm and 760-900 nm) and 2 m/pixel.
Field work: we collect ground truth data to train the classification algorithms and to validate the results 3 days after the image. 170 plots with the following information: type of crop, growth level, date and a photo id.
11
Crop mapping from very high spatial resolution satellite images
Producers Users
accuracy accuracy
Potatoes 94,7% 97,3%
Fallow land 93,8% 40,5%
Arable land 82,2% 94,9%
Vineyard 50,00% 93,80%
Overall accuracy 79,80%
Classification and validation
12
Crop mapping from very high spatial resolution satellite images
The classified raster image was adapted to the cadastral vector map. The majority class of the classification layer was assigned to each cadastral parcel (polygon).
Adaptation of the classified image into the
cadastral vector layer
Masked image
Classified image
Overlay of the cadastral
layer
Majority class is asigned
13
Classifying of forest fuel types by using VHR satellite imagery
Forest fuel types are the physical characteristics of the live and dead biomasses that contribute to the spread, intensity and severity of fire.
Detailed information about the conditions, quantity and spatial distribution of forest fuel are important variables in forest management and predicting fire behavior.
Remotely sensed data provides an important way to derive the spatial distribution of fuel types and its variation over the time, considerably reducing the cost associated to fieldwork
This study evaluate the potential of Object-Based- Image Analysis for mapping fuel types from WorldView-2 (WV2) imagery in a test area (La Orotava) in the northwest of Tenerife Island (Canary Islands, Spain).
14
Classifying of forest fuel types by using VHR satellite imagery
M5
Shrub cover < 30%
Tree cover > 50%
M1/M2
Grass cover > 50% or average fuel height 0.3 - 0.6 m
M3/M4
Average fuel height 0.6 - 2.0 m or 2.0 - 4.0 m
M6/M7
Shrub cover > 30%, Tree cover > 50% Canary Pine Forest
M6/M7
Laurisilva Forest
An WV2 image from 2011 and field data were used as a ground-truth dataset, firstly to identify the fuel types, and secondly, to evaluate performance and results.
15
Classifying of forest fuel types by using VHR satellite imagery
First step
Field work ≈ 70 circular plots
Third step
Nearest Neighbor
classification with
samples from a
shape file
Second step
Segmentation ≈ 25,839 objects
were identified
The fuel type classification methodology
16
Classifying of forest fuel types by using VHR satellite imagery
The performance of the classification was compared in terms of errors, rather than accuracies. The error was divided into two components: Quantity disagreement and Allocation disagreement. Fuel type M1/M2 showed an agreement of 100%, and fuel type M6/M7 had the highest allocation disagreement an 15% and a quantity disagreement of 8%.
Fuel types map
Now, we are working on fusion between LiDAR and WorldView data, improving the results a 10%.
Classifying of forest fuel types by using VHR satellite imagery
Canopy Height Model (CHM)
18
Main conclusion
VHR satellites imagery represent lower costs and higher updating frequency than traditional methodologies
vs.
19
“Satélites para deteção remota aplicada à Gestão Territorial”
http://www.satelmac.com In portuguese and spanish. This document provides access to information related to the most important operational satellites at present, and those that have potential application in Macaronesian land management.
Current and future projects
20
PALMERA, a cooperation project between the Canary Islands (Spain) and the Souss Massa Drâa region (Morocco), is intended to map the date palm groves and detect possible diseases using very high spatial resolution satellite imagery.
Current and future projects
21
SAT-RURAL:
“SPATIAL INFORMATION TECHNOLOGIES AS A TOOL OF MANAGEMENT AND DIAGNOSIS OF RURAL AREAS”
Presented past Friday 30 November to the last convocatory of PCT-MAC 2007-2013.
Partners:
GMR Canarias
GOTA-ULL
Direcção Geral de Agricultura, Silvicultura e Pecuária (Cabo Verde)
Departamento de Ciência e Tecnología - Universidade de Cabo Verde
