IEEE Geoscience and Remote Sensing Magazine - December 2020 - 15

D 3 = state services and, obviously, the membership proposed
price amounts are also significant. However, the variable
V = the annual average volume of requested images is not
significant here. By applying (5), the means of each significant sector are, respectively, €2,782 (research and education
organizations), €3,428 (state services), and €3,022 (global
sector). In this case, the median cannot be defined because
the decreasing probabilities curve starts with a probability of
0.32 (<0.5), corresponding to the least proposed membership price amount of €500. The global membership WTP
mean (€3,022) is clearly higher than that of the global WTP
per image (€1,696). This means that, in general, users tend to
be willing to pay more for a fixed yearly amount than when
being charged per image. State services represent the sector
that is willing to pay the most for a membership fee, with a
mean of €3,428. In fact, almost 12% of users agreed to pay a
€15,000 membership fee. This percentage of users increased
up to 20% for a fee of €8,000.
VOLUME ANALYSIS
Proceeding in the same way, we applied the binary logistic
model to fit the answers to the two proposed threshold volumes. The responses to the threshold-volume questions are
considered binary dependent variables. On the other hand,
the independent variables are Vproposed = the proposed threshold volume; V = the annual average volume of requested
mages; and the four dummy variables representing the D 1,
D 2, D 3 and D 4 sectors, with the research and education organizations as the reference level. The estimators' characteristics
are presented in Table 5.
As in the membership WTP model, the intercept coefficient is significant. Therefore, the reference sector (i.e., research and education organizations) should be considered.
In addition, sector D 2 = local authorities and the proposed
threshold volume are significant. We found an average volume threshold of eight images, above which subscribers are
ready to pay €750 for each additional image (Table 6).
In fact, a simple cross-comparison between the descriptive results of survey sections 4 and 5 and the results obtained
from the three main questions of sections 2 and 3 shows the
following: although 9.7% of users responded that the requested volume of images would not be affected in the case of a
price imposition, 10% were ready to pay €5,000 for an image,
and 11.8% would accept a membership fee of €15,000 for
an HR-image pooling system. However, 41% of users believed
that their organizations would not use any more images in
the case of a price imposition, 38% of users were not willing
to pay even €300 per image, and 67% refused to pay more
than €500 as a membership fee.
To have a global view, we combined the survey's results
with the GEOSUD database. A direct way to compute the
total economic benefits of the HR satellite images is by multiplying the total number of HR satellite images available on
the platform by the global mean value per image. This may
result in a total economic benefit of €12.7 million perceived
only by the direct users. Since the global demand WTP
DECEMBER 2020

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

curve represents the relationship between the price of the
-image and the quantity demanded, the area below this curve
can be presented as the net consumer surplus, the benefits
that users obtain for their use of HR satellite images. These
€12.7 million represent the economic gain of consumers at
zero cost per image. So far, the operating costs of GEOSUD
amount to €11 million. Second, when the results based
on the mean membership WTP (€3,022) are generalized,
GEOSUD, through the 500 entities registered on the platform, could account for some €1.5 million in the case of a
membership-fee imposition.
DISCUSSION
The approach chosen is based on the current users of a club
good [57] that can be accessed only by organizations, mostly
public entities, already registered on the GEOSUD platform.
The GEOSUD SDI, to which we had privileged access, has
been used as an example to illustrate the potential of these
images in a wide and growing range of uses. Although the
respondents were not totally in a position to make tradeoffs
between different agencies' budget priorities, the survey endeavored to frame their answers as if they could make these
decisions themselves. Hence, the study was not merely a
simple opinion-gathering vehicle but a more substantive

TABLE 3. THE MEANS AND MEDIANS IN EUROS OF THE
-DIFFERENT SIGNIFICANT SECTORS OF USERS.
SIGNIFICANT SECTORS

MEAN (EUROS)

MEDIAN (EUROS)

D 2 = local authorities

1,531

950

D 3 = state services

1,645

1,139

D 4 = nonscientific public institutions

2,126

1,860

Global

1,696

1,209

Means and medians represent WTP price per image.

TABLE 4. THE MEMBERSHIP-WTP ESTIMATORS' ANALYSIS.
COEFFICIENTS

ESTIMATE

STANDARD
ERROR

Z VALUE

P VALUE
(>|Z|)

b 0 = intercept

-7.987 #10-1

1.410 #10-1

-5.665

1.47 #10-8 ****

M = membership price ( b 1)

- 8.752 #10-5

2.106 #10-5

-4.156

3.24 #10-5 ****

D 3 ( b 5)

2.713 #10-1

1.643 #10-1

1.652

0.0986

TABLE 5. THE THRESHOLD-VOLUME ESTIMATORS' ANALYSIS.
COEFFICIENTS

ESTIMATE

STANDARD
ERROR

Z
VALUE

P VALUE
(>|Z|)

b 0 = intercept

0.31157

0.14780

2.108

0.03502**

Vproposed ( b 1)

0.05716

0.02687

2.128

0.03338**

D 2 ( b 4)

0.69706

0.23847

2.923

0.00347 ***

IEEE Geoscience and Remote Sensing Magazine - December 2020

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