GEO Joint Experiment for Crop Assessment and Monitoring (JECAM): Template for Research Progress Report

Transcription

1 GEO Joint Experiment for Crop Assessment and Monitoring (JECAM): Date: 2016/02/04 JECAM Test Site Name: Burkina Faso - Koumbia Team Leader and Members: Team Leader: Raffaele Gaetano Template for Research Progress Report Adresse : Maison de la Télédétection, 500 rue Jean-François Breton, Montpellier cedex 5, France raffaele.gaetano@cirad.fr Team Members and Affiliation: SANOU Patrice, ISESTEL Ouagadougou IMBERNON Jacques, Montpellier SOUMARE Mamy, IER, Bamako VALL Eric, CIRDES, Bobodioulasso JOLIVOT Audrey, CIRAD, Montpellier BEGUE Agnès, CIRAD, Montpellier Use of Information In addition to the report we would also like to use the information and images you provide to update the jecam.org website. Do you agree to this use of your information? Y Project Objectives Have the original objectives for your site changed? N Please briefly describe the areas that you are working on from the list below (i.e. topic, general methods, intended operational outcome, if any): Crop identification and Crop Area Estimation : Y Crop Condition/Stress : N Soil Moisture : N Yield Prediction and Forecasting : Y Crop Residue, Tillage and Crop Cover Mapping : N Others?

2 Site Description Location The city of Koumbia is located southwest of Burkina Faso in the province of Tuy, in the Hauts- Basins. Site Extend Centroid: lat: 11 10,596 / lon: Top left: lat: / lon: Bottom Right: lat: / lon: Topography Soils : Mostly sandy Drainage class/irrigation : No Crop calendar : June to November Field size : 3ha (Cotton and Maize/Sorghum) Climate and weather : Tropical dry Agricultural methods used Photograph(s) Earth Observation (EO) Data Received/Used Figure 1: Exploitable images for Crop calendar is highlighted. Figure 2: Exploitable images for Crop calendar is highlighted. Figure 3: Exploitable images for Crop calendar is highlighted.

3 Pléiades : o Space agency or Supplier : Airbus Defence and Space o Optical o Number of scenes : 6 o Range of dates : 2015/09/ /10/14 o Beam modes/ incidence angles/ spatial resolutions : pan-sharpened B/G/R/NIR bands at 15m spatial resolution o Processing level : L1 ortho Sample pictures of the Pléiades images Figure 1 : Pléiades mosaic for 2015

4 SPOT 6/7 : o Space agency or Supplier : Airbus Defence and Space o Optical o Number of scenes : 3 o Range of dates : 2015/09/ /09/24 o Beam modes/ incidence angles/ spatial resolutions : pan-sharpened B/G/R/NIR bands at 1.5m spatial resolution o Processing level : L1 ortho Sample pictures of the SPOT 6/7 images Figure 2: SPOT 6/7 mosaic for 2015

5 LANDSAT-8 : o Space agency or Supplier : U.S. Geological Survey o Optical o Number of scenes : 3 o Range of dates : 2015/04/ /11/25 o Beam modes/ incidence angles/ spatial resolutions : pan-sharpened (blue to SWIR2 bands) at 15m spatial resolution o Processing level : L1 ortho Sample pictures of the LANDSAT-8 images Figure 3: LANDSAT-8 June-October-November NDVI composite for 2015

6 SPOT5-TAKE5 : o Space agency or Supplier : Sentinel-2 for Agriculture o Optical o Number of scenes : 6 o Range of dates : 2015/04/ /09/12 o Beam modes/ incidence angles/ spatial resolutions : G/R/NIR/SWIR bands at 10m spatial resolution o Processing level : L3A (monthly cloud-free composite) Sample pictures of the SPOT5-TAKE5 images Figure 4: SPOT5-TAKE5 July-August-September NDVI composite for 2015

7 SENTINEL-1 : o Space agency or Supplier : European Space Agency o Radar o Number of scenes : 10 o Range of dates : 2015/05/ /11/20 o Beam modes/ incidence angles/ spatial resolutions : Dual (VH/VV) polarization at 10m resolution o Processing level : Level1 SLC + GRD Sample pictures of the SENTINEL-1 images Figure 5: SENTINEL-1 June-September-November composite (pre-processed VV intensities) for 2015

8 RADARSAT-2 : o Space agency or Supplier : Agriculture and Agri-Food Canada o Radar o Number of scenes : 9 (Ascending - FQ5) 10 (Descending FQ9) o Range of dates : 2015/05/ /12/04 o Beam modes/ incidence angles/ spatial resolutions : Quad-Pol at 11.5m resolution o Processing level : SLC o Acquired data only partly cover the study site Sample pictures of the RADARSAT-2 images Figure 6: RADARSAT-2 June-September-December composite (pre-processed VV intensities) for 2015

9 In situ Data Figure 7: Points (red) corresponding to plots selected for yield prediction and forecasting; parcels (yellow) are field surveys on October 2015 Field surveys on agricultural plots in October 2015 : ~900 GPS waypoints collected to identify crop types following the recommendations of the JECAM Guidelines for Field Data Collection_v1 0. Parcels for cropland/crop types identification (in yellow) have been manually digitized into polygons (surface data) using the VHSR Pleiades mosaic. Points for yield prediction (in red) will also concur to the validation of the classification products. With few exceptions, almost all points were identified transecting along roads and tracks at the end of the rainy season and using a GPS tablet with Pleiades/SPOT7 base maps.

10 Sesame Cotton and maize Groundnuts Herbaceous savannah Maize Sorghum Cow peas Young fallow Figure 8: Photos taken in 2015 during field surveys

11 Results Cropland/Crop Type identification: o Challenges: (1) Build a cropland/crop type identification map at the highest possible spatial resolution (0.5m) provided by the available EO data for 2014 agricultural season (data acquired in 2015 will be processed in 2016), for the different level of the JECAM nomenclature (see Fig. xx). (2) Develop a novel methodology for classification leveraging the data-fusion approach and limiting the use of site-specific prior information, in order to devise a processing chain which can work at a global scale. o Methods: Our data-fusion approach relies on the OBIA (Object Based Image Analysis) paradigm: an object layer is generated by segmenting the VHSR image, and a large set of radiometric/multi-temporal indices from HR imagery are projected at the object scale and joined to VHSR textural indices; a Random Forest (RF) classifier is used to carry out object-based classification, as well as to perform importance analysis over selected variables and to assess classification performances; several classification strategies have been tested to match the different levels of the JECAM nomenclature with an additional Level 0 for crop vs. non-crop identification (see Fig. 9), namely a traditional single level approach, and two (top-down and bottom-up) hierarchical approaches; a more robust validation is also performed using external validation segments not included in the training set, obtained by photointerpretation based on additional in-situ data.

12 Level 0 Level A Level B Level C Level D CLASSE Noncrop Crop Water Water bodies Water bodies Water bodies bodies Build-up Build-up Build-up surface Build-up surface surface surface Natural Rocs Rocs Rocs spaces Grassland Herbaceoussavann ah Herbaceoussavann ah Natural Natural forest Natural forest forest Shrub land Savannah withshrubs Savannah withshrubs Fallow Young fallow Young fallow Young fallow Old fallow Old fallow Old fallow Ligneouscro Other Cash Other Cash Other Cash p woodycrops woodycrops woodycrops Annualcropl Oilseed Groundnuts Groundnuts and Other Sesame Soja bean Soja bean Leguminous Cowpeas Cowpeas Cash crop Fibre crop Coton Cereals Maize Maïze Millet Millet Rice Rice Sorghum Sorghum Figure 9 : modified JECAM nomenclature including a Level 0 (crop vs. non crop) Variable importance analysis To limit the complexity of the overall methodology, we performed a first set of Random Forest classifications to assess the number of important variables to retain. The figure and table below show the overall accuracies as a function of the number of important variables used for levels 0, A and B. These experiments confirm that a number of variables around 20 (one tenth of the total) is enough to achieve a satisfying accuracy (above 95% of the maximum achievable accuracy).

13 Overall accuracy Level 0 Nb of important variables used % 94.5% 94.5% 93.9% Level A 91.1% 92.6% 92.6% 92.4% Level B 70.2% 74.7% 76.5% 75.2% Figure 10 : Overall accuracies obtained varying the number of important variables used (over 242) Classification assessment using internal Random Forest validation A first assessment of classification accuracies has been carried out using the internal Random Forest validation strategy (mean of the accuracies on randomly chosen validation samples over different trees). Encouraging results have been obtained, especially for the Levels 0 and A, as reported in Fig. 10. Scores for the most detailed levels C and D are very promising, but further inspection is necessary to confirm these outcomes.

14 Figure 11 : Overall accuracies for single level classifications using internal RF validation Classification assessment using external validation segments A further set of manually segmented areas (mainly obtained by photo-interpretation) has also been used as an additional test set to assess classifications. Accuracies obtained using this test set are less interesting, especially starting from level B, as resumed in Table 1. However, the reliability of the external validation set has to be further inspected. We could also test the different classification strategies, and verify that the hierarchical approach starting from the level-0 map gives the best accuracies at finer scales. Level 0 Level A Level B Level-specific 87.4 % 87.4 % 50.5 % Hierarchical % 54.1 % By grouping % 37.1 % Table 1 : Overall accuracies for different classification strategies using external validation data In the next Figures, some samples of the cropland/crop-type maps generated for 2014 agricultural season are shown.

15 Figure 12 : Level 0 map (crop vs. non crop) for the Koumbia village Figure 13 : Details of classifications at levels 0, A and B

16 Yield Prediction and Forecasting : o Challenges: (1) Describe and evaluate the main crop systems of the site: crop varieties, crop rotation, use of inputs, tillage, fallow, use of plough or tractors and (2) quantify the yield variability obtained by farmers and evaluate the link with the climate variability. o Methods: Six villages have been selected according to their spatial distribution, their accessibility, the studies already carried out, and the remote sensing image footprints. In agreement with the farmers and peasant organizations, thirty plots have been chosen in each village, to carry out two types of survey: A survey with the farmer, concerning the plot monitored: Preceding crops for the three last years, crop management techniques, area cultivated, production obtained, crop residues Concerning the crop monitoring on the plot for the season 2014 : A tend days crop monitoring The weighing of grains and biomass for three quadrats by plot. The daily measurement of rainfall, with three rain gauges put in each village (a total of eighteen rain gauges) Dimikuy Boni Founzan Koumbia Gombeledougou Figure 8 : Site descriptive, villages and area with monitored plots

17 o Some recall of the results obtained in the previous growing season (2014): Survey concerning the plot monitored Figure 3: Cotton yields for each village, from 2011 to 2013 Preceding crops and crop management techniques at the village scale Figure 4 : Rotation in with maize in 2009

18 Survey concerning the crop monitoring on the plot for the season 2014 Figure 5: Rainfall monitoring for the three rain gauges in the Boni village Figure 6: Sowing related to record rainfalls Data analysis for 2015 growing season is still undergoing. The number of monitored plots has been raised to 160, 85 cultivated with maize, 33 with cotton and 42 with sorghum.

19 Collaboration The workflow for crop type identification carried out for the Koumbia site has been conceived and applied in collaboration with the Brazil (Sao Paulo) and Madagascar (Antsirabe) study teams. We could confirm the good generality of this approach with respect to the geographical and agricultural specificities of the different sites. Conclusions To what extent have the project objectives been met? An appropriate general workflow based on the fusion of heterogeneous data has been successfully carried out for the identification of crop types. Current classification scores, although to be further validated, stand unprecedented for the Burkina Faso site and confirm that the proposed approach is promising. However, further development has to be carried out in order to : select more appropriate variables at different scales; refine methodology w.r.t. object-layer generation and object-based classification strategies; collect and process data from other sensors (radar) and/or sources, identify a more reliable external validation strategy. Can this approach be called best practice? We followed the recommendations of JECAM guides for the acquisition of field data. However, we have adapted the nomenclature to the actual crop types for our site. Have you modified the project objectives? If so, in what way? Yes. We added the study of yield Prediction and Forecasting Plans for Next Growing Season For the next growing season, we will basically maintain the current approach, which will however be further investigated. Do you anticipate ordering the same type/quantity of EO data next year? No

20 If not, what type and quantity of EO data do you plan to acquire? EO data: o We maintain programming Pleiades images on the area. The vesting period will be the same. o We plan to renew the order for SPOT 6-7 data (one image in the dry season and one in the end of rainy season). o We hope that this site will be selected for the acquisition of images from the VENUS mission (spatial resolution : 5m / temporal repetitiveness 2 days) o We will rely again on Landsat-8 images, and add Sentinel-2 multi-spectral images to the dataset in 2016 (including L3A products from the Sentinel-2 for Agriculture platform). o We expect to obtain further improvement through the use of SAR data for the generation of 2015 crop type maps. In this case, we will renew the acquisition of radar data. We also hope to renew field surveys: this will depend on the security conditions for the organization of the missions. Publications No publication yet, two papers are currently being prepared for submissions in the next months.